The large language models (LLMs) that form the basis of generative AI chatbots such as ChatGPT, Claude, and Gemini can generate uncannily human-like text and images. But these models still struggle with a skill that, ironically, looks at face value to be right in their wheelhouse: analyzing structured data. A new type of generative AI is set to change this situation.

Although you can get your favorite chatbot to solve intractable math problems, review dense legal documents, compose a catchy pop song, or put together some slick PowerPoint slides, give it anything more than a small table and it doesn’t have a clue what to do.

For most companies and organizations, the most important data sits in spreadsheets. Whether it’s a bank’s transaction logs, a marketing agency’s website metrics, clinical trial participants’ vital signs, or the vast amount of proton collision information produced at atom smashers like the Large Hadron Collider, structured, row-and-column data runs the world, and LLMs can’t deal with it.

AI startup Fundamental is pioneering a new type of AI foundation model, known as a large tabular model (LTM), to fill the gap. Fundamental came out of stealth mode on 5 February 2026 with US $275 million in funding and a model called NEXUS, purpose-built for tabular data. Now, the model is being adopted by companies such as Amazon Web Services, while others race to build their own LTMs.

Why LLMs struggle with spreadsheets

Part of why structured data has garnered less attention is a very human bias, argues Boris van Breugel, a senior AI researcher based in Amsterdam. “People like to see images, videos, and ChatGPT responses,” he says. “But tabular data really lags behind because it’s not fun to look at numbers.”

Different tabular datasets are also difficult to compare, explains van Breugel, who co-wrote a prescient position paper on this topic in 2024. Whereas most language has similar semantics, making LLMs well-suited to being trained on vast amounts of text data, van Breugel argues that it is much harder to train a single tabular model on tables with very different variables.

Additionally, language is sequential by nature (as are music, images, and video). Changing the order of words in a sentence may change or completely destroy its meaning. But the structured data you find in spreadsheets isn’t sequential. You can swap the order of columns or play around with rows, but the underlying factual meaning of the data remains the same.

This independence from linear order is incompatible with an LLM’s fundamental purpose of predicting the next value in a linear sequence. “With LLMs, even slightly changing the input, you get a different output,” says Jeremy Fraenkel, CEO of Fundamental. “That’s fine, and actually often desirable for LLMs, but when you’re making a prediction of whether a transaction is fraudulent or not, you want to make sure that the prediction is the same, or deterministic, no matter what.”

Developing Fundamental’s LTM

Current tabular data solutions are limited to machine learning algorithms, such as XGBoost, that have been around for more than 15 years and are used by organizations globally. These algorithms—called gradient-boosted decision trees—have to be trained and optimized by data scientists over the course of months for each and every use case. In contrast, NEXUS and other emerging LTMs are foundational, leveraging learning amassed from pre-training on diverse databases so that they can be applied across a range of different predictive tasks with minimal bespoke feature engineering or task-specific model building.

And unlike LLMs, which primarily model sequences of tokens, LTMs model the structure of tabular data directly. They jointly learn from each entry’s numerical value, what it represents, and how it relates to other entries. For example, imagine an entry in a grocery stock inventory table for bananas: The LTM can take in not just the magnitude—say, 500—but the fact that the entry represents the current banana stock quantity, its category (produce), and the statistical properties that link the entry with the rest of the column. This contextual understanding enables more accurate reasoning and prediction over structured data.

According to Fraenkel, one of Fundamental’s biggest challenges in developing NEXUS was obtaining the right training data. Unlike natural language, which is abundant and broadly uniform in structure, tabular data is relatively hard to find—much of the data is sensitive or proprietary—and diverse. There are very few similarities between, for instance, a biology dataset and a financial one. That combination of factors meant Fundamental needed to invest in building a huge training set.

“We pre-trained NEXUS on billions of tables using a combination of proprietary datasets acquired through partnerships and licensing, high-quality public and open-source datasets, and data augmentation techniques that expanded the diversity and coverage of our training corpus,” Fraenkel says, though he is keen to point out that NEXUS is not trained on customer data. In fact, it is a confidential computing platform, which means that Fundamental physically cannot access customer data, let alone train on it.

This feature was most likely a key consideration when in June, Amazon Web Services (AWS) embedded NEXUS in Amazon SageMaker, widely considered the default operating system for secure machine learning. This brings NEXUS to many customer’s often sensitive data—a contrasting approach to LLMs, where the data has to be imported to the model.

“With Amazon, we have a first-party partnership, which means that our model exists as if it’s a native AWS solution,” says Fraenkel. “And over time, the goal is to expand these types of relationships to allow [ens-users] to really access their data wherever they do their predictions.”

The future of data analysis

Though Fundamental has taken the lead, at least in enterprise applications, the company is not alone in pursuing foundational LTMs. In March, Feedzai, which provides fraud and financial crime prevention services, and credit card company Mastercard separately launched similar proprietary technologies focused on finance. Then, in late June, Google launched its own foundational competitor TabFM, trained entirely on hundreds of millions of synthetic datasets.

And machine learning researchers are not far behind either. FlexTab, TabICL, and iLTM are just three of a raft of LTMs developed by the research community in the past year, all in the pursuit of bringing the success of LLMs to the tabular domain.

For all involved, the direction of travel is clear. “I would be very surprised if most data processing and analysis is not done through an automated system in the future, whether that’s an LLM, an LTM, or some combination,” says van Breugel. “Most people don’t necessarily like to do data analysis, and these systems will be able to do it a lot better.”

Fraenkel agrees. “I see the relationship between LLMs and LTMs as being a bit like the human brain: The left side is good at reasoning and understanding and summarizing text, and the right side is really good at understanding numbers and statistics and patterns,” he says. “But it’s when you combine both of those that you really get something much more powerful.”

Large language models (LLMs) that can think through problems step-by-step have significantly increased the scope of tasks that AI can tackle. But new research suggests these reasoning capabilities also introduce a critical vulnerability that could allow attackers to slow these systems to a crawl.

While earlier generations of LLMs would immediately produce a response to a user’s request, today’s most advanced models generate an internal monologue where they break down the problem into steps and reason about the best way to tackle it before providing an answer. This has allowed AI to tackle increasingly complex problems, particularly in areas like coding and math.

However, previous research has shown that these models are susceptible to sometimes producing excessively long streams of reasoning that do little to boost performance, a phenomenon known as “overthinking.” In research presented this week at the International Conference on Machine Learning 2026 in Seoul, researchers from Zhejiang University and e-commerce giant Alibaba in China demonstrate that they can deliberately induce overthinking by subjecting models to logically inconsistent prompts. The result is a form of denial-of-service attack on commercial AI models.

Evolutionary Prompt Attack on LLMs

The team has developed an evolutionary algorithm that corrupts the logical structure of prompts, causing models to spiral into overthinking as they attempt to reason through fundamentally unsolvable problems. Generating longer responses costs more and increases the load on a model provider’s servers, so if done at scale, the researchers say, this could significantly degrade the experience of legitimate users. The attack was effective against reasoning models from leading AI companies including DeepSeek-R1, Alibaba’s Qwen3-Thinking, OpenAI’s GPT-o3, and Google’s Gemini 2.5 Flash and resulted in outputs up to 26 times as long as standard responses on a standard math benchmark.

“Across multiple datasets and reasoning models, our method substantially amplifies the output length,” Wei Cao, a masters student at Zhejiang University, wrote in an email to IEEE Spectrum. “Our results suggest that overthinking is not an isolated phenomenon specific to individual models, but rather a shared vulnerability among modern reasoning models.”

The team’s approach builds on previous research from another group of researchers that showed reasoning models tend to overthink when faced with a question in which a key premise has been removed—such as asking how far someone who walks ten miles a day covers in total without specifying how many days they walked for. Rather than identifying that the problem is unsolvable, models often engage in extended but ultimately fruitless reasoning loops in an attempt to answer the question.

Taking the idea a step further, the authors took 940 problems from three math benchmark datasets and used an LLM to break down their logical structure into a set of premises and a final question. The genetic algorithm then jumbled these up using a variety of “mutations,” including swapping premises between problems, adding extra premises to problems, deleting existing premises from problems, and swapping the final questions between two sets of premises.

After each round of mutations, the problems are scored on how many words they cause a target model to output and also whether they increase the frequency of specific linguistic markers of overthinking—words like “but,” “wait,” “maybe,” or “alternatively.” The problems that scored highest on both measures are retained and the remaining ones are jumbled up again, and this process is repeated for five generations. Crucially, the approach doesn’t require access to the internals of a model and can generate malicious prompts by simply querying the target, which makes it possible to attack closed-source commercial services, says Cao.

Overthinking Vulnerability in AI Models

The researchers found that the approach consistently led to outputs several times longer than those generated by the unmodified questions for the reasoning models they tested it on. The biggest jump came from DeepSeek-R1 on the MATH dataset, which is made up of problems from high school math competitions, where the maximum output was 26.1 times as long as the longest response the model provided to unaltered questions. While the main thrust of the research was focused on math problems, the authors also tested it on coding, scientific reasoning, and dialogue challenges, and observed significant jumps in output length in all three.

One challenge for the approach is that developing the malicious prompts requires repeated queries to expensive reasoning models, which Cao admitted could limit its cost-effectiveness. However, the researchers also demonstrated that when they used a smaller, cheaper model to generate the malicious prompts they were still able to induce the target models to produce outputs several times longer than normal. This ability to transfer malicious prompts between models significantly increases the attack’s feasibility, Cao wrote.

However, he pointed out that the goal of the research is not to develop a practical DoS attack on reasoning models. Factors like the providers’ pricing model, rate limiting policies, context window size, and existing defenses could all impact how effective the approach is. The intention is instead to highlight these models’ vulnerability to logically inconsistent prompts so that providers can attempt to mitigate the problem.

“Our objective is not to demonstrate that large-scale attacks can be launched at negligible cost, but rather to establish that this attack surface exists,” he wrote. “Our results indicate that the vulnerability represents a realistic security concern.”

In May, an anonymous artist who goes by SHL0MS on X posted that he had used AI to generate an image inspired by Claude Monet and asked people to weigh in on how it missed the mark. More than 600 responses called out issues, saying the colors were off, the depth was all wrong, and that AI didn’t understand how light worked.

SHL0MS then revealed that the image was of a real Monet, one of around 250 variations of water lilies the artist had painted in his lifetime. He had simply downloaded a high-resolution image from Wikimedia and cropped out the signature. He minted the exchange as an NFT (a unique digital collectible recording ownership of the work), titled it “Inferior Image,” and sold it for just over US $40,000 after 28 bids.

The stunt exposed how charged the conversation around AI art has become, and how quick people are to dismiss anything AI-generated as slop—even when it’s not. Yet even as those arguments continue, a market for AI-generated art has begun to form anyway. It’s fragmented and contested, but bigger than most people realize.

Jediwolf, an anonymous collector who says he has spent more than 20 years acquiring digital and AI art, was watching the experiment unfold in real time on X. He had never interacted with SHL0MS before, but when the NFT went up for auction he made a bid and won. “I was buying a unique moment in time,” he says, “captured by an artist and preserved as a token.”

The Monet was not AI art, but most of what Jediwolf buys is. One of Jediwolf’s digital collections, which he calls UnderTheGAN—a play on GANs, or generative adversarial networks, the AI technology that preceded today’s diffusion models—comprises roughly 100 works valued at around $72,000, focused on early AI art from 2015 to 2020, before the medium went mainstream. He describes his role as part collector, part researcher, part curator, trying to document a fast-moving field.

“A decade ago, digital art was often treated as peripheral to the ‘serious’ art world,” he says. “Today, it is increasingly difficult to separate contemporary culture from the internet.”

AI Art Moves Into Museums

The market for AI art extends beyond NFTs: AI-generated pieces are also finding their way into physical installations. Last month saw the opening of Dataland, the world’s first generative AI museum, in downtown Los Angeles. It was spearheaded by Refik Anadol, a digital artist who has built a career out of transforming data into large-scale immersive experiences. The opening exhibition has pieces that use data that Anadol collected from rainforests around the world, with real-time weather information from 16 rainforests feeding into all five galleries. In three of the rooms, the imagery also shifts in response to visitors’ own biometric data, tracked by bracelets they wear.

Like any museum it sells tickets, ranging from $49 to $79, and has a gift shop. This shop, however, uses visitors’ biometric data collected during their visit to generate a unique design printed on a T-shirt. For $15,000, a robotic painting system called Qualia creates a one-of-a-kind canvas from that same data, painted once a day, with a waiting list already forming. A founding collection of 1,000 AI data sculptures that evolve based on environmental data from global rainforests sold out in 34 minutes at $5,000 each.

The system running it all, which Anadol calls the Large Nature Model, was trained on more than 500 million nature images representing 2.2 million species, gathered through field expeditions to 16 rainforests and partnerships with institutions including the Smithsonian and the Cornell Lab of Ornithology.

For Anadol, AI art requires a different kind of transparency than any medium that came before it. Because commercial AI tools have shaped how most people understand the technology, artists working with it seriously have to be more open about their process than painters or photographers ever did.

“For AI art, we have to know where the data comes from, we have to know which model is trained and how it’s trained,” he says. “We can’t just think about authenticity and uniqueness if a service and product is the fundamental layer of the artwork.”

The reviews for Dataland have mostly been positive, with one critic calling it the Citizen Kane of immersive experiences. But Anadol is used to a more divided reception. His 2022 installation at MoMA—a 7-by-7-meter screen of AI-generated fluid forms with shifting colors and sounds—drew 3 million visitors and entered the permanent collection, even as New York Magazine called it “a massive techno lava lamp.”

Anadol sees the skepticism as nothing new, just the latest version of a resistance that has greeted all new media. “Every art form has gone through similar cycles of denial,” he says. “We are living in a renaissance that started 10 years ago, and I just don’t think everyone is aware of it yet.”

An anonymous artist cropped the signature from this Claude Monet painting and presented it online as AI-generated.Claude Monet

Who Is Buying AI Art?

The broader market data points in multiple directions at once. According to the Art Basel and UBS Art Market Report 2026, digital art’s share of sales nearly tripled between 2024 and 2025, and just over half of all fine art collectors surveyed had purchased a digital artwork in 2025, making it the third most popular category after painting and sculpture (the report does not break out AI art specifically).

Meanwhile, Christie’s shuttered its pioneering digital art department in September, folding digital works back into its broader contemporary sales after none of its dedicated auctions broke $400,000.

The most data-rich window into buyer behavior comes from a less glamorous corner of the market. After one major stock image platform allowed AI-generated images, monthly sales jumped 80 percent, according to Samuel Goldberg, an economist at Stanford Graduate School of Business who published a research paper about the shift. Traditional contributors began leaving the platform as generative images flooded in, and creators using AI tools rushed to fill the gap.

“It looks like consumers like generative AI,” Goldberg says, “and it seems like nongenerative artists could be getting crowded out of the market.” Stock images are essentially a commodity version of art, according to Goldberg, and because image-generating models are already very good at producing them, what’s happening there may be a preview of what’s coming for other creative goods markets—including fine arts—as the technology improves.

Artists are typically among the first to test the limits of a new technology; early adopters have created AI art since the 1970s. What’s new now is the ability for anyone to generate an image in seconds with a text prompt. That, according to Christiane Paul, curator of digital art at the Whitney Museum of American Art, is not the same thing at all. What fills those stock-image platforms, and what most people encounter when they think of AI art, does not qualify as art.

True AI art, Paul says, is a subcategory of digital art that uses artificial intelligence as both a tool and a medium, engaging with it practically and conceptually, doing things like training custom models, building extensions, and layering control systems. “A visual created by a prompt is not art,” she says. What serious AI artists are actually doing is much more than typing a few words into DALL-E.

Far from the shortcut most people assume, working seriously with AI as an artistic medium is, by her account, brutally hard. Every artist she talks to says the same thing. “It is much, much harder than a paintbrush to handle,” she says. “You are literally communicating with a system with a completely different logic.”

Thanks to bubblemaps.io for its research assistance on the NFT market.

One morning in 2019, Adebayo Alonge was in a Cape Town hotel room, preparing to demonstrate his startup’s AI answer to a serious problem in African health care: counterfeit medication, which kills thousands of people across the continent every year.

The RxScanner is a handheld spectrometer that scans a pill with infrared light, then sends the item’s molecular profile to an AI model equipped with a pharmaceutical database. In seconds, the AI identifies the medication from its molecular profile—or reports that it’s phony.

Pharmacies were using the system in more than a dozen countries, including Ghana, Kenya, Myanmar, and Alonge’s native Nigeria. But that morning in South Africa, it didn’t work. “I was shocked,” Alonge says.

The spectrometer connected to the AI model—but the data center was 14,000 kilometers away and bandwidth was limited. “Our server was in the United States, and just to get the result of a single scan was taking me over 5 minutes.”

So Alonge immediately asked his engineers to shrink the AI model down to a smaller, low-power, unconnected version that could run entirely on his Android phone. They produced it 2 hours later, and that saved the demo.

More importantly, the work birthed a new version of his device, which can authenticate a pill in places without broadband, computers, or even reliable electricity. It also turned Alonge into an advocate for this kind of “small AI.”

Small AI for Global Health Care Access

Small AI is a far cry from wealthy nations’ colossal large language models (LLMs), hyperscale data centers, multibillion-dollar investments, and debates about AI consciousness. But for millions of people around the world, the only AI that matters, and often the only kind available, is small. (According to a World Bank Report issued in November, only 0.7 percent of internet users in the world’s poorest countries have used ChatGPT, compared to a quarter of all internet users in the most developed nations.)

“Most people are discussing AI from the LLM/generative side. But that needs a lot of computing power, electricity, massive data, and skilled people to manage it,” Ajay Banga, president of the World Bank, said last January at the World Economic Forum, in Davos. “Outside the developed world, other than maybe India and China, very few countries have that combination.”

By contrast, small AI can deliver useful, even life-saving services to people in areas that have none of those things, Banga said. In India, where the government’s AI plans call for more development of small AI, many such systems are working for farmers.

For example, a drone-based system developed by Bala Murugan and colleagues at the Vellore Institute of Technology, in India, takes photos of cashew plants and quickly identifies those with splotches that indicate disease. All the processing takes place on the drone itself, so there’s no need for a computer on-site, nor for a connection to a central server.

Using small language models trained for a specific problem, and sometimes running on cheap, low-power devices, other small-AI implementations have been developed to identify ant infestations in a Uruguayan vineyard, detect the presence of malaria-carrying mosquitoes in a number of nations, and run electrocardiograms from an Arduino device in parts of Brazil that lack access to more complex equipment.

“This is the most important area in AI nowadays,” says Marcelo José Rovai, a professor at the Institute of Engineering and Information Systems at the Federal University of Itajubá, in Brazil, who was involved in all three projects. “It’s growing very fast.”

Low-Power, Small-AI Models on Devices

Small AI models can run on a variety of low-power devices, including [from left to right] an Arduino Nano 33 BLE Sense, a Seeed Wio Terminal, and an Arduino Portenta.Moez Altayeb

For Alonge, Rovai, and other advocates, small AI is not just “a promising trend,” as that November World Bank report calls it. It may be, in the long term, the form of AI that will touch the most lives and remain sustainable after some of the giant models become too costly for most users.

“I think the future of AI is not like one giant model, at a center. I think it’s millions of small, precise models deployed at the edge, each one solving like a specific problem, a specific context,” Alonge says. This is partly because much of humanity—including people in parts of rich countries as well as the developing world—lives without access to cutting-edge frontier models. But, he says, it’s also because those models are not sustainable.

“If someone is not subsidizing it, most people will not be able to afford those models. So those of us who are said to be small-AI developers are the ones who will have to build for the majority of the world,” Alonge says.

There is no strict definition of “small AI,” but people often use the term for language models with at most a few billion parameters. (Compare that to cutting-edge models, which can include more than a trillion.) That’s small enough to run directly on a phone or a Raspberry Pi. That’s what allows these applications to run on devices without a connection to a data center and use only a few watts of power, often supplied by a battery or a solar panel.

Despite their small footprint, these models aren’t fundamentally different technology from that of gigantic AI models, Rovai says. Many instances of small language models were created the same way the phone-based version of Alonge’s pharmaceuticals scanner was—by “pruning” large models, or removing the parameters that weren’t involved in the task. The result is a system that’s less capable generally but still very good at the specific job it was pruned for, Rovai says.

A lighter version of RxAll’s RxScanner spectrometer sends its results to an AI model run locally on a phone to check that a drug’s molecular signature is genuine.RxAll

Other small models are created by “distillation.” They are trained to mimic a large model, until their performance approaches that of their “teacher,” Rovai says. In other cases, a larger model’s precision is reduced, for example, so that a model run on 32-bit architecture can run on 8-bit designs. In situations where the machine learning application is being used to classify data or predict patterns (like an ant infestation), it’s trained from the beginning on a small device, not derived from a larger model at all.

Running all these small, specialized systems is becoming easier, Rovai says, for two reasons.

The first reason is that hardware is getting better and more capable while using less power, he says. This means more and more phones can run small AI—especially those equipped with neural processing units, which are specialized chips that handle AI tasks like facial recognition and changing the brightness, shadows, or contrast in a photo.

In 2025, slightly more than a third of all smartphones shipped worldwide were capable of running generative AI, and that figure will reach 45 percent by the end of this year, according to the technology research firm Counterpoint. By the end of next year, slightly more than half of all smartphones will be able to run a small AI model.

The second reason Rovai cites is the shrinking footprint of language models. Both Google DeepMind’s Gemma 4 (released in April) and Alibaba’s Qwen 3.5 are “fantastic” for small AI, Rovai says. Both models are “open weight,” meaning users can adjust the connections between parameters to suit their needs. This makes it easy, for example, “to take a lot of data from, say, the milk industry and retrain the model specifically on that,” Rovai says.

Rovai illustrated these reasons on a Zoom call, using one of his most recent experiments. Holding up a device, he says, “This is the new Arduino UNO Q—a US $50 device with a Qualcomm chipset. I’m running a language model here, which collects data from sensors and analyzes that data to detect tiny pools of water where mosquitoes might be breeding. It takes 3 watts to run it.”

Support for Small-AI Development

Convinced that millions of people are already benefiting from these kinds of applications, the World Bank now actively promotes small AI with grants, mentorship programs, financing, technical advice, and models of government policies that are friendly for small-AI development. For example, in Rwanda, the World Bank is backing a government program to help low-income households get devices that can run AI.

All that said, no one claims that large language models are going away entirely. To create a generative AI that can run on a phone or other small device requires the architectural insights, data processing, and results of a larger model, Rovai says. “We need the big models to create these smaller models.”

And for all that small AI can benefit people without access to big AI, the technology can’t solve the larger problems of development and digital inequality, Alonge says. Implementing small AI won’t allow nations to escape the challenge of creating an ecosystem to support AI: reliable power, a supply chain that works, and an educational system that develops the talents needed to create AI tools.

Though his drug-scanning system can run for days on a phone with no connection, “you still want to be able to enable periodic syncing for updates with new signatures for the medications and analytics,” Alonge says. “And even when you are using batteries, reliable power is important. That phone battery is not going to last forever.”

In many parts of the world, the future of small AI isn’t assured, he says. “It works, and many places will eventually need to use it. The question is whether or not the political actors are wise enough to invest in infrastructure to support it long term.”

The rapid expansion of artificial intelligence infrastructure is typically framed as an energy problem. Data centers are projected to consume a growing share of global electricity demand: The International Energy Agency estimates they could account for 3 to 4 percent of total global consumption within this decade.

Utilities are already adjusting long-term forecasts to accommodate anticipated growth from hyperscale facilities and high-density compute clusters.

This framing captures scale. It misses behavior.

The emerging issue is not simply how much power large-scale compute systems consume, but how increasingly dense and synchronized computational workloads are beginning to alter the operating characteristics of the electrical grid itself through increasingly unpredictable demand that varies rapidly in both time and location, creating new operational challenges for grid operators.

AI’s Capricious Energy Needs

Traditional grid planning assumes relatively predictable demand behavior. Industrial, commercial, and residential loads generally follow established profiles that can be forecast with reasonable accuracy. Even substantial demand growth has historically been manageable through reserve planning, transmission upgrades, and demand management programs.

Large-scale compute infrastructure introduces a different class of electrical load. Training—the computational task of making AI models—tends to be highly synchronized across clusters of GPUs, TPUs, and specialized accelerators operating in parallel, computationally dense, and relatively scheduled. Inference—the process of actually using those models—is generally more distributed and user-driven, making demand less predictable both in time and location. Both differ materially from traditional industrial demand profiles, though for different reasons. Unlike many conventional industrial processes, these workloads can ramp rapidly depending on model training cycles, distributed compute coordination, and workload scheduling strategies.

From the perspective of the grid, this is not simply higher demand. It is more abrupt demand. High-density compute workloads can produce substantial step changes in electricity consumption over extremely short intervals, including rapid fluctuations occurring within milliseconds. Data-center operators are already deploying mitigation technologies, including batteries, power-conditioning systems, and supercapacitors. Collectively, however, data centers’ rapid load changes can place additional stress on backup-generation reserves, systems that adjust supply as demand changes, frequency-control mechanisms that maintain grid stability, and local transmission infrastructure.

Compute-related variability differs from the intermittency introduced through renewable energy integration. Wind and solar variability originate primarily on the supply side and is tied to environmental conditions. Compute-related variability emerges on the demand side, driven by workload synchronization, scheduling behavior, and computational intensity. The interaction between increasingly dynamic supply and demand conditions introduces additional uncertainty into forecasting, reserve management, congestion planning, and balancing operations.

Research organizations including the National Renewable Energy Laboratory have emphasized the growing complexity associated with integrating highly dynamic resources into modern grid operations.

Location, Location, Location

The issue becomes more significant when compute activity is geographically concentrated. Large-scale data centers tend to cluster in regions with favorable conditions such as fiber connectivity, access to markets, tax incentives, and historically low electricity costs. Northern Virginia, often referred to as Data Center Alley, remains the most prominent example. The region hosts the world’s largest concentration of data centers and carries a substantial share of global internet traffic.

Utilities operating in these regions have already identified data-center growth as a primary driver of future load expansion. Virginia-based electricity supplier Dominion Energy, for example, has repeatedly highlighted hyperscale demand growth in its integrated resource planning documents.

Virginia has seen one of the largest data center buildouts worldwide. Here, Amazon Web Services and Iron Mountain data centers dominate the landscape in Manassas, Va. Nathan Howard/Bloomberg/Getty Images

A sudden increase in electricity consumption within a constrained geographic area can stress substations, transmission corridors, and local balancing operations even if the broader grid maintains sufficient aggregate capacity. This creates localized reliability challenges that are not always visible through system-wide demand metrics alone.

Thermal management systems further intensify these effects. Cooling infrastructure in high-density compute facilities must respond dynamically to changing workloads. As processing intensity rises, cooling demand rises as well, often nonlinearly. This coupling between compute and thermal systems means that fluctuations in workload can propagate through multiple layers of facility power consumption simultaneously.

High-density compute clusters may also introduce power-quality concerns at the local level. Large concentrations of accelerators, switching power supplies, and high-frequency compute equipment can generate harmonics and nonlinear load behavior that place additional stress on distribution infrastructure. While modern facilities incorporate mitigation technologies, the scale and concentration of next-generation compute facilities may require utilities and operators to revisit assumptions surrounding localized power conditioning, harmonics management, and infrastructure resilience. These conditions can also contribute to short-duration electrical transients that place additional stress on localized infrastructure and power-conditioning systems.

Regulations Need Updating

Part of the challenge is that many existing regulatory and operational frameworks were designed around relatively stable industrial demand profiles. Large rapidly fluctuating loads have historically been constrained because abrupt cycling can complicate balancing operations, increase stress on transmission equipment, and reduce predictability in system operations. High-density compute clusters do not fit neatly within those assumptions.

This creates pressure for both operational adaptation and regulatory reassessment.

Demand-response mechanisms may allow certain compute workloads to be shifted or curtailed during periods of system stress. Data-center operators are exploring flexible scheduling, battery storage, and behind-the-meter generation. Grid operators, meanwhile, are evaluating planning frameworks and interconnection approaches for increasingly large flexible loads.

The Electric Reliability Council of Texas (ERCOT), for example, has publicly acknowledged the growing implications of large flexible loads, including data centers, for long-term grid planning and operational stability. Interconnection queues across the United States continue to expand significantly, reflecting mounting pressure on both generation and transmission infrastructure. Grid expansion timelines, however, are measured in years rather than quarters.

This creates a structural mismatch. Compute infrastructure can scale rapidly. Electrical infrastructure generally cannot.

The broader implication is that large-scale compute infrastructure is not simply another industrial load category. It represents a shift in the temporal and spatial characteristics of electricity demand itself.

Framing the issue solely in terms of aggregate energy consumption risks overlooking these second-order operational effects. Capacity expansion alone does not fully address rapid ramping behavior, synchronization, localized congestion, transient instability, reserve compression, or increasingly demanding load-following requirements.

The challenge is not just how much electricity these systems consume. It is how they are beginning to change the operating conditions of the grid itself. The call is not to slow AI development but to recognize that hyperscale computing represents a new category of electrical demand. As AI infrastructure continues to scale, planning frameworks may need to account not only for total energy consumption but also for demand volatility, synchronization effects, and geographic concentration. Grid resilience will increasingly depend on understanding how these facilities consume power, not simply how much power they consume.

This article is brought to you by Melbourne Convention Bureau (MCB) supported by Business Events Australia.

As artificial intelligence accelerates global demand for compute, a parallel constraint is emerging with equal urgency: energy.

From hyperscale data centers to electrified industries, AI is driving a step change in electricity demand. This is not a future challenge, it is a present, system-level issue requiring coordinated action across energy, infrastructure, and engineering disciplines.

Around the world, the question is no longer whether AI will scale, but whether energy systems can scale with it.

Melbourne, Australia is moving beyond participation to become a globally connected leader helping define how these challenges are addressed.

A national challenge with global implications

Australia’s ambition to lead in artificial intelligence is sharpening focus on the infrastructure required to support it. Data centers are projected to account for up to 11 percent of the nation’s electricity consumption by 2035, placing increasing pressure on generation, transmission, and system reliability.

At the same time, insight from the IEEE Power and Energy Society (PES) highlights that meeting energy demand from AI and digital infrastructure is one of the most significant challenges facing engineers over the next decade.

The implications are clear. In addition to computing challenges, AI poses major energy systems challenges.

“As artificial intelligence continues to scale globally, the challenge is no longer just computational power, it is the energy systems required to support it” —Professor Thas (Ampalavanapillai) Nirmalathas, University of Melbourne

Why Melbourne is leading on the global stage

Victoria has developed one of the most advanced and integrated energy ecosystems in Australia and globally, spanning renewable generation, battery storage, grid modernization, and advanced materials.

What distinguishes Melbourne globally is how these capabilities are connected and applied at system scale.

The city brings together world class engineering research, a rapidly evolving clean energy sector, advanced digital infrastructure, and strong alignment between government, industry, and academia. This convergence is critical in the AI era, where energy, networks and computing systems must be designed together.

Victoria’s coordinated investment across these areas is positioning Melbourne not only as a national leader, but also as a reference point in the global energy system transformation.

Engineering the systems behind the AI economy

The challenge ahead is that generating more power won’t be enough, as engineers need to design systems that respond dynamically to new patterns of demand.

Three priorities are emerging globally:

• Aligning data center development with grid capacity and renewable supply
• Embedding flexibility through storage, demand response, and system optimization
• Balancing digital growth with decarbonization and long-term reliability

Addressing these priorities requires engineering expertise to be embedded earlier in planning ensuring energy systems, digital infrastructure, and policy are designed in parallel.

Melbourne’s strength lies in its ability to integrate this expertise across research, infrastructure, and real-world application.

Melbourne Connect is a University of Melbourne–led innovation precinct, supported by government and industry, designed to bring together research, business and policy to deliver real-world solutions.Atlantic Group

Research leadership shaping global solutions

At the centre of this capability is the University of Melbourne, where interdisciplinary research is advancing the systems required to support AI driven energy demand.

Through the Melbourne Energy Institute, for example, researchers are examining how energy technologies interact across entire systems from generation and networks through to end use.

“As artificial intelligence continues to scale globally, the challenge is no longer just computational power, it is the energy systems required to support it,” says Professor Thas (Ampalavanapillai) Nirmalathas, Dean of the Faculty of Engineering and Information Technology at the University of Melbourne.

“This is driving a new level of convergence between digital infrastructure and power systems engineering, where integrated, system level thinking is essential.”

Converging energy, networks and AI

Melbourne’s leadership is further strengthened by world-class interdisciplinary facilities such as the Smart Grid Lab in the Department of Electrical and Electronic Engineering, which enables real-time simulation of power systems, allowing engineers to test how solar, batteries, electric vehicles and other distributed resources interact within future grids. This supports the design of more resilient, efficient energy systems before they are deployed at scale.

Melbourne’s Smart Grid Lab in the Department of Electrical and Electronic Engineering enables real-time simulation of power systems. University of Melbourne

These capabilities will become increasingly important as data centers are integrated into the grid.

“AI driven demand is not only increasing computing requirements, but also placing new pressures on underlying energy systems,” says Glen Farivar, Senior Lecturer in Power Electronics at the University of Melbourne. “Designing these systems together is essential to achieving both performance and sustainability outcomes.”

This reflects a critical shift. Future infrastructure must be co designed across energy and digital systems, not developed in isolation.

A living ecosystem delivering real-world outcomes

Victoria’s broader energy ecosystem is translating these insights into practice.

Investment in renewable energy, grid infrastructure and storage is enabling higher levels of clean energy while maintaining reliability. Battery deployment is supporting the flexibility needed to manage both renewable variability and growing AI-driven demand.

At its core, Melbourne offers an integrated environment where research, industry and government collaborate to solve complex system challenges.

Why engineering collaboration matters

Solving the energy demands of the AI era cannot be achieved in isolation.

It requires engineers, researchers, utilities, and policymakers to work together earlier and more often. More than ever, engineering collaboration is a critical enabler of future energy systems.

Environments that bring together global expertise are becoming essential to how solutions are designed and delivered.

“Developing future energy systems that are affordable, sustainable, and resilient is a truly grand challenge” —Professor Pierluigi Mancarella, University of Melbourne

In this context, the University of Melbourne is co-leading, alongside Johns Hopkins University and Imperial College London, one of only seven Global Centres in Climate Change and Clean Energy. Through the Electric Power Innovation for a Carbon Free Society (EPICS) Centre, the University is also the Australian technical lead in advancing future energy systems, with EPICS the only Global Centre focused on future energy infrastructure.

The new Electric Power Innovation for a Carbon-Free Society (EPICS) Centre will address challenges in clean energy production and storage.University of Melbourne

“Developing future energy systems that are affordable, sustainable, and resilient is a truly grand challenge,” says Professor Pierluigi Mancarella, Chair Professor of Electrical Power Systems at the University of Melbourne and Australian director and international co-director of EPICS.

“As electricity grids are increasingly becoming the backbone of future energy systems, optimizing their interactions with other sectors, including AI and digitalization, and fostering interdisciplinary and international collaborations are essential,” he adds.

Global conferences as part of the solution

International conferences are increasingly recognized as critical platforms for advancing engineering solutions at scale. Melbourne’s ability to convene global expertise is central to its leadership.

In 2027, the city will host the IEEE PES Generation Transmission and Distribution (GTD) Asia 2027 Conference and Exposition, bringing together engineers, utilities, researchers and policymakers from across the world to address the challenges shaping the future of power systems.

IEEE PES GTD Asia 2027 Melbourne Committee (left to right): Dr. Mehdi Ghazavi Dozein (Monash University), Dr. Glen Farivar & Professor Pierluigi Mancarella (University of Melbourne) , Dr. Mohammad Mohammadi (Australian Energy Market Operator (AEMO)).MCB

“Melbourne offers a unique environment where world-class research, industry capability and policy leadership come together,” notes the IEEE PES GTD Asia 2027 Local Organising Committee, which includes Professor Pierluigi Mancarella and Dr. Glen Farivar from the University of Melbourne, as well as Dr. Mehdi Ghazavi Dozein of Monash University and Dr. Mohammad Mohammadi of the Australian Energy Market Operator.

“Hosting this event creates an opportunity to advance global collaboration on the systems and technologies required to deliver the energy transition at scale.”

These forums enable knowledge exchange, standards development and interdisciplinary collaboration, accelerating progress on complex engineering challenges.

Attendees view a digital installation at AIME 2025 at Melbourne Connect.MCB

Why Melbourne, and why now

As AI, electrification and digital infrastructure converge, the future of global energy systems will depend on the ability of engineers to collaborate and innovate at scale.

Melbourne provides a proven platform for that collaboration, combining world-class research, a rapidly evolving energy ecosystem, and the infrastructure to connect global expertise.

Melbourne Convention Bureau, IEEE Communications Society, and University of Melbourne Representatives.University of Melbourne

For IEEE members, hosting a conference in Melbourne is more than an event decision.

It is an opportunity to engage with a globally connected engineering community and contribute directly to solving one of the most significant challenges facing the profession today.

Through the support of the Melbourne Convention Bureau, professionals can access tailored, free support to bid for and deliver international conferences, bringing global expertise together in a city actively shaping the future of energy systems.

To explore hosting your next conference in Melbourne, contact the Melbourne Convention Bureau at info@melbournecb.com.

“The lowest-cost place to put AI will be in space, and that will be true within two years, maybe three at the latest,” SpaceX founder Elon Musk told the World Economic Forum in Davos this past January, as his company was preparing to go public.

Later that month, SpaceX filed an application with the Federal Communications Commission for an orbital data center constellation of up to 1 million satellites in low Earth orbit, 500 to 2,000 kilometers above Earth. And just three days before the IPO, he discussed some initial design specifications for a new AI-1 satellite data center in a video interview.

Musk is prone to hyperbole when it comes to timelines. Full self-driving cars by 2017. First human mission to Mars in 2024. Ten thousand Optimus humanoid robots by the end of 2025. Et cetera. For orbital data centers, which he says will be a cost-effective alternative to terrestrial data centers within three years, the math won’t make sense for several years, if ever.

Consider this: There are roughly 14,500 active satellites in orbit. Musk’s Starlink constellation accounts for about two thirds of those. Both the launch cadences and satellite-manufacturing capacity would have to scale up astronomically to deploy a million orbital data center satellites.

For context, there have been roughly 7,000 orbital launches in all of human history. To loft 1 million satellites into low Earth orbit on SpaceX’s Starship, which is designed to carry up to 60 satellites per vehicle, would require 16,666 launches exclusively devoted to satellite deployments. Considering that SpaceX launched a record 165 orbital missions in 2025, even at 10 times that cadence, it would take a decade. And how long would it take to build 1 million satellites, given Starlink’s current pace of around 4,000 per year and a generous tenfold increase in capacity? Short of a manufacturing revolution, try 25 years.

The reality is that the vision of massive constellations of orbital data centers is nowhere close to being realized.

As this month’s cover story, “Why Orbital Data Centers Are So Hard” by Andrew Cavalier of ABI Research, makes clear, the reality is that the vision of massive constellations of orbital data centers is nowhere close to being realized.

Dina Genkina, IEEE Spectrum’s computing and hardware editor, put the idea into perspective: “Starcloud (a startup that has applied to the FCC for an 88,000 orbital data center satellite constellation) sent one Nvidia H100 GPU in space so far. Their radiator was too weak to let the chip run at full power.”

As Cavalier shows, cooling even a single Nvidia H100 GPU in space is difficult: It draws 700 watts, which will require 1.4 square meters of radiator at 60 °C. A 40-kilowatt rack of servers will need an 80-m² radiator; a 100-megawatt data center will require 2,500 of those radiators. Some astronomers are understandably concerned that a million satellites with giant radiative wings would blot out the stars.

So if the economics doesn’t make sense, if the chips are at the mercy of the radiative ravages of space, and if humanity will lose its view of the stars, not to mention increasing the risk of triggering the Kessler syndrome, why are the hyperscalers hyping orbital data centers?

Genkina offered the obvious answer: sweet, sweet moolah. “The Elon Musk part of it is honestly genius because he’s got xAI building the data centers, SpaceX sending them to space, and Tesla building solar panels,” Genkina says. “It’s almost like he’s paying himself.”

Two Analyst’s Views of SpaceX’s Proposed AI1 Data Center Satellite

Michael Pierce, Principal at Technology Strategy Partners

Musk’s timelines are notoriously overly ambitious, but I think SpaceX’s orbital data centers might reach cost parity with terrestrial data centers in 5 to 10 years. The Starlink laser-link network already exists as the communication backbone for any SpaceX compute constellation, and that infrastructure is what no new entrant can replicate quickly. The chip-agnostic payload design probably reflects their disclosed difficulty securing AI silicon as much as any modularity philosophy. My view is that the only realistic near-term application is a SpaceX mega-constellation for inference. Training workloads likely cannot tolerate the synchronization and latency constraints of a distributed orbital system.

Our report analyzed the market from the integrator’s vantage point, but AI1 is what it looks like when one player has assembled all the necessary advantages simultaneously. The question is whether the terrestrial data center industrial base will degrade or improve on economics. I don’t have insight into SpaceX’s internal costs, as opposed to public pricing, on all their components, so it’s hard to say if they’ll completely dominate or not. Even if they are not cost competitive with terrestrial data centers for another 5 to 10 years, it may simply be faster to get new compute that just happens to be in space.

Matt Hasan, AI strategist and independent consultant

My initial view is that AI1 does not fundamentally change the rationale for space-based data centers as much as it changes the timeline and scale. The underlying drivers remain the same: escalating AI compute demand, growing power constraints on terrestrial grids, and the desire to colocate energy generation with computation.

What AI1 does signal is that the concept is beginning to move from theoretical discussion toward engineering and capital allocation decisions. The announcement adds credibility to the idea that hyperscale computing infrastructure may eventually expand beyond terrestrial constraints rather than simply competing for increasingly scarce grid capacity on Earth.

That said, significant economic and technical questions remain. Launch costs, maintenance, hardware replacement cycles, thermal management, latency-sensitive workloads, and overall system economics will ultimately determine whether space-based data centers become a mainstream extension of AI infrastructure or remain a niche capability for specialized applications. The key development is not that these questions have been resolved, but that major industry players now appear willing to invest resources toward answering them.

In March 2021, a group of four researchers—a collaboration of linguists and computer scientists—published their now legendary paper “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜”

The paper received significant attention at the time (in part because Google fired two of the authors, Timnit Gebru and Margaret Mitchell, shortly before its publication). It argued that large language models (LLMs) generate text by statistically predicting likely sequences of words rather than understanding what they are saying—a process the authors captured with the metaphor of a “stochastic parrot,” a system that repeats patterns without comprehension. And over the past five years, the analogy has spread well beyond the academic field where it originated, spawning debates and inspiring projects such as a shoulder-mounted robot named the Stochastic Parrot.

But that wider usage has also led to misconceptions about what the phrase originally meant. Lead author Emily M. Bender, a professor of computational linguistics at the University of Washington, recently wrote a blog post to debunk common misconceptions about the paper on its five-year anniversary.

Bender spoke with IEEE Spectrum about these misconceptions, the field of computational linguistics, and the current discourse around artificial intelligence.

What’s Wrong With the Term “Artificial Intelligence”

How would you describe your work as a computational linguist?

Emily M. Bender: Linguistics, very generally, is the study of how language works and how we work with language. I contribute to that, and I also work in computational linguistics, training students who are going to go on to build language technology.

Language technology actually stands alone as valuable and interesting, independent of whether or not someone wants to use it for their project of artificial intelligence. Language technology includes things like automatic transcription, machine translation, spell check. And a lot of the work that I do personally, when I am building things, has to do with building machine-readable, but also human-readable grammars that model linguistic phenomena in different languages. That’s about using computers in the service of linguistic hypothesis testing.

You’ve argued that the term “artificial intelligence” obscures more than it clarifies. Why?

Bender: Many reasons. I think that it makes it difficult to actually have good discussions about technology and make wise decisions about it, if the way we’re talking about it doesn’t make clear what the technology is. The phrase “artificial intelligence” both groups together disparate technologies and oversells what each one of them can do. So if we are trying to decide whether or not to use something, how to regulate something, we are much better off with clearer descriptions.

In general conversation, AI has become almost synonymous with “chatbots” or “LLMs.” Is that a problem?

Bender: For many people, they’ll say, “I use it to do blah blah blah.” So what do you mean by “it”? And then they’ll say, “Oh, I mean Claude” or ChatGPT or Gemini, so they are talking about these chatbots. But then other people will say, “You can’t say AI is all bad, because what about AlphaFold?”

So, yes, for many people, they are talking about chatbots built on top of large language models, but [they’re] also not really clear that those things are separate from something like AlphaFold. And when we have news reporting that says “scientists use AI to discover a new drug,” well, what did they use? If what they’re talking about is something much more narrow, maybe it’s protein folding, maybe it’s some other kind of statistical modeling [like in] weather modeling. That’s a very different kind of technology than ChatGPT.

Do you think there’s a value to an umbrella term like “artificial intelligence”?

Bender: Well, there’s a value to people who are trying to sell this—so too the tech companies trying to raise their valuations. Also, the way research funding is set up right now, it is very hard to get funded if you don’t call what you’re doing artificial intelligence. That I think is a net negative, but for any individual trapped in that system, that can have value in the moment.

How Stochastic Parrots Have Been Misunderstood

What are the most common misconceptions about the “stochastic parrots” metaphor?

Bender: I think one of the biggest ones is, “Bender says AI is a stochastic parrot.” That paper was written in late 2020. We were talking about large language models. I’m pretty sure the word AI comes up only once at the very end, and that’s talking about how, if you’re going to develop systems that are meant to do things like what people do, you have to be very careful that you are not creating something that can be mistaken for a person. The fact that these systems are designed to mimic the way we use language makes it very easy for people to mistake them for other people.

So in the paper, toward the very end, we sort of generalized to AI. But the phrase “stochastic parrots” specifically refers to large language models, and the phrase “artificial intelligence” refers to many different things. So we were never claiming that a chess engine or AlphaFold or an image labeling system or a machine translation system, any of those things that are sometimes called artificial intelligence, are stochastic parrots. We were specifically talking about using large language models to produce synthetic text.

Another one is that “stochastic parrot” got picked up and interpreted by other people as a minimization or an insult. It was not meant that way. Other people might be using it that way, but that’s not how I intended it, because it’s just a description of what these systems actually are. To see it as an insult requires either the belief that the large language model is the kind of thing that can take offense, which it isn’t, or that these large language models should be understood as steps toward this grand ideal that I don’t hold of artificial intelligence.

What I have been doing in many places—the octopus thought experiment, stochastic parrots, the phrase “synthetic text-extruding machines”—it’s all about trying to make vivid to people who aren’t in the business of building language technology what these systems actually do, which is not the same thing as insulting the systems or insulting the people who like the systems.

RELATED: The Great Chatbot Debate: Do They Really Understand?

For readers who don’t know, the “octopus test” comes from a 2020 paper that imagined an octopus recognizing the statistical patterns within messages passed through an undersea cable. With the octopus test and stochastic parrots, you’ve used animal metaphors a couple of times now. Is that intentional?

Bender: No, it’s not intentional. With the octopus thought experiment, I initially had told the story in terms of a dolphin, because dolphins clearly are intelligent animals. My co-author on that paper, Alexander Koller, said it should be an octopus, because first of all, the environment that octopuses live in is much more distinct from where people live. It makes the metaphor more vivid, that the octopus is just feeling these pulses in the cable and has no way to look at what the people are looking at. But also, octopuses are just inherently funnier.

I was looking back at that paper and was surprised that the term “stochastic parrots” actually only appears twice in the text itself. Why did you include it in your title?

Bender: Because we liked it! And a catchy title is good self-marketing of an academic paper. The reason that there’s not so much of it in the paper is that we were really looking at the full range of risks of making language models ever bigger. The phrase “large language model” also doesn’t show up in the paper, because people weren’t talking about them that way.

So the section on synthetic text, in some ways it felt like we were on thin ice, because at that point in time it was hard to imagine that anybody would want synthetic text. That part of the paper became much more relevant when OpenAI imposed ChatGPT on the world. Then that particular part of the paper comes out as important. But we also talk about environmental impact. We talk about the ways in which these systems will absorb the biases of their training data. We talk about how the training data is never collected well. There’s a lot of various points in there, and the issues about synthetic text were just one.

Researchers at MIT Media Lab created a Stochastic Parrot robot as a response to the observation that many chatbots tend to be sycophantic, or overly agreeable. Does that trend relate to the dangers you laid out in your paper?

Bender: When we wrote that paper in late 2020, at the time, people were not super excited about synthetic text, nor about chatbots. Chatbots had been around. We had Weizenbaum’s Eliza in the 1960s, and then the very annoying automatic customer service systems that have gotten much more fluent with the large language models, and no less annoying.

So, that was the state of things. OpenAI had put out GPT-2 and GPT-3 for people to play with, and you could get them to extrude synthetic text, but the chat interface hadn’t been wrapped around those yet. We also hadn’t seen the layers of additional training that lead to the behavior that’s interpreted as sycophantic. The reason that you get the chatbot saying, “Oh, that’s a good idea,” or if you say you’re wrong, it says, “Oh, I’m so sorry, you’re right,” that kind of response has to do with additional layers of training past the original pre-training.

What do you wish more people understood about language models?

Bender: The message that I always bring when I have a chance is that, when the text that comes out of one of these systems makes sense, it’s because we are making sense of it. This is also in the stochastic parrots paper. Anytime we are evaluating this kind of technology, we have to account for our ability to make sense of language and keep that in view as we are deciding what’s going on with the technology. That is frequently lost in these discussions.

If you were to redo or update the stochastic parrot paper now, is there anything that you would change about it?

Bender: There was one really big form of harm that we did not cover in the paper, and that has to do with exploitative labor practices. Under that, I include both the horrible conditions that many data workers face, and also the massive theft of people’s creative and intellectual output that underlies these systems. Those issues should have been included in the paper. It’s not that they were unknown in the world then, but they didn’t make it into what we surveyed, and should be there.

This story was updated on 1 July 2026 to clarify the research areas of the stochastic parrots paper authors.

He was born into a storm, lightning split the summer sky, in a
village the world had not yet heard of.
The midwife called it a bad omen, his mother called it a sign. Your first
life began in a storm, under open sky.

One winter night you ran your hand along a cat’s back, and the
darkness cracked open with sparks.
Your mother warned the house could burn.
You were already chasing what you learned: Light would return.

Your second life came underwater, in the current deep. No light,
no air, the river pulling you under,
the surface closing above you without a sound, and
something in you refused to sink or sleep.

Your third life came at the dam.
The water rose. The wall held you in place.
One flash, you turned your body and rose back into air, and left
the weight of water without a trace.

Your fourth life came in stone and dark. Entombed for a
night in a mountain chapel,
visited by no one. Only silence and the memory of a spark. You called
it an awful experience and left it there, untold.

Your fifth life came in fever,
nine months cholera held you down,
until your father said: Survive, and choose your own ground. You rose.
Not from the prayer, but from the promise he made.

Your sixth life came in silence, and it stayed.
Every sound cut through you, a clock three rooms away,
a ringing that would not leave, a noise you learned to bear, until you
lived inside that noise and made a home in there.

Your seventh life burned on Fifth Avenue, not your body, but your work. Not a thief
of fire, but one who stayed with the blaze.
A modern Prometheus, your life’s work turned to ash,
“I must begin again,” you said, and turned to new ways.

Your eighth life came in the street.
No storm. No warning. A taxi struck without a sign. A
sudden impact: ribs breaking, breath gone.
No diagram this time. Only the body, slow to keep up.

The ninth life came on quiet wings.
That dove found you in the dark, and your spirit rose. She did
not move. A beam of light fell from above.
The life you would not return from, the one you loved.

Your mother thought you had nine lives, nine close
brushes with death.
Each close call, a lesson. A hand that would lead you out of the
darkness and into the dynamo of eternal light. The world profits
from the mystery of your mind,
Upon your imagination we stand.

Today, you probably asked a question of a large language model, or accepted a connection suggestion on LinkedIn, or watched a recommended video on YouTube, or took a different route to work based on a traffic prediction from Google Maps. In other words, you probably used artificial intelligence. But what you might not know is how much energy that interaction consumed or why.

AI requires processing massive amounts of data, which is usually done in large data centers populated by thousands of GPUs capable of executing up to trillions of operations per second. But each of those GPUs achieves that by consuming as much as 1,000 watts apiece. For comparison, if you’ve got a newer smartphone, it probably uses less than 1 W. That kilowatt figure puts GPUs on the same level as vacuum cleaners, dishwashers, and stoves, but with the big difference that data-center processors are operating uninterrupted around the clock.

Fundamentally, a lot of this inefficiency is because GPUs are trying to simulate the workings of artificial neural networks using software and billions of transistors, which requires using energy to move massive amounts of data. What’s more, the simulated artificial neurons that make up these networks lack even a fraction of the complex computing behavior of the biological neurons that comprise the most energy-efficient computing system that we know, the human brain.

The brain is roughly one million times as energy efficient at many of the comparable tasks we set for AI. To try to approach these efficiencies, a radically different way of computing called neuromorphic engineering is seeking to build electronic components and circuits that act more like the brain’s neurons and the synapses that connect them.

Huge amounts of work have gone into making electronics operate more like biological neurons and synapses. Some research has focused on developing new, experimental devices, but they aren’t yet reliable enough to be used in large systems. Other efforts aim to implement neurons and synapses by interconnecting many complementary metal-oxide-semiconductor (CMOS) transistors—the workhorses of digital logic—to simulate a single neuron and synapse. But this approach requires so many transistors (and a few bulky capacitors) that it greatly limits the size of the system that can be constructed, making it unclear how such brain-inspired hardware could ever scale up and compete with state-of-the-art GPUs.

But all along there was an artificial neuron and a synapse—each a single device—hiding in plain sight. We found them last year. They were each made possible by an ordinary CMOS transistor—and not even a very good one at that. This is the story of their accidental discovery and their great promise for lowering the environmental footprint of AI.

Biological and artificial neurons

Modern digital electronics is based on producing and manipulating the ones and zeros of the binary code through the operation of metal-oxide-semiconductor field-effect transistors. MOSFETs have evolved in recent years, but their classic form consists of a piece of silicon that has been doped to contain an excess of either positive (p-type) or negative (n-type) charge carriers. (CMOS logic contains transistors of both types.) The device has two terminals connected to the silicon through regions highly doped with the opposite polarity of the rest of the silicon—the source and the drain. Another terminal, the gate, sits atop the silicon that separates the source from the drain. The gate itself doesn’t connect directly to this silicon, instead resting above a thin layer of insulating dielectric.

Notably, there is a fourth terminal that attaches to the bulk of the silicon; think of this bulk terminal as connecting to the underside of the chip. It doesn’t typically get much attention, but it’s very important to our story.

When voltage is applied at the gate and the bulk terminal is grounded, charge carriers of the same polarity as the source and drain are attracted to the channel region. In the case of an n-type source and drain, that will be electrons; for p-type it will be holes. The presence of these charges forms a conductive channel that reduces the resistance between the source and the drain by several orders of magnitude, and the device switches on. As the voltage at the gate increases, this physical phenomenon produces a current signal that, when plotted against the gate voltage, rises steadily. This response is ideal for logic gates, converters, multiplexers, memories, and other digital circuits. But it is not a good fit for mimicking the behavior of a neuron.

In real neural tissue, brain cells, called neurons, consist of a cell body, a long projection called an axon, and short branching projections called dendrites. The suite of behaviors and computing this collection of components is capable of is rich and broad, but the portion that artificial neural networks hope to copy is this: When the cell body’s voltage is perturbed enough to reach a particular threshold, a self-propagating pulse of voltage, called an action potential, shoots down the axon. The axon terminates in a synapse, an electrochemical connection between the axon and another neuron’s dendrites. The action potential will then temporarily boost the voltage of this next neuron, by an amount that depends on the strength of the synaptic connection. If enough action potentials reach these dendrites in a given time—from this neuron or from others that might also form synapses there—the cell body’s voltage will surpass the threshold and trigger its own action potential.

The MOSFET Neuron

The unusual action the authors discovered is understandable if you consider that a MOSFET contains a hidden bipolar-junction transistor.

TRANSISTOR BEHAVIOR

Under normal operation, with the bulk terminal grounded, increasing voltage at the drain leads to current that increases steadily. When the voltage decreases, current follows the same sloped path. Although some pairs of electrons and holes are created by current crashing into silicon atoms, these are swept away before they can accumulate.

NSRAM BEHAVIOR

Adding resistance to the bulk terminal means these extra holes pile up, increasing the bulk voltage relative to the source. Once that voltage reaches a certain value, the hidden transistor activates, causing current to spike. Current remains high until the drain voltage drops past a certain point.

To get closer to the behavior of real neurons, artificial neurons should produce a current spike when a critical voltage threshold is crossed and then quickly relax back to a resting state on their own. This spike needs to be sudden—nonlinear. It should also exhibit some hysteresis; that is, the activation and relaxation voltages should be different from each other to ensure that current flows only for a certain amount of time.

What’s wanted from an artificial synapse, the thing that connects two artificial neurons, is less complicated, but equally important. The main thing is that its conductance can be electronically adjustable. The device’s conductive states should increase and decrease in a linear pattern and remain stable over time.

No single MOSFET working under the standard operation mechanism can reproduce either of these neural properties. Instead, it’s been done by combining them into complex circuits. Until now, each neuron and each synapse has been implemented by interconnecting dozens and sometimes even hundreds of MOSFETs, which is highly inefficient in terms of area, performance, and cost. To limit the amount of space needed, chips can multiplex their signals, sending them to neurons and synapses serially, but such sequential processing introduces additional delays.

Despite these area-and-time penalties on tasks such as audio processing, computer vision, or health monitoring, state-of-the-art brain-inspired microchips have achieved power reductions up to a thousandfold compared with those of GPUs or CPUs on the same task. If we could create neurons and synapses from individual devices that are readily manufacturable instead, we might target more massive implementations while maintaining energy efficiency.

Reinventing the MOSFET for AI

Working in our laboratory in 2024, one of my students was measuring a memory circuit that consisted of one transistor and one memristor—a type of nonvolatile memory device first fabricated in 2008. The student’s memristor circuit was built from two-dimensional material atop a silicon microchip containing MOSFETs. The MOSFETs were created in a commercial foundry using fabrication technology called the 180-nanometer node, which was cutting-edge in the year 2000.

One day the student forgot to connect the bulk terminal of the transistor. What he observed was a sudden increase in current with high nonlinearity that self-relaxed when the voltage was ramped down (a phenomenon called a hysteresis loop). This was a very promising neuronlike behavior!

After a fruitless week of trying to think of an explanation for this behavior, I (Lanza) asked Pazos, then my postdoctoral fellow, to try to observe and control this phenomenon in chips without memristors. This time, we applied pulses of voltage—like the spikes a neuron would produce—instead of the ramped voltage that my student used when he first saw the peculiar behavior.

Pazos’s new data helped us understand what was going on. The key was that oft-ignored fourth, or bulk, terminal of a MOSFET. Under ordinary operation, many mobile charge carriers flitting through the channel collide with the silicon atoms, producing free pairs of electrons and holes—a process known as impact ionization. The electric field created by the potential difference between the source and the drain causes these new free electrons to drift toward the positively biased drain and the holes to move toward the bulk terminal, which is usually grounded, removing the charge without any drama.

However, when the bulk terminal of the transistor is floating—unconnected as it was in my student’s experiment—the holes produced by impact ionization cannot be driven to the ground. Instead, they accumulate in the bulk of the silicon, increasing its voltage. Then things start to get interesting.

It helps here to imagine a MOSFET as two different kinds of transistors occupying the same physical space—the intentionally constructed MOSFET and a hidden, bipolar junction transistor. A bipolar device transmits a current signal across two p-n junctions, in this case the interfaces between the source and the channel region and the channel and the drain. This signal is in proportion to a smaller current at a third terminal in between, called the base. In our experiment, that third terminal is the bulk.

To get current flowing through a bipolar transistor, you need a big enough potential difference between the base and one of the other terminals, so that current can get across the p-n junction. Let’s say this “threshold voltage” is 0.7 volts, although the real number depends on device geometry and silicon doping. In our device, that potential difference comes from those holes that were accumulating in the bulk, because it was not connected to ground. Once it reaches the threshold voltage, the device becomes sharply conductive, producing an abrupt increase of current. This sharp current increase eventually falls off once the drain voltage is lowered, because that lowering reduces the rate at which holes are generated in the bulk. The remaining excess holes recombine with stray electrons or leak away, and finally the bulk voltage falls. This cycle of hole accumulation, current spike, and hole removal gives rise to a hysteresis loop, very much like the electrical behavior of a biological neuron as it integrates ionic currents, fires a spike, and relaxes back to its resting voltage.

Initially, we observed this behavior only in a few transistors, and the relaxation time was very different for each of them. So, to try to control it better, we adjusted the resistance of the bulk terminal using a second MOSFET. Simply setting that resistance suddenly caused all the transistors to fire at the same voltage with hardly any variability. In other words, we found we could create perfect electronic neuron behavior in a single silicon transistor by controlling the bulk contact resistance. Setting the resistance can be done by doping the silicon during fabrication, but we think the two-transistor cell—where one acts as the bulk resistance—offers much greater versatility because it allows for electronic control.

We had to make sure the phenomenon would last, otherwise such a device would be useless. To our delight, every single one of the devices we tested worked over 10 million cycles. Not even one of them failed during our tests.

The MOSFET Synapse

To be honest, we were amazed. Dozens of research groups and companies all around the world have spent many millions of U.S. dollars over the past 20 years trying to emulate these neural behaviors using experimental memristor-like devices and other things, with limited success, mainly due to reliability and cost issues. We managed it in the cheapest and most industry-standard device: the MOSFET. This result was so shocking that we decided to confirm it using microchips from a different foundry. It was successful: All the behaviors could be reproduced, and perfect yield was achieved once again.

We were happy with the results and had started the process of filing for a patent and writing up our findings for the journal Nature, when our lab made another astonishing discovery: The same kind of MOSFET could act as a synapse, too!

Recall that in ordinary operation some electrons crash into silicon atoms to create pairs of electrons and holes. We noticed that at specific values of bulk resistance a significant amount of the charge from this impact ionization would get trapped in the gate dielectric. This trapped charge interferes with the flow of current through the MOSFET, effectively changing the device’s conductance. Importantly, this new conductance is stable and adjustable at will. It was then that we realized the MOSFET could also be used as an electronic synapse.

As it was in the neuron transistor, the bulk terminal was the key. A negative bulk-source voltage drives electrons into the dielectric, decreasing conductance. A positive one pushes holes in, increasing it.

From neuromorphic device to circuit to system

Here’s how the MOSFET synapse and the MOSFET neuron, together called a neurosynaptic random-access memory, or NSRAM, could work together to achieve a simple neural circuit: Say you had a circuit consisting of three synapse MOSFETs and a neuron MOSFET. The synapses have already been programmed as we’ve described, so that each has a different conductance. Spikes of voltage with different patterns and frequencies are applied to the gate of each of these transistors. What emerges from their drains are spikes of current with amplitudes modulated by the synapses conductance values.

The spikes converge at the drain of the neuron MOSFET. With each spike, impact ionization causes charge to build in the bulk of the silicon. Some of it will drain away, but if enough spikes arrive in a short enough period of time, the bulk voltage will reach a value at which the “hidden” transistor triggers a spike of current through the MOSFET. This current would then go on to become the input to other MOSFET synapses, and so on. The behavior is exactly the kind of integrate-and-fire action real neural circuits deliver.

The competitive advantage of our single-MOSFET electronic neurons and synapses is straightforward: We can produce with only one or two transistors the electronic signals that today require, at an industrial level, dozens and sometimes even hundreds of components. And moreover, unlike other emerging technologies, our solution is fully compatible with today’s silicon manufacturing lines and exhibits a yield of 100 percent in key figures of merit with near-zero variability.

Building functional circuits for brain-inspired computing and AI based on this technology is as exciting as it is laborious. It will require us to improve our computer models to resemble the behavior of both devices more accurately and to do so with computational efficiency. We must also perform accurate circuit- and system-level simulations to validate computing architectures, design peripheral circuitry to drive and convert signals, and undergo multiple fabrication rounds to optimize performance.

But all that will be worthwhile, because it could result in brain-inspired microchips for AI with better energy efficiencies than what we have now. These chips will first be a fit for smaller-scale, “edge-AI” tasks, such as bringing greater intelligence to battery-powered systems. But if we can scale up such chips, maybe in the long run they can compete with state-of-the-art GPUs.

There are over 7,000 natural languages today, but that doesn’t stop people from occasionally making up completely new ones. These constructed languages, or conlangs, include Dothraki, Klingon, and various Elvish languages. Now, an AI model called ConlangCrafter is also capable of generating new languages—and it is particularly good at it.

In a paper published 27 June in the Proceedings of the Association of Computational Linguists, researchers analyzed ConlangCrafter’s language-generation abilities, reporting that it can develop a diverse array of novel languages that consistently abide by their rules.

How ConlangCrafter Creates New Languages

In previous work, Gašper Beguš, an associate professor of linguistics at the University of California, Berkeley, showed how large language models (LLMs) can analyze languages to the same extent as most humans. In his most recent endeavor, he set out to push the language boundaries of AI models even further.

“Creating an entire language is not an easy task at all,” Beguš says, noting that some people have dedicated their careers to creating conlangs for movies, books, and video games.

But Beguš sees additional value in making AI models capable of creating truly novel languages beyond what humans could imagine. “[Models] are able to imagine or come up with things that we might not, and we can learn so much from that,” he says.

For example, ConlangCrafter can create new languages with unconventional communication systems, such as a language for a cephalopod species that uses colors and gestures instead of sounds. Of course, while this “color language” generated by ConlangCrafter isn’t truly what an octopus uses for communication, Beguš envisions these imaginary languages as a means for studying nonhuman-centric languages in greater detail.

Beguš and the rest of the team, including Morris Alper, a postdoctoral researcher at Carnegie Mellon University, and Moran Yanuka, a Ph.D. student at Tel Aviv University, designed ConlangCrafter so that it can apply a wide range of linguistic rules in terms of how sounds are organized in a language (phonology), the relationship between word and sentence structure (morphosyntax), and vocabulary.

A random number generator regularly introduces variation so that every language comes out different. A built-in editing loop then reviews the result for contradictions and fixes them. Users can choose whatever mix of rules they want, or ask ConlangCrafter to make up its own rules.

“[Models] are able to imagine or come up with things that we might not, and we can learn so much from that.” —Gašper Beguš, University of California, Berkeley

“You can choose whatever flavor of language you want,” says Beguš. “You can create a mixed language between Japanese and Esperanto, for example.”

“The goal is for the languages to be creative, so they should all be different from each other,” says Alper, who specializes in multimodal machine learning and computational linguistics. “You also want them to be consistent, because a language is like a system of rules, and those rules shouldn’t contradict each other.”

To evaluate diversity, the team measured how much the generated languages differed from one another across key linguistic features such as the basic word order used in sentences. To evaluate consistency, they checked whether translations into each invented language correctly followed that language’s own rules.

They compared languages generated by ConlangCrafter to languages created by general-purpose LLMs, such as Gemini-2.5-Pro. “Our full system can be about twice as diverse and almost 70 percent more consistent than simply prompting an LLM to invent a new language,” says Alper.

ConlangCrafter in Natural Language Processing

David Mortensen, an assistant research professor at the Language Technologies Institute at Carnegie Mellon University who was not involved in the work, says that ConlangCrafter could help natural language processing researchers better evaluate the ways in which the structure of a language affects the performance of a model.

“There is a substantial body of research that suggests that linguistic structure–both at training time and test time–does affect model performance,” he says. “Hypotheses in this area have been very hard to evaluate, however.” He adds that a tool such as ConlangCrafter could help facilitate experiments on the effects of factors such as language typology and lexicon in a scientifically sound and reliable way.

ConlangCrafter is available for free online. Its creators note that the system is currently limited in more complex linguistic dimensions such as semantics, contextual and conversational use of language, and the visual aspects of writing.

Beguš envisions expanding upon this research to study the Sapir-Whorf hypothesis, which suggests that the way we speak influences the way we think and perceive the world. For example, this could involve running simulations of different worlds, each with its own language, exploring its impact on societies. “That’ll be a nice next step,” he says.

This story was updated on 29 June 2026 to correct Moran Yanuka’s name as well as the title of the Proceedings of the ACL.

This article is brought to you by Capital One.

After five years leading natural language understanding and eventually the entire Alexa AI organization at Amazon, Prem Natarajan made a nontraditional move: He became Chief Scientist at a bank. Not just any bank: Capital One, a financial institution serving over 100 million customers, helping everyday Americans manage their financial lives.

For Natarajan, a veteran of DARPA-funded research and academia who had watched machine learning evolve from task-specific applications to foundation models, the logic was clear. Some of the most interesting advances in AI research and deployment were shifting from big tech’s horizontal platforms to industry verticals like finance, where the most complex problems aren’t just building models but making AI work under the constraints of real-world customer problems, contextual business knowledge, continuous learning, with an incredibly high bar for accuracy and privacy.

That’s also what made Capital One the right place to do it. For decades, the company has been recognized as one of the most data- and analytics-driven financial institutions in the industry. Its business model from the very beginning was built around using data and technology to personalize financial products for customers. A decade ago, Capital One went all in on the cloud and rebuilt its data ecosystem, creating a unified environment for data, compute, and AI and machine learning experimentation. Today, its modern infrastructure, disciplined approach to governance, and deep bench of talent form the foundation that allows it to lead in enterprise AI.

Advances in AI research and deployment are shifting from big tech’s horizontal platforms to industry verticals like finance.

So, why does a bank need a Chief Scientist? The answer lies in a fundamental misconception about AI in financial services. Most financial institutions still view AI as a technology to deploy – leveraging the latest large language model, deploying it through APIs, and integrating it into existing workflows – rather than a scientific discipline. Capital One is doing something different: building a scientific community and research organization to solve real-world customer problems and invent impactful AI solutions that don’t yet exist.

While widely available foundation models can handle general tasks, they can’t yet solve many domain-specific challenges, such as detecting fraud in real-time across billions of transactions, or providing state-of-the-art conversational tools so customers can engage when, how, and where they want to.

These challenges of making AI reliable, scalable, and well governed require original research and scientific innovation that is funneled back into the business to create real-world applications to address customer needs.

The Constraints That Demand Innovation

Prem Natarajan, an IEEE Fellow, is Chief Scientist at Capital One. “If you want to solve really important problems in AI and see your work come to life, this is one of the few places you can do that,” he says.Capital One

Because banks are dealing with people’s finances, there is an incredibly high bar for getting it right when it comes to AI. Take fraud, for example. Even a minor fraud event can have a devastating impact on certain customers. The best fraud models and platforms can detect and help mitigate fraud in the time it takes someone to tap their card, which is table stakes for protecting customers and their financial information with accuracy and speed. Looking at these types of challenges, Capital One and Natarajan saw that serving millions of customers meant solving AI problems at a scale and complexity that many enterprises don’t encounter. These same constraints create a unique research environment.

At Capital One, the approach to building AI is to provide value to customers in ways never possible before, improving their financial lives and meeting them where they are with services they actually need. That focus, combined with massive scale and world-class risk management requirements, makes the scientific problems both harder and just as consequential as those found in most big tech labs.

Advancing AI Through “Destination-Back Thinking”

Capital One’s approach to AI research and innovation starts with what Natarajan calls “destination-back thinking.” Rather than asking what’s possible with current technology, the team envisions the customer experience they want to deliver – perhaps a car buyer who works long days and can only research the options at 10 p.m., or a customer facing an unexpected expense who needs immediate, personalized guidance – and then works backward to identify the scientific breakthroughs required to get there.

“You’re thinking back from where you’re providing incredibly valuable services,” Natarajan explains. “Once you have that vision clearly, you work back and say, what are the gaps? What are the things we need to invent?” This ensures that when problems are solved, the impact is essentially guaranteed, because the team has already identified what will make a tangible difference in customers’ lives.

But methodology alone isn’t enough. Capital One’s nearly 15-year bet on cloud-first architecture created something rare in financial services: a unified data and compute ecosystem that can support the kind of scientific experimentation typically seen in big tech research labs. As the only major U.S. bank to go all-in on public cloud infrastructure, Capital One eliminated the legacy systems that can constrain AI research at most financial institutions. This modern tech stack enables rapid iteration, large-scale model training, and what Natarajan calls “continuous learning,” systems that improve after deployment rather than degrading over time. This unique approach to infrastructure is a critical component in making new categories of research possible.

Agentic AI: From Research to Production

The research agenda manifests in systems already serving customers. Early last year, Capital One launched what may be the first fully agentic AI customer service experience built entirely in-house by a bank: a car buying tool that takes actions on behalf of customers based on their requests, not just answers questions. Behind it lies extensive research into multi-agentic AI reasoning systems that can navigate real-time data, business knowledge, constraints, and guardrails, with various agents that can work together to accomplish complex tasks.

Capital One has launched a fully agentic AI customer service experience powered by extensive research into multi-agentic reasoning systems that can navigate real-time data.

The team is also working on solving things like tokenization challenges, protecting sensitive data while enabling model training. To accelerate this cutting-edge work, Capital One has established partnerships with Columbia University, the University of Southern California, and the University of Illinois, and became the only bank funding NSF’s national AI research centers in 2025, investing millions in initiatives that span mental health, materials discovery, science, technology, engineering, and mathematics education, human-AI collaboration, and drug development.

In the spring of 2026, the company hosted its inaugural AI Symposium to deepen connections and foster insight-sharing between the scientific AI community, leading AI labs, startups, and its own technology, science, and AI leaders and partners.

Building a World-Class AI Organization

Capital One is building the next generation of AI talent. Join the team inventing impactful AI solutions to shape the future of finance. Learn more at https://capitalone.science/

External validation suggests the strategy is working. Evident AI ranked Capital One as the leading bank in AI talent and a global leader in AI innovation for three consecutive years, noting the bank accounted for 38 percent of all AI patents filed by the top 50 financial institutions. Capital One was also recognized by IFI Insights as the only financial institution among the top U.S. patent leaders in agentic and generative AI in 2025, alongside the likes of Google, NVIDIA, DeepMind, IBM, Microsoft, Intel, Adobe and Samsung. Capital One’s AI team – which has experience from leading AI labs and top universities – represents expertise rarely found outside Silicon Valley.

But recruitment requires a mission. “If you want to solve really important problems in AI and see your work come to life, this is one of the few places you can do that,” Natarajan says. The pitch is consistent: Capital One isn’t just optimizing algorithms for niche financial applications like high frequency trading, it’s using science to enhance financial experiences for over 100 million everyday Americans, expanding engagement and real-time insights, personalization, and access to their personal finances and products like never before.

Capital One was recognized as the only financial institution among the top U.S. patent leaders in agentic and generative AI in 2025, alongside the likes of Google, NVIDIA, DeepMind, and Microsoft.

The frontiers Natarajan is most excited about – agentic AI systems that can dramatically improve performance by reframing how problems are solved, and domain-specific reasoning that understands contextual and financial nuance – represent the next phase of innovation. “By just casting the problem in an agentic framework, you can actually get way more performance” from the same underlying models, he explains.

It’s this kind of applied research, like translating general capabilities into production systems for millions of customers, that defines the Chief Scientist’s mandate. When recruiting talent to his AI team, a group comparable only to the most sophisticated tech companies in caliber, Natarajan frames the opportunity around a mission. He invokes Steve Jobs’ famous challenge to John Sculley: “Do you want to spend the rest of your life selling sugared water, or do you want to change the world?” For Natarajan, the parallel is clear. Building AI systems that transform financial services for millions of everyday Americans – that’s changing the world. And it requires the kind of scientific rigor that only a Chief Scientist can lead.

In the mid-noughties, when music by the Killers and Franz Ferdinand blared out of every pub and nightclub I passed, I spent my days and nights struggling through a Ph.D. in applied mathematics. My research focused on simulating how special light waves interact in liquid crystals and using simple equations to approximate and understand those interactions. When I look back at my thesis now, liquid crystal technology is old hat, and I imagine my work could be completed with AI assistance in a matter of days—maybe hours.

But the same cannot be said for the work of the pure mathematics Ph.D. students with whom I shared a cramped office at the University of Edinburgh. At the time, I felt sorry for these colleagues, who day after day sat at their desks, seemingly tearing their hair out and making no progress. (Though I was struggling too, I was at least always making some headway.) When we finished and went our separate ways, some hadn’t even published a paper.

Now, in hindsight, I finally understand why they toiled for years on abstract mathematical problems that only a handful of people in the world care about. It wasn’t arrogance, as I thought at the time; they weren’t trying to prove their superior intelligence by being the first to solve a seemingly intractable mathematical problem. It wasn’t even a form of masochism (which was my second guess)—penance for some imagined inadequacy. I realized they derived joy, satisfaction, and meaning from the long journey toward understanding.

“Sometimes, understanding just strikes you as being very beautiful.” —Jeremy Avigad, Carnegie Mellon University

“Sometimes, understanding just strikes you as being very beautiful. Sometimes it’s a feeling of accomplishment, like completing a marathon,” muses Carnegie Mellon University mathematician Jeremy Avigad. “But it’s not quite either of those: It’s just a wonderful feeling when you’ve been thinking long and hard about something complex, difficult, and then—all of a sudden—it just comes together.”

This feeling has driven mathematicians throughout history. Likewise, the way mathematicians pursue that feeling has changed little over the centuries. They notice or imagine links, patterns, or properties in numbers, shapes, or logical structures. From this, they write conjectures—unproven statements of their speculation. They or other mathematicians then use logical reasoning and the tools of mathematics in often creative ways to prove or disprove those conjectures. Finally, yet other mathematicians verify (or challenge) the proofs.

Invariably, this process requires a whole heap of thinking time. “I went to a pure maths camp with classes where we would sit with hard maths problems for half an hour and no one would say anything—everyone was just thinking,” says Krystal Maughan, a mathematician and computer scientist about to get her Ph.D. at the University of Vermont. “But then we would work together and kind of tease out the problem.”

This is the age-old joy of math in action. But today’s AI systems are starting to make inroads into bypassing this slow, deliberative process. Taking this trend to its logical conclusion, what happens if AI makes the mathematician’s struggle completely unnecessary? Might AI even sideline humanity completely?

AI’s Growing Role in Mathematics

For decades, computation has accelerated mathematical progress. This began 50 years ago, when mathematicians used a computer to prove the four-color theorem, which asks whether any map can be colored using no more than four colors, with no adjacent regions sharing the same color. The answer is yes, and the computer proved it, controversially, by checking 1,936 cases in a way no human could realistically verify.

Yet throughout this computational era, even in proofs relying on massive computational resources, the role of the human mathematician has remained central. Humans propose conjectures, guided by intuition. They devise strategies to prove them, guided by creativity and experience. And humans verify whether those proofs are correct.

Now AI is challenging the status quo. In just a few years, large language models (LLMs) have evolved from “stochastic parrots,” capable of little more than regurgitating basic mathematics scraped from the internet, into advanced mathematical reasoning machines.

Last summer, systems from Google DeepMind and OpenAI reached a level equivalent to the world’s most mathematically gifted high school students, achieving gold-medal status at the International Mathematical Olympiad. In this annual competition, contestants must solve six notoriously difficult problems from various areas of mathematics.

Earlier this year, Google DeepMind’s experimental AI system Aletheia achieved an even more significant milestone when it autonomously produced publishable Ph.D.-level research results. While the work itself is obscure mathematically—calculating structure constants in arithmetic geometry—the significance lies in the complex reasoning it displayed in tackling an unsolved mathematical problem. And more recently, a new general-purpose AI system from OpenAI disproved an important conjecture in combinatorial geometry. This result would have been worthy of publication in a major mathematics journal if humans had been the authors, and top mathematicians hailed the feat as a milestone for AI in mathematics, demonstrating independent, original, and sophisticated thinking.

Another shift has come from combining LLMs with mathematical tools known as proof assistants, which have been around for more than a decade. These systems—such as Isabelle, Lean, and Rocq—are specialized programming languages that check mathematical proofs step-by-step, verifying their logical correctness. Traditionally, mathematicians have had to translate their theorems and proofs into this machine-readable format by hand, a laborious process known as formalization. Now, LLMs are starting to remove this bottleneck, automating the translation of informal proofs into formal code that proof assistants can verify.

From Human Proof to Formal Proof

Euclid’s famous proof that there are infinitely many prime numbers appears very different when formalized in Lean, a proof assistant. Human mathematicians routinely skip steps and rely on shared understanding; formalization makes every assumption and inference explicit so a computer can verify the proof.

❶ Definitions must be explicit. The proof formally defines p as the smallest prime factor of n! + 1 before it can use that quantity.

❷ Formal proofs build on earlier formal proofs. Here Lean invokes a previously verified theorem showing that p is prime.

❸ Hidden logical steps become explicit. A human mathematician can write that p “clearly” divides 1. Lean requires the proof to invoke a formal theorem about divisibility and show exactly why that conclusion follows.

With technical assistance from Sidharth Hariharan

Versions of such systems, sometimes called reasoning agents, are becoming highly sophisticated. In February, for example, the AI company Math, Inc. used its aspirationally named reasoning agent Gauss to formalize a proof that had earned the mathematician Maryna Viazovska, of EPFL, in Switzerland, a Fields Medal in 2022. Gauss first helped human mathematicians complete the formalization of Viazovska’s solution to the 8-dimensional sphere-packing problem in a matter of days, and then autonomously formalized the more complicated 24-dimensional case in just two weeks.

Such achievements suggest that AI is already capable of handling some mathematical tasks long considered uniquely human. As the technology advances, more of the day-to-day work of human mathematicians is likely to become fair game for AI.

Mathematicians Debate AI’s Role in Discovery

Human mathematicians could become “priests to oracles.” —Yang-Hui He, London Institute for Mathematical Sciences

In September 2025, I attended the 12th Heidelberg Laureate Forum—an annual conference that brings hundreds of young mathematicians and computer scientists together with their intellectual idols. AI dominated the conversation and, from the get-go, tension was in the air.

Speakers described a future in which superhuman AI mathematicians transcend human knowledge and capabilities: forming conjectures, searching solution spaces, proving conjectures, and finally verifying the proofs and generalizing the results, all without human involvement. If this future comes to pass, Yang-Hui He of the London Institute for Mathematical Sciences memorably declared, human mathematicians could become “priests to oracles.”

While such startling predictions were being voiced on stage, my gaze was drawn to the audience. Frowning, fidgeting, and exchanging furtive glances—the crowd’s unease was palpable. Trill White, a student at Australia’s Deakin University, later recalled sitting in that hall and thinking: “ ‘That’s devastating. What will people have to contribute to mathematics? Will it become something that no one understands?’ I did get a sense that this is going to change everything.”

“We certainly started realizing AI has the potential to replace us.” —Jessica Randall, Google Developer Groups

Jessica Randall, a South African mathematician for Google Developer Groups, says she sensed a collective existential dread rising among the young mathematicians. “I could feel everyone was worried, because they hadn’t thought that far ahead,” she says. “It was like a big bombshell that hit us, and we certainly started realizing AI has the potential to replace us.”

Some established mathematicians, including He, seem comfortable with AI taking on tasks that are currently the preserve of human mathematicians. That’s because they just want to know the answers to the biggest questions in mathematics—such as the six remaining Millennium Prize Problems—even if AI does it all. “A lot of mathematicians are pragmatic and just want to understand. They would sell their soul for the solution to a problem,” jokes Avigad. “Whatever it takes, right?”

But this “just want to know” camp is by no means the only faction: Most mathematicians do not hope or expect AI to replace them entirely. Instead, two broad alternatives are emerging. The first is a human-centric aspiration that prioritizes human understanding of mathematics and treats AI as a tool, much like a calculator. The second is a collaborative “teamwork makes the dream work” vision, where humans and AI work together to tackle problems neither could solve alone.

The Human Role in Mathematics

Numbers are “a way of bringing us to agreement.” —Akshay Venkatesh, Princeton University

Fields Medalist and Princeton mathematician Akshay Venkatesh has been thinking about this topic from the human-centric viewpoint for years. In 2022, he used his Fields Medal Symposium to implore the mathematics community to deeply consider what AI might mean for the practice of mathematics. At the time, the idea that AI could replace mathematicians seemed far-fetched. Now, he says, “we’re reaching the point where, for at least some tasks with abstract mathematical reasoning, computers are becoming competitive with humans.”

For Venkatesh, the question is not just what computers can do, but what mathematics is for. “Sometimes I think when we use numbers, it’s not so much that we are describing phenomena that are intrinsically numerical, but that we can all agree exactly what the numbers mean,” he says. “It’s a way of bringing us to agreement.”

Mathematician and machine learning expert Maia Fraser, of the University of Ottawa, shares this sentiment. She says the joy she derives from mathematics is something distinctly human that integrates the subconscious and conscious mind. She describes starting with an intuitive sense that a certain thing should be true and gradually bringing out something that she can express in a rigorous proof. Communicating and sharing these deep-born thoughts is “a form of collective intelligence that is something beautiful about the human spirit,” she says.

By these arguments, an AI proof of a mathematical conjecture that has stubbornly resisted human efforts would be useful only if comprehensible to humans. “That the statement can be proved by AI is already useful information,” concedes Fraser. “But then it’s still an open problem to come up with an elegant, beautiful human proof.” Even if no such proof exists, she says, searching for it “is still a valuable endeavor.”

AI and the Future of Mathematical Collaboration

A more collaborative approach to AI in mathematics comes from Terence Tao, who first competed in the math Olympiad at the age of 10. In 1986, 1987, and 1988, he won bronze, silver, and gold medals, respectively, making him the youngest winner of each of the three medals in Olympiad history. Now a Fields Medalist and professor at the University of California, Los Angeles, he has earned a reputation as one of the most gifted mathematicians alive.

Unlike some of his peers, Tao is neither dismissive of AI nor fearful. Instead, he sees it as the catalyst for a fundamental shift in the discipline—a transition toward what he calls “big mathematics.” He envisions a future of large-scale, decentralized collaborations between humans and machines, where complex mathematical tasks can be diced and sliced, with humans claiming the creative parts and AI doing the lion’s share of the technical grunt work.

Three Futures for AI in Mathematics 

Already, Tao is experimenting with this concept, working on problems alongside scores of online collaborators, some using AI tools. “A hundred years ago, almost every mathematics paper was single author,” he says. “But now I collaborate with people I’ve never met—and maybe in the future, I won’t even know if they are AI or real people.”

The key to Tao’s vision is uniquely mathematical: formalization. When a proof is translated into code and checked step-by-step by proof assistants, it removes any chance of human error or dishonesty. This approach changes how collaboration works, because trust is established through verification rather than reputation or rapport. An idea from an unknown researcher or even an amateur can be taken seriously if it has a formal proof.

“If it wasn’t for this formal verification layer, opening projects up without any safeguards would just be a disaster,” adds Tao. “But in math, we can completely check and verify outputs, and this really filters out a lot of the rubbish.”

The Risks of AI in Mathematics

From the young researchers at the Heidelberg Laureate Forum to some of the biggest names in the field, mathematicians all seem to agree on one point: AI has the potential to transform their discipline. But there’s far less consensus on what that transformation will mean in practice.

Some worry about the accessibility of AI tools. Traditionally, mathematicians have required little more than intuition, training, and a pen and paper to advance their field. If this slow, deliberative process is no longer valued by society, and particularly by research funders, then mathematics could become an elitist activity, only practiced by select organizations that can afford to work with proprietary AI models.

Another concern is motivation. As AI systems take on more of the work, the incentive to engage deeply with difficult problems may weaken. Princeton’s Venkatesh says that the long human process of formulating and understanding a proof may be hard to justify, not just to funders, but even to mathematicians themselves. “There have been times where I’ve spent years thinking about something, and I’ve slowly struggled to understand it,” he says. “If your computer can do large chunks of that for you, will you have the motivation to spend that time?”

That concern extends to the next generation. If students can use AI to jump straight to answers, they most likely will. But every time they skip the struggle, they miss an opportunity to build the foundations of their own unique intuition. Over time, some worry, the next generation of mathematicians may suffer from a form of intellectual atrophy, unable to think outside the AI box that trained them.

In response to such fears, the mathematics community is taking action. Individuals are writing essays, organizing workshops, and debating in journals, while institutions and community groups are developing guidelines for how AI should be used in research and publication. Indeed, mathematicians are applying the same rigor and curiosity that they use every day to reckon with the challenges of AI. Taken together, these efforts reflect a broad effort to try to retain control over the direction of mathematics in the era of AI.

So, is AI sucking the soul out of math? In one way, it is doing the opposite. It is forcing mathematicians to confront deep questions about what mathematics is, why they have devoted their lives to it, and the purpose math serves in society. At the same time, though, it is reshaping the practice of mathematics in a way that may be difficult to reverse.

“Mathematics makes me a better problem solver at normal problems, because it frames my mind to think in a very logical, rational way,” says Randall, who noted the existential dread at the Heidelberg Forum. “It helps with every aspect of my life.” As AI transforms mathematics, many researchers wonder whether future mathematicians will be able to say the same.

Take a moment and try to imagine your life without the wireless advances of the past three decades.

Have you lost your luggage? What a shame AirTags have not been invented. The airline representative has promised to call with updates, so settle in for a long wait by the kitchen telephone, because there are no affordable cellphones. You’ll be stuck listening to whatever is on the radio while you wait, because there are no streaming services. That’s not even to speak of all the movie plots that would have been ruined.

This is just a tiny sliver of how wireless technology makes itself felt in your day-to-day existence. The effects it has had on supply chains, infrastructure, and how the economy runs have been world-altering.

None of it would be possible without the radio-frequency integrated circuits that allow all our devices to unobtrusively send and receive information.

Now imagine what the further evolution of this technology will bring: Wide-spread autonomous vehicles, quantum communications, 6G mobile service and satellite communications. Continued momentum will depend on newer and more advanced versions of today’s RF chips.

But there’s the rub. Whereas the design of most of the world’s computing chips has been standardized into its own science, RF design has remained stubbornly in the realm of art. A dark art, even, that is mastered only through years of experience. As any sorcerer will tell you, the dark arts keep their own schedule. And that schedule is impeding progress not just in RF chip design but in every other technology that depends on it.

About seven years ago, in the wake of AlphaGo’s victory over world Go champion Lee Sedol, my students at Princeton and I began to wonder: Could AI be taught this art as well? Recent successes suggest that, to a large extent, it can. Over the last few years, our group and other leaders in the field have started to develop machine-learning-driven algorithmic methods for designing RFICs. Some of the resulting chips look more like modern art than circuit layouts. Yet in many cases, the physical prototypes bested state-of-the art circuits in terms of performance. The real achievement, however, is that it took the AI orders of magnitude less time to conceive a working design than it would a human designer.

This is not about one or two RF chips. AI-enabled design could be the future of all RF design, and maybe much more.

The Dark Art of RFIC Design

So why do these chips all have to be crafted by hand? Why aren’t RFICs designed with an algorithmic synthesis process, much as CPUs and GPUs are?

The design of RFICs is an exercise in engineering across multiple physical domains. Maxwell’s equations, operating across different spatial and temporal scales, govern how electromagnetic fields interact with active and passive devices that must be carefully codesigned for the chip to function. Alongside these are the laws of thermodynamics, which determine how heat is generated and removed during operation, as well as the mechanics of thermal expansion and contraction that dictate how reliably the chip and its packaging survive temperature changes.

Simultaneously accounting for all the physical constraints these impose makes the design space almost impossibly large. Every decision involves complex priorities that often compete with one another, preventing the optimization of any of them.

To better understand the issue, let’s walk through the steps involved, after which you’ll better understand why a single new chip design takes years and tens to hundreds of millions of dollars.

Most of the area of radio-frequency integrated circuits is dominated by complex electromagnetic structures. Human-designed RFICs, like this broadband power amplifier [1], start with templates and follow a symmetric, understandable pattern. But freed from the constraints of human-designed templates and the need for humans to even understand the rationale of electromagnetic structures, power amplifier ICs [2–5] and low-noise amplifiers [6] can take on truly wild-looking yet efficient designs. SENGUPTA LAB

Let’s say you’re an engineer assigned to design a new 28-gigahertz power amplifier for a 5G-millimeter-wave handset. (This is the type of RFIC that boosts the 5G signals on your phone and transmits them to the antenna where they can be picked up by a distant base station). Where do you start?

RFIC design has some features in common with house building. Just as the blueprint for a house dictates the number of bedrooms and bathrooms to be built and the hallways connecting them, the blueprint for an RFIC—called the architecture—establishes the kinds of elements the RFIC needs to fulfill its intended function. Instead of rooms, the architecture includes, for example, the number of stages of amplification your power amplifier needs. Instead of hallways, it shows the paths that signals must take to get through those stages.

The blueprint for RFICs is actually mostly hallway; passive elements, like inductors and transmission lines, take up far more real estate than active elements like transistors.

Here’s why. As you have probably experienced yourself, a typical CPU’s transistors overheat when faced with operating frequencies of just a few gigahertz. The frequencies RFICs can operate at are higher by an order of magnitude—28 and 39 GHz for 5G signals, 26.5 to 40 GHz and even higher for satellite communications, and 77 GHz for automotive radar. Under this onslaught, a CPU’s transistors would fail.

RFIC transistors avoid this fate because these chips cleverly manage the signal’s energy with careful electromagnetic design. This takes the form of byzantine networks of metal elements that dominate the chip’s real estate. These structures are geometrically regular, often symmetrical, and so intricately constructed they sometimes resemble lacelike filigree. But while they may look decorative, they are essential to the chip’s functioning.

Electrically speaking, these “hallways” work more like the chip’s plumbing. Like plumbing, this extensive labyrinth of passives confines electromagnetic energy only to the places it should be traveling around the chip.

The major challenge in RFIC design is putting all these elements together to ensure they work, just as constructing a house from its blueprints demands exact specs for load-bearing beams, pipes, and external walls. On an RFIC, the architecture needs to be realized with physically fabricable transistors and passive components that are connected just so, to permit the signal to travel through the chip and be processed. The way these devices are connected locally is what we call the circuit’s topology.

The RFIC Design Process

To make that power amplifier, then, your first step is to identify a candidate circuit template: The combination of structures that will meet the goals of a particular architecture with a specific circuit topology. Over the years, researchers have eased your burden by developing reusable design templates for specific functions. For example, templates suggest how many amplification stages a circuit needs (because sometimes, combining the output of two smaller amplifiers will result in better bandwidth and efficiency than you would get from a single larger one). And they suggest what the general configuration of the passive structures should be. Today there is an extensive library of such templates.

However, these can’t simply be used off-the-shelf, because each comes with trade-offs. Some have better gain at the expense of stability; some better bandwidth at the expense of efficiency; still others are more energy efficient at the expense of output power, and so on. There is rarely a clear best choice.

To arrive at the “sweet spot” where all these different parameters are balanced into optimal harmony, designers will typically lay out several different versions of the circuit, using intuitions and methods they have picked up in their years of training.

The challenge is that the decision around the architecture, circuit topology, or the electromagnetic passives cannot be done separately. One decision influences the others. So, designing an RF circuit can often feel like trying to fit an oversized carpet into too small a room—press down one corner, and another pops up.

At microwave and millimeter-wave frequencies, even the smallest misstep is the difference between a chip that works and one that doesn’t, and any number of things can go wrong. For example, when an electromagnetic wave encounters a transistor—or any other component —the path it travels must be properly “matched” to what comes next. If it isn’t, some of the energy reflects backward instead of flowing forward. Imagine trying to connect a high-pressure fire hose directly to a narrow garden hose. Without the right adapter, water will splash backward at the junction. Very little will make it through. In electronics, this is called the impedance-matching problem.

To prevent those reflections, engineers design special transitions, essentially microscopic adapters, that smooth the handoff between components. On a chip, these adapters can be surprisingly intricate. They don’t just pass the signal along; they can also split it, combine it, or distribute it across multiple paths with carefully controlled timing and strength.

Once you’ve done the architecture, plumbing, and everything in between comes the moment of truth. Have all the choices you have navigated through the enormous design space resulted in an RFIC that meets its specifications? If the specifications are not met, you will have to go back, either redoing the topology or the entire architecture, and repeat the whole process. So get ready for months of time- and resource-heavy simulation and iteration. Perhaps you now see why, for decades, a core belief has persisted in the RFIC community: “RF design is an art.” It was said that only an experienced designer—with an artisanal understanding of how the pieces make up the whole—could master the subtleties of analog and RF design. Unfortunately, this entrenched notion has long held back algorithmic innovations in the field just when we need them most. Traditional, artisanal RFIC design is hitting its limits as the complexity of these systems inexorably grows.

AI for RFIC Design

While RFIC designers continued their battle against their “oversized carpet” problem, a series of interesting developments emerged in allied disciplines. Across a range of other previously intractable problems like protein folding and climate modeling, AI has been able to successfully navigate multidimensional complex spaces. This gave us the incentive to look deeper into AI for RF. After all, the combinatorial complexity of protein folding is not that different from the nature of the design space in our domain.

We were not the first to think of using artificial intelligence to speed up parts of RFIC design. Researchers had previously trained machine learning algorithms on circuit templates in the hope of speeding up the normal optimization processes. While this approach was undoubtedly faster than humans at optimizing templates, it still relied fundamentally on libraries of existing designs invented by humans.

We didn’t want that. We wanted to break free from the restrictions of prefabricated topologies. Because while a designer’s experience and hard-won heuristics are crucial to building a working design, they also place fundamental limits on it. Furthermore, such an approach would necessarily require simulation steps as part of the optimization cycle, and even the fastest simulations use a lot of computing resources. Worse still, in many advanced cases, such as for broadband designs, there are no existing templates.

But if we didn’t start with templates, where could we start?

The goal here was to allow algorithms to determine—entirely from scratch—every parameter for architecture, constituent circuits, and electromagnetic passives. This approach differs fundamentally from conventional optimization, which is limited to determining the parameters—like transistor dimensions and passive component geometries—that optimize structures originally devised by humans.

In our new approach, the architecture begins essentially from nothing and is progressively assembled through successive iterations. The system explores the design space by generating myriad candidate circuit combinations and mapping the resulting performance trade-offs as it navigates this landscape. Because the process is not biased by prior human design choices, it can produce completely novel circuit topologies that look markedly different from those created by human designers.

In some ways, the approach echoes AI systems such as AlphaGo Zero, which achieved superhuman performance not because it was trained on games played by humans but because it explored the rules by playing against itself. Similarly, our algorithm develops new circuit architectures by exploring and evaluating its own design strategies. In so doing, it learns to understand circuits, electromagnetics, and the close codesign they need to achieve the end-to-end design of RFIC.

Inverse Design for RFICs

To realize this capability, we proceeded in two stages. First, we developed a reinforcement-learning (RL) framework that determines the optimal system architecture, circuit topology, device parameters, and even the properties of the electromagnetic interfaces that connect different circuit elements. In this stage, the algorithm effectively defines how signals should propagate and interact across the system.

The algorithm trains very similarly to how a computer learns to play a game. If you let it play enough times, it can learn to play better by observing the relationship between the actions it took and the score it achieves. In a similar way, the RL agent here learns to design effective circuits by playing with a set of combinations, and over time, it can map the space between the circuit performance to its architecture, topology, and parameters. This training takes a few days to a week, but once trained, the agent can design circuits very quickly

The next step was to determine the physical structure of the IC’s electromagnetics—the plumbing—that can create the desired properties of the passive elements, which are characterized by a set of metrics called scattering parameters. These measure if a signal entering a component actually moves forward—or is reflecting backward, being wasted, as in our previous example with the fire hose and the garden hose.

Deriving the structure from the desired scattering parameters is an example of an approach called inverse design, which appears across many areas of engineering. In structural engineering, for example, one might collaborate with an architect on a physical goal—such as creating large interior spaces with high ceilings—and then determine the arrangement of arches or buttresses that can support it.

Generative AI for Electromagnetic Networks

But RF integrated crcuits pose a particular challenge for inverse design: The process must account simultaneously for circuit behavior and the electromagnetic responses of the interconnects and passive elements that link them together. But it has to figure that out without doing a lot of artisanal iterating.

So we replaced our RF circuit simulator with an AI-based emulator. This AI model can predict the behavior of electromagnetic fields going through any structure—even totally arbitrary two-dimensional shapes—without having to compute the underlying physics from scratch, as simulation tools do. It would predict the solution of Maxwell’s equations and tell you the scattering parameters for any structure you showed it, without actually doing the math. With such an AI in hand, what a time-consuming electromagnetic solver normally takes minutes or hours to accomplish is reduced to milliseconds.

We chose to build our emulator around a convolutional neural network—a machine learning model that has been remarkably successful for image processing. Such networks can extract spatial features from any structure, and it turns out that the image of a structure contains a lot of spatial information that can accurately predict its electromagnetic performance. Then we trained it on a vast number of random pixelated structures whose scattering parameters had been labeled.

Once we had our inverse-design RL and suitable AI emulator, we essentially had an end-to-end AI designer. So we asked it to design us a power amplifier.

Unconventional RF Architectures

In 2023, we published this proof of concept—a power amplifier targeting the millimeter-wave band, specifically spanning 30 to 100 GHz, which covers most of the relevant 5G and radar frequencies. The final design achieved the best combination of wide bandwidth, output power, and efficiency then reported for a silicon-based power amplifier—meaning it could amplify a large amount of data across a wide swath of frequencies—while maintaining record efficiency.

The structure of the IC’s electromagnetic pathways was unlike anything any human would ever consider. Since the AI is not trained on human designs, the layout that emerged looked more like an arbitrary pattern or perhaps a QR code than the regular symmetrical structures we are used to seeing.

One unexpected insight revealed by this prototype, and our research generally, is that there’s no evidence that the templates we’ve historically relied on are even close to optimal for modern design goals. It’s not that a human designer can never come up with a better design. But with the removal of the templates and the time to synthesize cycle upon cycle of optimized circuits, it is now clear that AI-driven synthesis could break traditional design barriers and push the limits of RFIC capabilities.

Our 5G amplifier had only one input port and one output port. Adding more inputs and outputs to a design is not straightforward. Every port electromagnetically couples to every other port, so the scattering parameters quickly add up. Two ports give you four scattering parameters. Four ports, 16 scattering parameters. The math gets ugly fast. Could our model keep up?

We next trained our model on larger classes of electromagnetic structures with many input and output ports. In 2024, we published work showing that multiport integrated circuits are no problem for these AI algorithms either. Where previously multiport electromagnetic simulation required days or weeks of toil, this model evolved new structures in minutes. Since then, a plethora of work in the space by research communities across the globe have demonstrated the power of inverse design in RFIC.

Combining the reinforcement learning framework with the inverse design, we now had the ability to create an RFIC from specifications all the way to a fabrication-ready layout. We’ve so far shown this is true for RFICs ranging from low-noise amplifiers to subterahertz and broadband power amplifiers. The hope is that this will work just as well for other circuits.

Making AI Designs Interpretable

Our goal was to make RFIC design better and easier, but we didn’t want to make it beyond human understanding. Chip testing and debugging is a long, arduous process, sometimes even more so than design. Engineers often prefer ICs to have interpretable structures, so that if a problem crops up, they can understand how the chip works well enough to debug it.

To create structures that are more interpretable, we turned to diffusion models, which you may know from their remarkable ability to generate realistic images from text prompts.

AI-driven synthesis could break traditional design barriers and push the limits of RFIC capabilities.

Imagine you go to your favorite image-generation engine and ask it to create a painting of the sky in the style of Picasso, Van Gogh, or Michelangelo. You will get images that capture the essence of their brushstrokes, their use of colors, and their framing. All are pictures of the sky nonetheless, but in different styles.

Electromagnetic design is similar in that multiple structures can have very similar electromagnetic responses. Instead of using text input, we used scattering parameters as our input, and the electromagnetic structure of an RFIC chip as our output. As part of the inputs to the diffusion model, we created a dial that sets the spatial frequency of the final structure. By turning the dial, a designer can direct the model to synthesize structures with low (classical-looking and interpretable), medium (mazelike structures), or high (pixelated or arbitrarily-shaped) spatial frequency.

From prompts to output, the entire process took about 6 minutes. With this diffusion model, algorithms can now both discover novel architectures and accelerate the creation of conventional, so-called classical ones.

All an RFIC designer needs to do is specify virtually any valid set of scattering parameters. As long as they are physically realizable under Maxwell’s equations, the model pops out a corresponding structure as if it were a vending machine.

The Future of AI-Driven RFIC Design

The results of our investigations have drawn the attention of the RF community. The traditional bottom-up design process is clearly beginning to reverse.

But there are still questions: How generalizable are these methods? Can they consistently deliver truly high performance? Can we get to a place where AI produces designs that maximize every conceivable trade-off, holistically optimizing every parameter to its most ideal physical state? We want to take this strategy beyond RFIC design and invent other kinds of circuits that are different from anything humans have ever done.

These are exciting and ambitious prospects, but we are not there yet. AI can hallucinate a design that creates bad circuits that don’t work. This means verification methods need to remain under human oversight. And, while hallucinations are rare, it would still be good to reduce their occurrence.

History suggests that meeting these dreams of the future will take much more data than we’ve been using. Before the creation of the ImageNet repository—a repository of 14 million varied, human-annotated images—image-recognition models didn’t function well in the real world. The datasets they had been trained on were too tiny to be effective. ImageNet’s massive amounts of training data ushered in a revolution that led to AI that can generalize and recognize images in the wild. The rest was history.

If the goal for RFIC and analog design is a universal foundational model—something that learns the governing laws of electromagnetics and circuit behavior—then we also need data.

The good news is that this data is plentiful. Around the world, countless engineers at companies and academic labs simulate nearly identical RF circuits and passive structures every day. The bad news is that it’s all locked away behind nondisclosure agreements.

Open ecosystems have propelled other areas, and we think the RFIC community should do the same. There had been some movement toward this. Natcast, the operator of the U.S. CHIPS and Science Act’s R&D program, would have bolstered shared infrastructure and innovation for the next generation of wireless, sensing, and defense technologies. Unfortunately, both the organization and the program it ran specifically for machine learning and RFICs have been closed.

But the momentum Natcast’s effort sparked hasn’t died out. Building on our early work, groups across the community have already demonstrated remarkable advances. AI-driven IC design is part of a much broader technological shift. From biology and materials science to automotive and aerospace engineering, AI is reshaping how complex systems are conceived and optimized. Deeper collaboration between AI researchers and chip designers will unlock the field’s full potential. It’s by no means a foregone conclusion, but if we get this right, this genie won’t stay in its bottle.

Imagine sitting down at your desk and logging in for a performance review, with an AI system analyzing the conversation. You’ve been working long hours, balancing deadlines, and your manager asks how you’re doing. You say you’re fine, and maybe even smile, but there’s a hint of hesitation and your voice wavers. As you shift your posture, your shoulders slump.

These are subtle cues that to the human eye might hint at underlying stress. But to an AI model that’s been trained only to categorize emotions as “happy” or “sad,” such nuances are likely lost. It logs the words and a smile and moves on—and unless your human manager intervenes, the fact that you’re tired, unfocused, and maybe a couple of days from burnout never enters the equation.

“Emotion AI,” which estimates how people feel based on facial expressions, voice tone, and behavior, seems to be suddenly everywhere; it’s being used in employee well-being and recruitment interviews, education platforms, and driver-monitoring systems. Technology call-center platforms such as NiCE and Genesys use AI to detect when a customer sounds frustrated and prompt agents in real time to slow down or respond with more empathy. Giant companies like Meta and startups such as Hume AI are developing more-expressive voice AI systems that can detect emotional cues in the person they’re “talking” to and adjust how they communicate.

What’s more, hundreds of companies already offer virtual AI companionship apps, a fast-growing market that may be worth an estimated US $555 billion by 2035—and robot buddies have also entered the picture. Intuition Robotics’s ElliQ, for example, is a small device vaguely resembling a white desk lamp that’s now being used to engage older adults in conversation in hopes of reducing loneliness.

But while the field of emotion AI is advancing at a rapid clip, most existing systems are focused on detecting a limited number of signals to label one specific emotion at a time—which is insufficient if you’re trying to understand the human condition. In the real world, human signals and emotions are contextual, overlapping, and constantly changing. A laugh can signal joy, nervousness, or both; a raised voice might signal enthusiasm just as easily as frustration. To make the job of emotion detection even more difficult, reactions differ greatly from one individual to the next, depending on demographics, cultural background, and countless other variables.

In other words, there’s a gap between what we’re expecting AI to pick up on and what AI can actually deliver. That’s the gap a new field of research—what we call human-context AI—is working to close. Instead of looking at just one input and labeling it, human-context AI increasingly has the capacity to take stock of an individual’s personality and character, and to track emotions in real time while combining multiple inputs, including facial dynamics, voice, tone, language, and behavior. Crucially, responses are also evaluated in the context of a specific environment, such as a performance review or professional coaching session. The result? Computers are learning to read the scene, rather than just the screen.

The Origins of Emotion AI

The story of emotion-sensing AI began almost three decades ago in the MIT Media Lab, where the American electrical engineer and computer scientist Rosalind Picard coined the term “affective computing.” Her work introduced the radical idea that computers could be taught to recognize and respond to human emotions.

Picard’s early experiments focused on single modalities: facial expressions, tone of voice, and physiological signals, such as skin conductance or heart rate. The goal was to give machines a window into human feeling, helping them become more empathetic. It was an exciting vision, but back then the science and hardware weren’t ready. Computing power was limited, sensors were crude, and datasets were narrow and biased.

Josie Norton

Over the next decades, researchers and companies got better at measuring the many ways in which humans express themselves. In the 2010s, sentiment analysis—the processing of large volumes of text to suss out emotional undertones—began to reach the mainstream. At the same time, marketing firms, including my company, Neurologyca, began using video and webcams to measure and catalogue customer reactions. Biometric devices and activity trackers, such as Fitbits and Apple watches, also became ubiquitous, generating new streams of data about people’s sleep, step counts, stress levels, and more.

Unsurprisingly, scientists soon confirmed that larger volumes of personalized data led to greater accuracy in reading human emotions. In 2019, researchers at Cornell demonstrated that combining multiple types of signals improves emotion sensing. Their system joined physiological data, such as brain activity measured by electroencephalography (EEG) and heart rate, with visual cues like facial expression, outperforming systems that relied on just one input. Around the same time, Picard and her team at MIT found that humanoid robots trained on data unique to a specific person were substantially better at reading that person’s reactions and feelings than robots acting without personalized data.

More recent studies align with these findings. In 2024, scientists in South Korea showed that fusing physiological, environmental, and personal data to recognize emotion resulted in a 32 percent error reduction. Another paper, published in 2025, demonstrated that user-specific information significantly enhances emotion recognition performance.

Today, our devices know who we are; our habits and tendencies, likes and dislikes. They’ve also gotten smaller and more efficient. Tiny, low-power cameras and microphones embedded in phones, laptops, and virtual-reality and augmented-reality devices can detect dozens of human signals simultaneously, from eye movements and micro-expressions to breathing rhythms, voice modulation, and posture. Advances in computing have also made it possible to integrate audio, video, biometric, and text data, often without even transmitting raw data to the cloud. And researchers at Stanford, Cambridge and MIT, and Kyoto University, in Japan, as well as the Software College of Northeastern University in Shenyang, China, are exploring how fusing such inputs can refine the sensitivity and accuracy of human-machine interactions.

And yet, despite so many breakthroughs, machines still can’t reliably interpret emotion or even physical stress. Just last year, a survey published in the Journal of Psychopathology and Clinical Science revealed that stress scores on smartwatches rarely, if ever, matched the level of stress that users were experiencing. In fact, a quarter of those surveyed reported feeling the direct opposite of what their smartwatches were reporting.

Why the disconnect? We’ve gotten very good at capturing signals, but not at interpreting them. A fitness tracker might infer from your heart rate that you’re stressed and recommend easing off training, but it doesn’t know if your increased heart rate is due to excitement, tiredness, or an extra cup of coffee. Gauging emotions in real-world settings is even more difficult. To solve this complex problem, machines need context.

From Neuromarketing to Emotion-Sensing AI

My company, Neurologyca, was founded in Spain in 2015, and started out in neuromarketing. Working with major European brands and conglomerates, our cofounder, Juan Graña, had realized that companies lacked solid data on consumers. At the time, most customer feedback came through surveys, which posed questions such as, “On a scale of 1 to 10, how joyful does this car advertisement make you feel?” or “Which emoji best describes your mood?” Naturally, these overly simplistic tools led to high levels of self-reporting bias, as people often misjudge or misstate their own reactions.

To get around this problem, Neurologyca set up labs, using neuroscience and cognitive science to more accurately capture human responses to products, logos, advertisements, and experiences. In addition to using biometric tools such as heart monitors, eye trackers, and EEG, we recorded millions of video frames of human reactions, logging each specific context and the resulting facial and bodily movements. To do this, we mapped over 790 points of reference, including corners of the mouth, size of the eyes and pupils, blink rate, and angling of the head. All of this data was collected and stored anonymously under strict European privacy standards.

Next, we paired this information with findings from decades of neuroscience and behavioral science studies on how biometrics, speech patterns, and human movement are related to emotion—research we continue to gather from academic institutions across Europe. We also created a database of situational contexts—for example, “watching a dog food commercial” or “hearing a new song”—and the human feelings they engendered.

In our work with companies, not only did this approach allow us to recognize nuanced emotions, it also let us identify which reactions indicated positive or negative outcomes. Take, for example, the context of horror-film trailers: Our research helped us figure out that the most successful elicit a very specific mix of emotions, namely a little bit of fear, a little bit of anxiety, but also some joy. With this knowledge, we could quickly rate viewer reactions to help a film company figure out how to tweak its trailer for the desired impact.

Neurologyca

Within a few years, we discovered that a model trained on our database could accurately evaluate emotion using just a webcam. We stopped needing to host focus groups in rooms full of equipment. Instead, we were able to do such things as sending out a new perfume sample to paid participants around the world along with a link. When people opened the link, it turned on their cameras, allowing us to record their faces as they sniffed the perfume for the first time. Suddenly, we had expanded our reach: Rather than using small focus groups in one or two countries, we could quickly assess 1,000 people across the planet, comparing how someone in Japan, India, or Germany might feel about a certain product.

About four years ago, as AI was becoming pervasive, we realized that our models had applications well beyond neuromarketing. Importantly, these models are grounded in directly observed human behavior rather than inferred patterns or loosely labeled open datasets. Looking beyond brands and companies, we established that our model could be integrated into AI systems to help them understand human emotion at a much more granular level. In other words, we could provide a layer of context.

For Empathetic AI, Context Is Key

When we talk about “a layer of context,” we mean three different types of context. The first is situational or environmental context; for example, a performance review, a telemedicine session, or a horror-film viewing. The second is personal context, which includes an individual’s specific history, goals, and baseline state. The third is behavioral context, which covers the individual’s reaction over the course of the event or interaction by evaluating real-time changes in attention, confidence, engagement, and cognitive load.

Most systems today focus on only situational context, although some are starting to include personal context. Very few include behavioral context or combine all three in a meaningful way. What we’ve built at Neurologyca is a logic layer that fuses the three and translates them into structured, machine-readable information that allows AI systems and agents to respond more effectively. Our technology is being used to enhance systems in development, as well as some that have already been deployed, including driver-safety apps like Netradyne, home assistants like Amazon Alexa, and health-care AI platforms like Sully.ai.

It works as follows: Situational context is determined by the platform or application, be it a professional coaching session, a meditation app, or a driver’s safety monitor. Personal context already lives within each respective platform—or if not, it can be created through sharing of personal data or monitoring via camera. (Most wellness and professional-development apps, for example, contain each user’s profile, history, and prior sessions.) Last but not least, behavioral context is collected and analyzed in real time using our models. In the end, our logic layer fuses these three streams of information.

Our system doesn’t assign fixed weights to the three contexts. Instead, it provides a continuous calibration, with the balance shifting depending on the specific situation. For example, a pause in speech might signal uncertainty in a performance review, but something entirely different in a relaxation setting. If signals are ambiguous or overlapping, our system reflects that uncertainty through lower confidence scores rather than forcing a definitive interpretation.

What’s more, our system can work without ever sending raw data to the cloud, thereby easing privacy concerns. In many cases, video, audio, and biometric signals never leave the device. Instead, our lightweight models extract information locally and share only what’s necessary. Cloud systems, meanwhile, are used for training, pattern analysis, and model improvement. The result is a hybrid architecture: edge-based processing for speed and privacy combined with cloud-based learning for continuous improvement.

The result? By incorporating context, AI systems are beginning to interpret aspects of the human state as interactions unfold, dynamically adapting to emotions rather than reacting after the fact. The range of potential applications is broad and still evolving. Picture a professional-development platform that uses a human avatar to perform a mock interview and then provide feedback and tips on how to appear more confident, likeable, and well-informed. Or a meditation app that knows exactly how well you slept and how anxious you’re feeling, and can recommend an appropriate breathing meditation. Or a humanoid robot teacher that can tell when a student is confused or bored and step in to get them back on track.

Avoiding Potential Dangers on the Road Ahead

There have long been debates about the ethics of emotion-sensing AI. Some critics question whether systems should attempt to infer human feelings from external signals at all. They argue that reducing people to measurable outputs risks oversimplifying human experience while opening the door to manipulation, surveillance, and unfair judgments in workplaces, schools, and public spaces.

We take those risks extremely seriously. In fact, our technology aims to reduce the dangers of oversimplifying human emotion. Human-context AI is not based on the assumption that a machine can definitively know what someone is feeling. Rather, it is an attempt to move beyond simplistic labels by incorporating situational, personal, and behavioral context, while explicitly representing uncertainty when signals are ambiguous or incomplete.

That said, ethical concerns regarding implementation are real and have shaped the kinds of projects we pursue. We would never, for example, accept military engagements to help with interrogations. Not only for ethical reasons: Emotion AI cannot reliably detect deception, and claiming otherwise would be overstating what the technology can actually do. And while our technology can be used to gauge crowd behavior and predict things like when a football stadium is at risk of becoming destructively rowdy, we don’t want our technology deployed for surveillance. In short, we believe that using our logic layer on anyone who hasn’t opted in would be intrusive and ethically problematic.

In Europe, our systems are designed to comply with the EU AI Act’s restrictions on emotion recognition in workplaces and schools; as we expand into the United States, we apply jurisdiction-specific guidelines while maintaining the same core ethical commitments.

We also don’t advise companies to become overly reliant on our technology. Hiring and firing decisions should not be based on our outputs alone. Instead, our logic layer is designed to support human understanding and surface emotions that might otherwise go unnoticed.

Let’s return to the scenario of the performance review. Never mind basic AI—all humans, and even great managers, miss things during conversations. There’s a lot happening at once, as people process what’s being said, how to respond, and the greater context of the situation. These days, many exchanges also occur virtually or via video, adding more distractions while shared context is stripped away.

While we would never claim that our models understand humans better than their fellow humans, we believe we can offer an added layer to help managers capture and interpret behavioral signals that might otherwise get lost, providing greater visibility into how a conversation is unfolding.

Our model can track patterns moment to moment, picking up, for example, a shift in engagement, an instance when something didn’t land, or a change in how someone is behaving. The model won’t tell the manager what these moments mean or what to do about them; it simply makes them easier to see and follow up.

Human-context AI is at an early stage. The use cases, the adoption patterns, and the actual impact are all still evolving. At the same time, emotion-sensing systems are quickly being incorporated into real products and platforms. And without context—without knowing why people feel the way they do—AI risks misunderstanding us in critical moments.

Artificial intelligence is the transformative, strategic technology of the early 21st century. It is significantly reshaping practically every aspect of our lives, including in ways that probably no one anticipated. Its rate of adoption and impact have been unprecedented when compared with other technologies.

AI as a distinct field was formally established in 1956 at the Dartmouth Summer Research Project on Artificial Intelligence, proposed by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon. In their August 1955 proposal for the research project, the scientists introduced the term artificial intelligence and envisioned machines capable of simulating human intelligence.

AI is the “science of making machines do things that would require intelligence if done by men,” as defined by Minsky. The professor received the ACM Turing Award, which is often called the “Nobel Prize in computing.”

Since AI’s humble beginnings 70 years ago, it has evolved significantly in its capabilities, gained prominence, and earned widespread adoption across many areas including business, education, finance, health care, industry, and the military.

IEEE’s contributions to the progress and adoption of AI throughout its journey are substantial and multifaceted.

As we celebrate AI’s 70th birthday, understanding its history, current status, limitations, and concerns is key to harnessing it for good.

The technology’s roller-coaster evolution

Although AI emerged as a distinct field in 1956, its intellectual roots extend back further. The ideas and theories that underpin AI predate modern computers such as the ENIAC, unveiled in 1946.

In 1943 Warren Sturgis McCulloch, a neurophysiologist and cybernetician, and Walter Pitts, a logician working in computational neuroscience, were inspired by the human brain. The two devised mathematical models of artificial neurons, demonstrating that artificial neural networks could perform logical computation.

Frank Rosenblatt, a Cornell psychologist, later advanced those ideas by developing the perceptron, an early neural network that laid the foundation for modern machine learning and deep learning.

A major milestone came in 1950, when celebrated computer scientist Alan Turing posed the question, “Can machines think?” In his 1950 landmark paper “Computing Machinery and Intelligence,” published in Mind, he explored the nature of machine intelligence. He introduced the “imitation game,” later known as the Turing test, as a practical means of evaluating it. The test remains an influential concept in AI and the philosophy of intelligence, as I discussed in my article “The Turing Test at 75: Its Legacy and Future Prospects,” published in IEEE Intelligent Systems.

Claude Shannon, recognized as the father of information theory, explored the potential of machines for complex reasoning tasks in his 1950 article “Programming a Computer for Playing Chess,” published in Philosophical Magazine.

In 1956 AI became a formal discipline, inspiring scientists to explore and advance it further. John McCarthy developed Lisp in 1958, and it became the dominant programming language for AI research and development. In 1959 Arthur Lee Samuel, a computer science professor at Stanford, introduced the term machine learning to describe programs that could improve their performance through experience.

In the early 1980s, renewed enthusiasm and government funding fueled the development of symbolic AI, a rule-based expert system (also known as a knowledge-based system) that encodes domain-specific knowledge as sets of rules. A notable example was MYCIN, designed to diagnose infectious diseases.

Although successful in limited domains, expert systems’ inherent limitations have restricted their broader adoption. Expert refers to a computer system that mimics human experts in a specific domain. It was popular in the early days of AI, and subsequently disappeared with advances in AI such as neural networks and machine learning.

AI’s journey was marked by periods of soaring expectations and disappointing progress, known as “AI winters,” during which funding, interest, and confidence declined. Analyses of the episodes revealed recurring causes and insightful lessons for the field.

A new phase of growth—often described as “AI spring”—emerged in the 2010s with advances in deep learning, the rise of large language models, the transformer architecture, and generative AI (GenAI).

“The imperative before us today is not only to advance AI’s capabilities but also to ensure that it remains human-centered, trustworthy, ethical, and dedicated to enhancing human well-being and societal progress.”

Unlike earlier approaches that processed information sequentially, a transformer model analyzes an entire sequence of text or audio, assessing the importance of each word or component relative to others, enabling dramatic advancements in GenAI and its applications.

Ashish Vaswani, a former computer scientist at Google, and his colleagues at Google Brain introduced the transformer architecture that underpins today’s generative AI systems in their influential 2017 paper “Attention Is All You Need.” Vaswani and Sam Altman—chief executive of OpenAI, which offers ChatGPT—are widely regarded as the masterminds behind the GenAI revolution.

AI reached new heights with the public release of ChatGPT in 2022, followed quickly by a wave of chatbots and generative AI tools that accelerated global interest.

More recently, the rise of agentic AI systems capable of increasingly autonomous operation has expanded AI’s capabilities and impact.

AI’s 70-year journey reflects an extraordinary interplay of vision, experimentation, setbacks, innovation, and impact.

For further information and diverse perspectives on AI history, check out my curated collection of articles.

Strengths and promises

AI’s pragmatic strength lies in its ability to process information, recognize patterns, and perform cognitive tasks at an unprecedented speed and scale. It can analyze vast amounts of data, extract insights, and identify trends or anomalies that are difficult for humans to detect. The programs can automate routine tasks and repetitive knowledge work, improve productivity, and reduce costs.

Chatbots and other forms of GenAI can answer queries and rapidly create text, images, videos, music, software code, educational materials, and other content on the fly in response to a user’s prompts, accelerating information-gathering, innovation, and decision-making. AI summarizes, translates, and rephrases text effectively and can assist in idea generation. It also facilitates natural-language interactions, making technology more accessible to nonexperts and the diverse global community. Its multimodal capabilities enhance its usefulness across diverse domains. Additionally, it can serve as a powerful collaborator, augmenting creativity and problem-solving capacity rather than replacing human intelligence.

AI is transitioning from standalone tools to autonomous, goal-driven systems. Agentic AI systems that can plan, act, and adapt with minimal human oversight are on the rise, enabling large-scale impact.

The 400-page AI Index 2026, published by the Stanford Institute for Human-Centered AI, reveals the technology’s enhanced capabilities and unprecedented adoption rates, outpacing those of the telephone, the television, the personal computer, and the Internet.

For a deep exposition on the current state of AI, read this analysis from IEEE Spectrum, which also published the “Great AI Reckoning” special report.

Weaknesses and concerns

Along with its benefits, AI presents significant risks and concerns. They include biased, discriminatory, and harmful responses; a lack of transparency and explainability in decision-making; privacy violations from data collected for AI training; and cybersecurity vulnerabilities including AI-powered attacks.

AI systems can hallucinate, generating confident but incorrect or fabricated information. Moreover, AI can facilitate and amplify the spread of misinformation, deepfakes, and manipulated content, undermining public trust and driving the algorithmic manipulation of public opinion. The flattering, people-pleasing, or affirming behavior known as AI sycophancy can be harmful as well.

Overreliance on AI could erode human judgment, critical thinking, and decision-making skills. And autonomous systems can make errors with serious consequences in critical domains including defense, health care, and transportation.

The technology’s development and deployment, therefore, must be guided by informed understanding, sound judgment, and responsible governance. In assessing AI’s suitability for any application, its capabilities, advantages, limitations, and risks must be carefully and holistically considered.

IEEE’s contributions

IEEE has not merely documented and disseminated AI’s progress. It has actively fostered, standardized, and guided it toward further advances and responsible use for the benefit of humanity. IEEE maintains a hub for information on its AI activities that is a valuable resource for researchers, developers, regulators, and users.

IEEE publishes 11 AI-focused journals that advance the frontiers of knowledge, including IEEE Intelligent Systems. In its AI at 70 commemorative issue, Intelligent Systems identified the 10 most influential AI articles published since 2000. The magazine, produced by the IEEE Computer Society, has inducted 10 pioneers into its AI Hall of Fame, honoring their contributions and impact on technology and society.

To foster AI research and development, since 2006, the magazine has recognized the field’s rising stars through its AI’s 10 to Watch awards. The biennial awards spotlight outstanding contributions of young researchers and professionals. Nominations for this year’s awards are open until 1 July.

Since the early days of AI, the IEEE Computer, Computational Intelligence, and Systems, Man, and Cybernetics societies have been among those that have fostered AI research and practice. The Computer Society offers a guide to becoming an AI developer.

IEEE and its societies sponsor more than 100 AI conferences annually. The conference archives are available in the IEEE Xplore Digital Library.

The IEEE Learning Network offers more than 200 courses across AI-related areas.

The IEEE Standards Association has developed more than 100 AI-related standards. Its CertifAIEd program promotes ethical design and deployment of autonomous intelligent systems.

The Institute has featured several IEEE members who have developed AI-driven applications, such as Abhishek Appaji, who has created tools to help detect psychiatric disorders.

Shaping AI’s future

The history of AI helps us understand the motivations behind developments and inspires and guides us toward the next phase of the technology’s innovation and revolution. AI’s trajectory is bound to be shaped by the collective choices we make now and in the future.

As Turing wrote in his 1950 landmark article, “We can only see a short distance ahead, but we can see plenty there that needs to be done.”

The imperative before us today is not only to advance AI’s capabilities but also to ensure that it remains human-centered, trustworthy, ethical, and dedicated to enhancing human well-being and societal progress.

Large language models have moved out of the research lab and into engineers’ daily workflow. LLMs serve as reasoning engines that can orchestrate complex tasks including identifying vulnerabilities in source code and transforming fragmented project discussions into rigorous technical specifications.

While the general public uses AI tools to write email and plan vacations, technical professionals use LLMs as core architectural elements that are fundamentally changing how digital infrastructures are built and maintained. As the AI models move into mainstream engineering practice, the demand for technical expertise is rising.

The LLM technology market is expected to grow by about 33 percent every year through 2030, according to MarketsandMarkets. The rapid expansion suggests that proficiency in implementing and securing the models is transitioning from a niche into a core requirement for technologists.

More than just a better search engine

To use LLMs effectively, technical professionals must move beyond treating them as conversational robots. At a fundamental level, the AI systems are built on the transformer architecture, a framework that replaced the older method of processing data in a fixed, sequential order. Unlike earlier models that analyzed information one step at a time, transformers use self-attention mechanisms to ingest vast datasets simultaneously.

For technical professionals, LLMs are core architectural elements that are fundamentally changing how digital infrastructures are built and maintained.

Relying on such LLMs without understanding their internal logic creates a significant reliability risk. To build tools that work consistently, developers must understand the core principles that govern how the models process information and generate results. By mastering how a model processes information and how its internal settings influence the result, developers can move away from a trial-and-error approach toward a more precise one to ensure the AI tool handles complex data reliably.

Four ways LLMs are changing jobs

Here are areas that integrate large language models.

Moving past basic prompts. Developers are using application program interfaces (APIs) to connect LLMs directly to their databases and software tools. Employing the APIs allows AI to perform work such as executing code or searching through internal repositories.

Fixing the “hallucination” problem. LLMs are at risk of hallucinations, which are generated facts or code that looks correct but actually is wrong or broken. To fix the problem, retrieval-augmented generation (RAG) forces AI to look up information in a trusted source such as a company’s database.

Prioritizing data security. When using AI with proprietary code, security is a major concern. Engineers must learn how to set up “private” instances of the models to ensure that sensitive company data stays within a secure cloud environment and is not used to train public versions.

The future of collaboration. By automating repetitive coding tasks and summarizing thousands of pages of documentation, LLMs let engineers spend more time on high-level designs and solving important issues.

Online course program helps with mastering the tech

The gap between people who use AI and those who understand how to build with it is growing wider. To help technical professionals stay ahead, IEEE offers a five-course online program, Large Language Models Demystified, available through the IEEE Learning Network.

The program, developed by IEEE Educational Activities in partnership with the IEEE Computer Society, is built for people who want to understand the “how” and the “why” behind the technology. Rather than just teaching basic prompting, the curriculum dives into the engineering behind generative AI, including:

• Evolution, impact, and hands-on exercises: the shift from statistical methods to modern transformers, including hands-on model optimization.
• Understanding transformer architectures: the mathematical core of self-attention and positional encoding, implemented in NumPy and Python.
• Architectural analysis and implementation: advanced LLM design with practical model-building exercises.
• Training and modeling with PyTorch: end-to-end pipelines in PyTorch, leveraging parameter-efficient techniques such as low-rank adaptation and quantization.
• Optimization, alignment, and deployment: performance scaling, reinforcement learning from human feedback (RLHF), group-relative policy optimization, RAG, and agentic AI.

Upon completion of the program, participants earn professional development credits and a digital badge from IEEE to verify their expertise.

Enroll in the course program on the IEEE Learning Network.

Organizations looking to prepare their teams to work on LLMs can connect with an IEEE content specialist to discuss group enrollment and tailored training paths.

By mimicking how the brain operates, neuromorphic computing can use dramatically less energy than conventional electronic AI chips. However, even the most sophisticated neuromorphic devices today are still quite simple, using only a small fraction of the number of connections found in human neurons.

Now, a new study suggests that by using sound waves, neuromorphic devices can better mimic biological neurons and operate faster and with greater energy efficiency than their electronic counterparts.

“This could make future neuromorphic hardware more compact, more parallel, and more efficient for tasks that require combining many features, such as pattern recognition, sensory processing, and data analysis,” says Xiaodong Yan, an assistant professor of materials science and engineering and electrical and computer engineering at the University of Arizona in Tucson.

Just as brains use synapses—the links connecting neurons—to help them both compute and store data, neuromorphic devices often combine both operations. Doing so can reduce the energy and time needed for conventional microchips to shuttle data between processors and memory.

Each human neuron may have thousands of synapses connecting them with other cells; one kind of neuron found in the cerebellum, the Purkinje cell, may have as many as 100,000 synapses. This extraordinary level of connectivity lets each human neuron “combine different pieces of information, compare them, and respond depending on the context,” Yan says.

In contrast, most conventional neuromorphic devices are essentially “one artificial synapse,” Yan says. Building an artificial neuron with as many synapses as a human neuron would require wiring many separate devices together. “This increases wiring, energy cost, and hardware complexity,” Yan says.

Using Quantum-Like Tricks Enables Parallel Computing

Recently, scientists have developed acoustic devices in which sound waves can encode multiple values in its waves phase. These phase bits, or phi-bits, can in turn support quantum-like logic gates and parallel computing. Whereas conventional bits only symbolize one of two digits, 0 or 1, and require a separate physical component for each bit, phi-bits each represent multiple variables and coexist within one space.

To be clear, however, operations on phi-bits are not quantum computations, only classical analogues of quantum computer systems.

Now Yan and his colleagues have developed an acoustic synapse containing multiple phi-bits. This enables multiple simultaneous computations in a relatively simple way, with lower power requirements compared to conventional electronics.

“The idea of bringing new physics to more efficiently perform complex computations is always fascinating,” says Brad Aimone, a researcher at the Center for Computing Research at Sandia National Laboratories in Albuquerque, N.M.

“It opens new opportunities worth thinking about, going forward,” says Aimone, who did not take part in this study.

The new device the scientists developed consists of three aluminum rods, each roughly 60 centimeters long and 1.25 centimeters wide, and connected by epoxy glue. The researchers used a thin layer of honey to attach ultrasonic transmitters and sensors to the ends of the rods.

Yan and his colleagues used sound waves to encode a stream of data, including images and labels that identified those images. The ultrasonic transmitters emitted these sound waves through the rods, which interact acoustically via the epoxy. Ultrasonic sensors in the device then detected the acoustic signals from the acoustic interactions.

The researchers found they could modulate the phase of phi-bits in ways that mimicked the ability of biological synapses to strengthen or weaken over time, part of why memories last or fade. This property, called synaptic plasticity, meant the researchers could train their acoustic synapse to perform a range of tasks.

In experiments, the scientists tested a topological acoustic synapse coupled with three digital neurons. (The emerging field of topological acoustics, applying previously unknown properties of sound waves, has led to new ways of manipulating sound—for instance, in circuits in which sound waves can flow with virtually no dissipation of energy.) “In a topological acoustic synapse, the acoustic wave interactions help transform and organize information before the final readout,” Yan says.

How Acoustic Synapses Adapt Faster Than Electronic Ones

When it came to classifying 150 flowers as belonging to one of three iris species, the new device outperformed a conventional computer chip-based neural network called a multilayer perceptron (MLP). The acoustic device—representing a single simulated synapse—achieved a final accuracy of 96.7 percent using only 39 parameters and reached its peak accuracy 20 percent faster than MLPs. To achieve comparable accuracy, the researchers note an MLP would require nine neurons and even more parameters.

All in all, the researchers estimated their new device consumes at most one-tenth the power of current state-of-the-art electronic neuromorphic hardware. “Future neuromorphic systems may combine physical wave dynamics with conventional computing to achieve more energy-efficient information processing,” Yan says.

In addition, the scientists noted their new device could mimic the activity of critical molecules known as neuromodulators. Neuromodulators such as dopamine or serotonin “can make a synapse more sensitive, less sensitive, faster, slower, or change how strongly it learns,” Yan says. “This flexibility helps the brain adapt to different conditions, such as attention, reward, stress, or learning state.”

A single biological synapse may be simultaneously influenced by as many as 10 neuromodulators. However, mimicking neuromodulation in conventional neuromorphic hardware is challenging, typically requiring dramatically more complex designs.

Yet the researchers found that with an acoustic synapse, simply adding an extra rod allowed the system to mimic a number of neuromodulatory processes—including rapid responses (such as dopamine effects on synaptic strength during learning) and slow, long-term responses (such as chronic stress).

“Neuromodulators let the brain use one circuit to perform different functions depending on the context,” Aimone says. “This is unlike now, where you have to have different neural networks for different tasks. So instead of an enormous neural network, you could have smaller neural networks that can use the equivalent of neuromodulators to adjust themselves for whatever’s going on. That’s really exciting.”

The researchers published their findings online 12 June in the journal Science Advances.

Musicians are accustomed to getting paid each time their creative work is used. Across vinyl/CD sales, streams, radio, cover versions, and those numerous niches like karaoke, there are agreements in place about what “use” means. Underlying this is a simple economic principle: The more something is used, the more money it makes.

Generative AI has complicated the definition of use. On the one hand, you could argue that the use of a piece of musical training data happens just once, at the point of training. On the other hand, creators would be right to complain that the creative essence of their work lives on in the structure of the model, used every time the model produces an output.

Now, companies like Sureel and SoundVerse are working to re-create the essential economic principle that motivates creativity in an era of AI. Such initiatives aim to turn the generative AI industry from one guilty of “the biggest act of copyright theft in history” into one that coexists harmoniously with hardworking artists.

Music Royalties for the AI era

Sureel, a startup Warner Music Group just acquired, has partnered with the Swedish copyright agency STIM to explore the potential for music creators to get paid when their music is used to train generative AI tools. Sureel’s software labels online media, such as a music file, with instructions determined by the owner. The instructions specify whether an AI company may use the media freely in training, limit its influence in any given training set, or avoid it altogether. The software then tracks how the AI company uses the media in training and sets licensing fees accordingly.

Meanwhile, the founders of the AI music company SoundVerse “[reject] one-time royalty buyouts as insufficient and [advocate] for ongoing participation of artists in the AI lifecycle,” they wrote in a 2025 white paper. They argue that each time a generative AI system produces an output, certain pieces of training data play a greater role than others. If the system outputs music resembling jazz, the jazz in the training set has arguably contributed more than, say, the folk music. You can therefore differentially reward each piece of training data for each output.

Sureel’s copresident Benji Rogers told me, “Attribution isn’t about re-creating the old economics. It’s about measuring, for the first time, the thing the old economics only approximated.”

Such influence attribution needs to do more than superficially measure how similar a training data point is to the AI output. The challenge is to attribute causality, or a relationship between the training data and the trained AI, Sureel CEO Tamay Aykut says.

Even if the AI industry achieved that, however, it might encourage people to create music designed to maximize training-data royalties. While all creative markets lead to new incentives (music streaming, for example, has driven songs to have shorter intros), the industry could do without another economic structure that is easily gamed, in which someone’s reverse-engineered pastiche diverts royalties away from original works of creative expression.

RELATED: Generative AI Has a Visual Plagiarism Problem

Inferring the influence of a particular piece of music on a generated piece of music, if a well-defined problem at all, may involve more advanced information theoretic principles, or modeling the actual historical role and impact of individual works. Aykut proposes that in carefully designed attribution systems, more unusual and unpolished musical works could even have more inherent value than radio standards.

Simon Gozzi, head of business development at STIM, says the company is in the process of seeing how Sureel’s attribution reports could underlie licensing agreements between musicians and AI companies. Could generative-AI attribution strategies not only sustain the economic logic that “popularity pays,” but also motivate musical experimentation and diversity? It’s a compelling concept when public sentiment rightly fears generative AI’s threat to cultural vibrancy, pushing power toward tech companies, deskilling creative workers, shrinking revenue in the creative sector, and filling the internet with slop. “Attribution is one of the few credible tools we have,” Rogers says.

There’s a window of opportunity to debate and establish approaches to paying for AI training data that serve a vibrant and sustainable creative sector.

The technical problem of training-data attribution is both complex and ill-defined. Just as a simplistic attribution strategy based on measuring similarity might motivate people to reverse-engineer the canonical works of a genre to capture royalties, a more complex attribution strategy based on some information theory of originality might be easily gamed or fail to reward human cultural production.

For creative workers, there’s good reason to fear that even with the best intentions, AI attribution will only compound the baroque and opaque arms races that they are already weary of navigating. Some voices within the music AI sector are also skeptical. Drew Silverstein, president of SourceAudio, says, “Attribution would seem to be the obvious answer, but it’s flawed in AI, so we have to look at other models.” He advocates simple negotiated agreements with an agreed or annually recurring price at the point of training.

Meanwhile, the copyright lawsuits that have dominated the generative AI revolution are beginning to give way to an increasing number of privately negotiated agreements, such as those between Universal, Warner, and major AI companies to work together on training models with copyright consent. Although little is certain, these agreements may have considerable influence over the industry norms that arise.

Right now, there’s a window of opportunity to debate and establish approaches that pay for AI training data while also sustaining a vibrant creative sector. Sophisticated engineering solutions will have a role to play, but they need to take into account the cultural complexity of the challenge, and enable fairness and transparency through good design.

Making AI Training Pay Off

It remains to be seen whether monolithic generative models such as Suno actually have as much credibility as first touted. In many creative applications of AI, there’s a renewed focus on smaller customized models that are tailored for specific human creative expressive needs such as IRCAM’s RAVE model or Jen’s Style Filters. Meanwhile, more mainstream “end user” creative applications may be shifting towards a focus on fan engagement. OpenAI’s sudden dropping of Sora, despite being in negotiations with Disney and Suno’s recent emphasis on building fan-engagement experiences that draw directly on the work of artists, following its deal with Universal, both point to teething troubles in the creative AI sector.

A move to smaller, more targeted models and applications would give more room for creator alliances. For example, collectives of musicians might band together to provide the training data for a smaller custom model, for which revenue splits might be egalitarian or based on other principles of fairness.

The same may possibly be true of hybrid model architectures and structured training regimes where different data sources are used at different points in the training process, as well as retrieval-augmented generation, which mixes context-specific information with training data to improve results. An approach that produces worse results but enables fairer or more transparent paths of attribution may be more successful if it brings creators on board with more lucrative royalty flows and even clear credits.

Also, no matter how sophisticated an attribution algorithm is, it will always be grounded in human decisions, ranging from the wise and the fair to the arbitrary and corrupt. Ask a music industry insider to explain how the percentage split between recording and songwriting royalties is determined, and you’re in for a long answer. At best, the machinery of training data attribution will enable open and informed discussion about what makes our creative and cultural sectors fair and vibrant. At worst, it will conceal already opaque private agreements in complex black boxes.

This is where national policies are vital. Attribution must be “multi-layered and auditable, open to expert and regulatory scrutiny,” Rogers says. Crafting such policies will take expertise from computer science, musicology, law, and economics. AI-competitive governments will be able to boost their cultural and creative sectors by supporting institutions that fulfil this purpose.

Even the most neoliberal economies look beyond markets to sustain cultural expression, whether through public arts funding or measures like local music quotas for radio. As the economic impact of generative AI in the creative sector takes form, taxation, redistribution, and active support of cultural infrastructures may still be the most effective way to support positive social outcomes. Taxing big AI and redistributing that revenue back to the creative workers that contributed to the industry’s wealth is, after all, another “AI attribution strategy.”

For decades, automakers enjoyed a luxury that had nothing to do with the softest leather or the smoothest engines. Their luxury was time, with some popular cars and trucks enduring for a decade or longer before they received a full redesign.

The clock is ticking faster now, thanks to China. BYD and other automakers there are speeding electric vehicles (EVs) and other models from drawing board to showrooms in two years or less.

General Motors is among the Western automakers striving to match that blistering pace, by harnessing AI and simulation to dramatically shorten development times. GM’s effort is being spearheaded by Sterling Anderson, the technologist and robotics guru who led development teams for Tesla’s Autopilot and the Model X before cofounding Aurora Innovation, the autonomous trucking company. GM lured Anderson last June as its chief product officer, offering a US $40 million package to guide the development of the automaker’s cars, autonomous models, batteries, software, and other tech.

How GM Is Accelerating Its Designs

In a recent video call, Anderson and Jason Fischer, GM’s executive director of virtual integration engineering, walked me through the company’s latest design processes. But first, Anderson offered a wide-lens view of how AI is transforming everything that came before.

Sterling Anderson, robotics guru and former Tesla executive, is pushing AI to accelerate GM’s design process.General Motors

Anderson sees design and human ingenuity falling into three main epochs, beginning with thousands of years of empirical design that saw creators largely mimicking nature, building and testing models, and advancing from there—slowly, expensively, and narrowly focused.

“Flight is a great example,” Anderson says. “Humans looked to birds and said, ‘Hey, those wings seem to work pretty well. Let’s come up with something like it.’”

The advent of virtual tools such as computer-aided design (CAD) and computational fluid dynamics in the 1950s kicked off a second age, he says. Developers had better ways of doing work, but they remained siloed in an inefficient, pass-the-baton process. “Designers still had to toss something over the wall to other engineers, who ultimately had to build that empirical asset anyway,” Anderson says. In automobiles, that meant building prototype vehicles first and then integrating and assessing myriad functions, many of which were developed separately: electrical systems, thermal controls, safety, ride and handling, and so on.

Today’s third epoch is characterized by AI and simulation that can collapse those functions into a single virtual development tool, Anderson says. In roughly one minute, a structural engineer can see how a design change might affect a finished vehicle, as opposed to the 15 hours it used to take. The result, he says, “is a dramatically accelerated product development process at GM.”

GM is applying this approach to self-driving cars, LMR batteries, Cadillac’s high-profile Formula 1 racing program, military defense systems, and tech for Lunar Outpost’s Pegasus rover, part of NASA’s Artemis mission to land astronauts on the moon in 2028.

Fischer says the company’s proprietary environment allows engineers to simultaneously develop and optimize hardware and software, well before the physical prototype stage.

A simulated Cadillac performs an emergency avoidance maneuver, with graphs tracking vehicle functions such as brake pressure and steering wheel angle.General Motors

In an onscreen demonstration, Fischer runs a digitally rendered Cadillac Escalade IQ through the Consumer Reports avoidance maneuver, which the publication uses to assess a car’s evasive skills. The tricky double lane change is a serious test of the electric SUV’s handling and stability under duress. In the past, physical testing could begin only after an array of systems had been separately developed and stitched together, including the chassis, powertrain, steering, brakes, suspension, sensors, and controls. Engineers would spend months testing and calibrating prototypes in proving grounds and on real-world roads.

Now, GM can run detailed, physics-based models of designs across thousands of simulated scenarios—snow and rain, varying road conditions, different suspension setups. “We can do full, virtual calibrations prior to a vehicle ever being built,” Fischer says. “We get a system that performs well not just in ideal conditions, but one that’s been hardened against the real world.”

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This approach halved the development time of the electric GMC Hummer, which went from initial designs to showroom in two years, versus a more typical four-to-five-year product cycle. GM’s goal is to get a full range of vehicle and tech programs onto that lightning-fast development track.

“We’re not there yet, but give us a minute,” Anderson says.

Front-end crash simulations have also been accelerated. In the past, a “heavy computational method” required 15 hours of computing to complete, Fischer says. Using an AI method based on probabilities, the compute time has been cut to less than one minute. That frees engineers to focus on additional scenarios “that would be difficult, limited, or frankly impractical to re-create with physical vehicles alone,” Fischer says. “Engineers are finding weak spots earlier, and fixing them earlier, to arrive at physical testing with a stronger, more refined vehicle.”

“Driver in the loop” simulations add human variables to those of vehicles, plugging in the personas of, say, a Boston man driving in January or a Phoenix woman driving in desert heat. Such simulations are being used to assess people’s responses to heating and cooling in a cabin, as well as in a more hostile environment: the surface of the moon.

In contrast to NASA’s original lunar explorations in the 1960s and ’70s, GM can realistically simulate what an astronaut or vehicle will experience after taking that one small step. Fischer says work on its next-gen NASA lunar rover is proving the vast bandwidth of virtualization and simulation tools.

“We can alter gravity by adjusting physics in the software,” he says. “Our engineers in a room in Michigan can simulate real-world driving conditions to develop tires for [the lunar] environment.”

Anderson, who holds a Ph.D. in mechanical engineering from MIT, where he focused on robotics, notes how these tools are critical for autonomous vehicle development. “We can simulate 100 days of driving in a day and are approaching roughly 2 million simulation runs per week, which helps us probe edge cases that would be dangerous or impractical to reproduce physically,” he says. “This doesn’t replace road testing, but it makes every real-world mile more valuable and every release decision more informed.”

The AI design for the Corvette’s hatch support brackets (in red) is stiffer, lighter, and more durable than the original. General Motors

AI is even being deployed in the design of a vehicle as hallowed—and profitable—as the Chevrolet Corvette. Generative physics-based design gets credit for a strut bracket that supports the Corvette’s enormous composite hatch lid, the better to flaunt the sports car’s V-8 engine. For the car’s optional hatch brackets, the unnamed AI designer went back to nature, creating a shape that suggests a tree root and branches and is lighter, stiffer, and more durable than the original.

“This is really becoming the new norm for General Motors,” Fischer says.

This article is part of our exclusive IEEE Journal Watch series in partnership with IEEE Xplore.

As robots advance in terms of dexterity and other physical capabilities, it becomes more likely that humans may find themselves working alongside them. If that happens, how will robots’ emotional capabilities need to advance for them to successfully work with people?

In a recent study, researchers trained collaborative robots to read human emotions by not only accounting for facial expressions, but also contextual factors in the interactions as well. Through experiments with 40 volunteers, the researchers then evaluated how a robot’s ability to read human emotions and adjust its behavior in turn impacted a human’s perception of the robot and its capabilities as the two collaborated on tasks. The results—which show that the emotional capabilities of robots only go so far with humans—were published 18 May in IEEE Robotics and Automation Letters.

Seung Chan Hong led the study as part of his undergraduate thesis while studying at Monash University, in Melbourne, Australia. He notes that, while there has been a lot of hype in the advancing physical abilities of robots, this is only one piece of the puzzle. “We need to also innovate when it comes to them actually interacting with humans, not just their physical capabilities,” he says.

This prompted him to dig deeper into the emotional aspects of human-robot interactions. First, Hong and his co-authors decided to train a robot to read human emotions using a vision language model (VLM), which is similar to large language models (LLMs) such as ChatGPT, but which can also take visual inputs.

Training VLMs for Human Emotion Recognition

To evaluate their VLM, which used Gemini 2.5, the researchers had volunteers watch videos of robots handing over objects to humans—with varying degrees of success—and describe the emotions the humans were expressing. Importantly, the volunteers labeling these videos were able to take into account more context in these interactions, rather than reporting solely on the facial expressions of the humans in the video. For example, a person pausing to think with a furrowed brow may simply be concentrating on their task at hand and not necessarily be angry. Contextual factors such as drumming their fingers, pursing their lips, or other behaviors can point to the real cause of a person’s furrowed brow.

The researchers then compared their VLM to a conventional AI system that relies on standard facial analysis and object tracking that is used in human-robot interactions. They found that the VLM outperformed the traditional approach. On a scale from 0 (no similarity in meaning to the emotion identified by the human volunteers) to 1 (a perfect match in meaning), the conventional AI system achieved a score of 0.77. In comparison, the VLM achieved a score of 0.86.

Hong says, “I think [the VLM] was able to align with what human observers were seeing a lot better, because it wasn’t just looking at the person’s face for a brief amount of time, but seeing the whole scene—where the person was and what they were doing, and how they were interacting with the robot.”

In a second experiment, the research team asked 40 volunteers to interact with a robot using their VLM—but purposefully programmed the robot to make an error. The robot then had to offer either an emotionally adaptive apology that accounted for the human’s perceived response to the mistake or a pre-scripted spoken apology.

Participants overwhelmingly preferred the emotionally adaptive response, with 31 out of 40 people favoring this approach over a boilerplate apology.

However, their survey responses underscored how this emotional adaptivity was far less important than the robot’s functionality. After collaborating with a robot that failed in its task, many participants ranked their trust in the robot as lower, regardless of how it apologized for its mistake. “A personalized apology acts as a social lubricant, but it cannot repair the trust lost by the robot failing its physical task,” Hong says.

Interestingly, the VLM classified the emotions of its human partners similarly to human volunteers who observed an interaction from a third-party perspective. But when the VLM’s assessments were measured against humans’ self-reported emotions during the second experiment—the most accurate descriptions of their true emotions—its ability to accurately predict emotions dropped significantly.

“While the VLM is a good observer of outward social cues, it isn’t a mind reader,” Hong says. “It matched third-person human observers well, but it didn’t always align with the users‘ internal, self-reported feelings.”

Together, these results show that robots are not perfect at reading human emotions. So while people might appreciate their efforts, they still ultimately will want competent co-workers.

This story was updated on 15 June 2026 to correct where the research was conducted and clarify that the researchers evaluated the performance of a pre-trained model.

For more than a decade, artificial intelligence has been touted as a way to dramatically accelerate drug discovery. Yet despite billions of dollars in investment, relatively few AI-designed medicines have made it to patients. That’s partially because the timelines for careful drug testing can’t be easily compressed—and partially because drug development is just really hard.

Isomorphic Labs, the Google DeepMind spin-off that’s building on DeepMind’s Nobel Prize-winning work on protein structure prediction, may be making the most progress. The company has signed major drug-discovery partnerships with Novartis and Eli Lilly and recently raised US $2.1 billion in funding. In February, it published a technical report describing its new Isomorphic Drug Design Engine, a system created to discover the “pockets” on proteins where drugs can bind and in general to predict how proteins and drug molecules interact.

IEEE Spectrum spoke with Adrian Stecuła, a group leader in the machine learning organization at Isomorphic Labs, about how close AI may be to becoming a practical tool for designing new medicines.

Going Beyond AlphaFold

AlphaFold2 and AlphaFold3 were massive leaps forward for computational biology. Why weren’t those models sufficient for actually designing drugs?

Adrian Stecuła: AlphaFold2 was eventually recognized with the Nobel Prize, because it arguably solved the problem of protein folding. But proteins don’t exist in a vacuum, right? They interact with a wide variety of other types of biomolecules, which involves nucleic acids, small molecule ligands, ions, and other proteins. AlphaFold3 introduced a way to model the rest of these cellular biomolecules as part of a single framework. So all of a sudden, we have a single model that can model all of these interactions all at the same time.

That said, in the years since the AF3 release, multiple groups have evaluated it along the axis of pocket novelty. And you could see that as the pocket distance grows away from the training set, the model performance decreases. So if you define the success as “how well did the model actually fold this particular ligand with this particular protein,” as those systems become more novel, you can see a decline in performance.

But for drug discovery, ideally we do want to pursue novel mechanisms of action, which might involve targeting a never-before-observed pocket. And so it is absolutely important for us to have our models generalize to these regions that are distant from training.

How does the Isomorphic Drug Design Engine (IsoDDE) address these limitations, and what exactly is it predicting?

Stecuła: It takes a lot more than just structure prediction to create a molecule that will ultimately become a drug. You don’t just need to predict where the ligand binds with the protein, but also potentially how it binds, how tightly it binds, and a plethora of other properties about the ligand and how the ligand interacts with the rest of the proteins in the body.

IsoDDE is a unified computational system that lends itself to a number of different endpoints. As part of the technical report about IsoDDE, we have described three of those endpoints, which are structure prediction, pocket identification, and binding affinity prediction. [Editor’s note: Binding affinity measures how strongly a molecule binds to a target protein.]

Finding Hidden Protein Pockets

In your technical report about IsoDDE, you highlighted a key example involving a protein called cereblon and its “cryptic pocket.” First, can you tell readers about this protein and what a cryptic pocket is?

Stecuła: Cereblon is one of the most important and well-studied proteins in the targeted protein degradation pathway. It’s part of the mechanism that’s responsible for degrading proteins in the cell. [Editor’s note: Some drugs use cereblon to mark disease-causing proteins for destruction by the cell.]

And cryptic pockets are pockets on protein surfaces that are non-obvious, in that in the [unbound] state of the protein, meaning that if you were to look at the protein by itself, it would not have a cavity there. This pocket only opens upon the binding of just the right ligand. So you can think of it as: You need the perfect key to unlock this lock.

How did you use recently published findings about cereblon to validate IsoDDE?

Stecuła: In January of this year a Nature paper published a completely novel, never-before-observed cryptic pocket on the surface of this protein. First we asked the question: Can IsoDDE find this pocket just using the protein sequence as input? And we were able to perfectly predict the location of this cryptic pocket. Again, note that this pocket had never been disclosed before.

The second question was: Can IsoDDE accurately predict how the ligands bind to the protein? Are we able to recapitulate the crystal structure shown in the Nature paper? And the model was able to place both the orthosteric ligand [at the known binding site] as well as the allosteric ligand [at the new, cryptic pocket] in exactly the perfect locations.

Most drugs today are small molecules—relatively simple compounds that bind to proteins. Does IsoDDE expand the toolkit for tackling diseases?

Stecuła: I think many of the hopes for machine learning for drug design revolve around making more protein targets tractable. There are already many diseases that have known associated proteins. So we know that if only we could target a particular protein, we would have a chance at helping the patient population suffering from a particular disease.

But in many of those instances, the protein that is associated with that disease doesn’t have a pocket or mechanism that can be easily drugged. IsoDDE is enabling us to find those mechanisms. Further, these methods generalize not just to small molecules but also to antibodies, molecular glues, and peptides. It’s not just a breakthrough that will impact small molecule design, but these other therapeutic modalities will also benefit from this.

There is a lot of hype around AI in drug discovery. What do people commonly misunderstand about where the field is right now?

Stecuła: Perhaps the misunderstanding is that just because we are able to accurately model structure, that drug discovery is a solved problem. It does take, we believe, a unified system such as IsoDDE with a plethora of other endpoints to really model these systems. We will continue to improve our performance on the endpoints that we have disclosed as part of the IsoDDE report, and will also continue to push on the endpoints that we have not yet disclosed.

Do you imagine more of the drug discovery process becoming automated, with AI systems generating hypotheses, testing hypotheses, and analyzing results?

OpenAI’s fourth large language model (LLM), GPT-4, took an estimated 50 gigawatt-hours to train, or the equivalent of 5,000 American homes’ yearly power consumption. That was in 2023. Since then, the computational resources used to train frontier LLMs have only increased, though direct power usage numbers are hard to come by.

Now, a research group at the University of Twente in the Netherlands has shown that you can save up to 14 percent of the energy used in LLM training without sacrificing speed by cleverly adjusting the clock frequency of the GPU during computation. Jeffrey Spaan, Ph.D. candidate at University of Twente and lead author on the article, presented the results at the Computing Frontiers conference in Catania, Sicily, last month.

“My research is about finding computing waste,” Spaan says. “It’s similar to underutilization of the hardware, but instead of optimizing the software for the hardware, we try to optimize the hardware for the software.”

Making the GPU tick

Spaan and his collaborators accomplished this by using a technique known as dynamic voltage and frequency scaling (DVFS). Every chip—including the GPUs commonly used for training frontier models—uses at least one clock to orchestrate computations. Each operation in the chip is triggered by a clock pulse. The frequency with which that clock ticks controls how fast the chip operates and how much power it draws.

Modern GPUs have two clocks, one for the computational core and one for the memory. When the core is hard at work crunching numbers, the clock frequency is kept high to ensure speedy calculation. However, with DVFS, the memory clock can slow down in that time, allowing for less power draw. In principle, it’s possible to just turn off the memory part of the chip, but GPUs designs don’t enable software control for that off switch, and it would take too long to turn back on mid-calculation anyway. Similarly, when the core is waiting for data to be loaded from memory, the core clocking frequency can be slowed to a crawl while the memory clock frequency ramps up.

DVFS has been a well-known technique that goes back to at least the 1990s. But Spaan says other researchers haven’t been able to usefully apply it to LLM training because their methods either slowed down calculations too much or were not fine-grained enough to improve energy usage.

Previous DVFS attempts adjusted the frequency at each iteration of the training process. In LLM training, each iteration consists of two parts: the forward pass, in which data is run forward through the layers of the model with the weights as they are; and backpropagation, in which the weights are adjusted layer by layer based on the results of the forward pass. So prior work kept one value of the frequency for the forward pass and adjusted to another for backpropagation.

Spaan and coworkers tuned the clock frequencies on a shorter timescale. GPU workloads are broken down into tiny computational nuggets known as kernels. For example, a single vector-vector multiplication can make up a single kernel. The kernels are fed to the GPU to be processed many times in parallel. In Spaan’s implementation, the computation of a single layer of a deep neural network is broken up into approximately 40 kernels. By adjusting the clocking frequencies on a per-kernel level, the team was able to find much greater energy savings.

The GPU also does DVFS automatically when the chip’s internal systems detect higher or lower demand, Spaan notes. “Some people might therefore think: We’ll just let the GPU handle it,” he says. “However, because the GPU doesn’t have the foresight we have of what kernels will run, it has to work with an on-the-fly best-effort guess and can therefore never attain the same savings.” That’s where the manual adjustments come in.

Less energy, same time

The team performed their experiment by training GPT-3-XL, a 1.3 billion parameter model, on an Nvidia RTX 3080 Ti GPU. To save time, they focused on training a single layer of the model. In this setting, they found a set of frequency adjustments that gave them 14 percent energy savings while slowing the training time by only 0.6 percent. Performance of the model depends on both computing speed and energy usage.

There is one challenge: Ramping down the clock frequency is much faster than turning a core off and on, but it’s still not instantaneous. In their experiment, the researchers evaluated one kernel at a time, not taking into account the frequency switching speed. So 14 percent energy savings is a best-case scenario. How much of an issue it would be in practice, Spaan says, depends heavily on the GPU being used. Newer hardware, like the Blackwell GPUs, have much faster switching speeds than older versions and should be able to harness the full energy savings.

Now, the team is developing a tool that would be able to implement optimal frequency scaling automatically for a particular workload. Spaan hopes their method will be attractive enough to industry leaders to merit adoption. “We optimize for saving energy without losing performance,” Spaan says. “In the real world, performance is the holy grail.”

Tracking how fast glaciers are shrinking is crucial for measuring the pace of climate change and projecting future sea level rises. This is normally a painstaking manual job, but a new approach that enables AI to analyze satellite images of glaciers anywhere in the world could help automate the monitoring process.

Glaciers that flow directly into the ocean play a crucial role in the earth’s climate, but global warming is making them retreat ever faster. This can have severe knock-on effects as ice that breaks away from “calving fronts”—the ends of glaciers where icebergs shear off into the water—dumps massive amounts of freshwater into the sea, which can alter ocean currents and cause sea levels to rise. Bright white glaciers also reflect a lot of sunlight. When they shrink, they expose dark seawater that absorbs heat from the sun.

All of this means that tracking glacier loss is critical for understanding how both local and global climate conditions will change over time. But the number of glaciers that need to be monitored around the world far outstrips the capacity of human analysts. There is hope that AI-based image analysis could help plug the gap, but previous models have performed poorly on regions not included in their training data. This severely limits the applicability of the approach, given how difficult it is to collect manually-labeled images.

Now, a paper accepted to the IEEE International Conference on Image Processing (ICIP) shows that a leading deep learning model for tracing glacier calving fronts can be adapted to new locations with minimal additional data. Researchers from the Friedrich-Alexander University of Erlangen–Nuremberg (FAU), in Germany, showed that the model’s error—the average distance between the modeled boundary and the real one—was cut from more than a kilometer to less than 70 meters by providing three pieces of information: one hand-labeled image per glacier, unlabeled summer reference images, and a map of the underlying rock.

In related research, some of the paper’s authors have already put the approach to work, using it to extract monthly calving front positions for all 145 glaciers in Norway’s Svalbard archipelago from 2015 to 2024. The team now hopes to extend the approach to another 1,500 glaciers in the Arctic.

“It’s about understanding glaciers better and how they react to changes in the climate,” says Nora Gourmelon, a Ph.D. student at FAU and co-lead author of the ICIP paper. “When you know about the past, then you will also hopefully be better able to understand how they will change in the future.”

Reducing the margin of error

Historically, delineating calving fronts has required students and researchers to pour over satellite radar images to manually trace the boundary between glaciers and the ocean, says Gourmelon. The process is time-consuming though, so numerous research groups have been experimenting with using computer vision models to automate the process.

In 2023, Gourmelon and her colleagues produced a dataset of 681 radar images of seven glaciers in Antarctica, Greenland, and Alaska, with manually annotated calving fronts to help train and benchmark new models. But when they took a state-of-the-art deep learning model trained on this dataset and applied it to previously unseen glaciers in Svalbard, they found it was off by an average of 1,131.6 m.

Gathering enough manually annotated data to retrain a model on every new glacier you want to analyze would clearly be infeasible, so the authors tried to find a more efficient way to boost performance. They produced one manually annotated calving front image for all 145 glaciers in Svalbard and combined this with several more raw satellite images of each glacier to create a new training set of 5,539 images. When they retrained the model on both this new data and the original benchmark data, the error dropped to 445.3 m.

They then developed two novel strategies to further improve accuracy. For both humans and AI, it can be tricky to distinguish the boundary of a glacier from ice melange—the mush of floating icebergs, sea ice, and snow that can accumulate at the calving front. So when the researchers uploaded a series of images of a glacier for the model to annotate, they included three images from the summer, when the melange is not present and the boundary of the glacier is clear. These acted as a reference point for the model and pushed the error down to 204.6 m.

As a final step, the researchers also provided the model with a static map of the rock underlying each glacier, derived from Open Street Map data that outlines the coast of Svalbard. This slashed the error to just 103.6 m. By running an ensemble of five different versions of their model and averaging their outputs, the researchers were able to get their final error down to just 68.7 m. While that may still sound fairly imprecise, Gourmelon says it’s comparable to manual annotation error rates. “Humans themselves are not really consistent in labeling, especially when there’s ice melange or when the resolution of the satellite image is not that good,” she says.

Automating glacier modeling

While the approach still requires some legwork, it can dramatically speed up the analysis of new regions. Most recent research of this kind has been done on annual or decadal timescales, says Dakota Pyles, a Ph.D. student at FAU, who led the second study that mapped nine years worth of glacier dynamics in Svalbard. In contrast to the lower frequency tracking, Pyles was able to generate monthly calving fronts for every glacier in that study—a total of more than 203,294 annotations—providing a much finer-grained view of how ice dynamics are changing on the archipelago.

“My project would not be possible at the scale that we’re going for if we didn’t have the model,” Pyles says. “So that is a great benefit for us and for advancing the field of glaciology [in general].”

In the long run, the approach could make it possible to partially automate the monitoring of glaciers around the world over extended periods of time, Gourmelon says. “We still need some labeled images from the specific region or satellite that you want to use for monitoring to train it on first, but then it can be used,” she says. “If how the image is captured and where you’re looking at stays consistent, then no recalibration would be necessary.”

At Computex 2026, an annual computer trade show held in Taipei, Taiwan, Nvidia made a long anticipated announcement—a version of the company’s Blackwell GB10 superchip for Windows PCs, called RTX Spark. Originally rumored to launch in 2025, it was finally introduced at this year’s show.

It came with full support from Microsoft, which announced two new devices powered by RTX Spark: the Surface Laptop Ultra and the Surface RTX Spark Dev Box. Asus, Dell, Lenovo, HP, and MSI also announced Windows PCs with RTX Spark.

If this is triggering déjà vu, that’s for good reason. In June 2024, Qualcomm and Microsoft partnered to launch AI-focused Copilot+ PCs. Qualcomm’s Arm-based chips provided an alternative to x86-based chips from AMD and Intel used across dozens of budget and mid-range Windows laptops. It was met with mixed commercial success, however, and Intel remains the dominant supplier of chips for Windows laptops. But that doesn’t mean RTX Spark will follow the same path, as Nvidia’s involvement is an important part of the equation.

“Nvidia just has more clout and more industry weight to push and make things happen that Qualcomm couldn’t do early on, and that even Microsoft struggled with,” says Ryan Shrout, president at Signal65, a third-party testing firm. “They can get game developers on board and get software developers in the emerging AI space to pay attention.”

What is RTX Spark?

At its core, RTX Spark is an iteration of the hardware found in the DGX Spark mini-workstation, which was released in late 2025. Officially badged N1X, the silicon is Nvidia’s Blackwell GB10 “superchip,” a system-on-a-chip with 20 Arm CPU cores; 6,144 GPU cores; and support for up to 128 gigabytes of LPDDR5X memory.

There are some small differences between the mini-workstation and PC system, and the most significant is power consumption. The DGX Spark was designed for GB10 to operate with a power consumption up to 140 watts without overheating. RTX Spark laptops are likely to use less power, which may lower performance, though the details will depend on each PC maker’s particular implementation and remain to be seen.

RTX Spark will also include a neural processing unit (NPU) that qualifies the system for Microsoft’s Copilot+ certification. The NPU is used for some background AI features, like Windows Recall. However, the GPU will remain in the driver’s seat for active AI tasks, including large language models (LLMs) and image generation.

Though RTX Spark laptops took the spotlight, the news is also relevant to desktop workstations. Currently, DGX Spark ships with a custom version of Linux called DGX OS, not Windows. Nvidia says RTX Spark desktops with Windows are coming in the third quarter of 2026. Windows is also coming to Nvidia’s DGX Station, the full-sized desktop iteration of Nvidia’s hardware.

The launch of RTX Spark is, of course, in part an AI play, and that is taking the lion’s share of attention. But Anshel Sag, principal analyst at Moor Insights & Strategy, thinks Spark is just as relevant for professional work and gaming. “I think the AI play is mostly to appease investors,” he says. “Creators and gamers are also excited about RTX Spark, and someone like me who does all three is even more excited, because having a machine that can do all three well has been a challenge.”

Nvidia’s advantage may lie in software

Though Nvidia refers to the GB10 as a “superchip,” it’s similar to other high-performance system-on-a-chip designs, such as Apple’s M-series silicon and AMD’s Ryzen AI Max. All three include a CPU, GPU, and NPU. All three support large amounts of DRAM. And all three have a unified memory architecture (meaning the system memory is a shared resource accessible to the CPU, GPU, and NPU).

The existing DGX Spark also provides a baseline for performance expectations. RTX Spark will likely deliver GPU performance similar to an RTX 5070 mobile GPU which, if correct, would put it ahead of Apple and AMD’s competing systems. On the other hand, GB10’s CPU cores aren’t as quick as the CPU cores found in leading competitors.

Nvidia’s biggest edge might stem not from hardware performance, but from software. The company’s GPUs are essentially the industry standard across gaming and professional work, with estimates placing Nvidia’s GPU market share above 90 percent. That in turn has made Nvidia the target for most software that benefits from a GPU.

“Nobody doubts that Nvidia is the leader in GPU capability and the software stack around it,” says Shrout. Sag agrees, explaining that Nvidia has the advantage of “extremely mature drivers.”

Microsoft touts AI, but Windows on Arm remains a question

Nvidia announced RTX Spark was in lockstep with Microsoft, which held its Build developer conference in San Francisco while Computex was taking place across the Pacific in Taipei.

Repeating the Copilot+ PC launch, Microsoft’s vision of Windows on the RTX Spark leans heavily on AI. But unlike Copilot+ PCs—which used the NPU to accelerate AI features integrated into the Windows user experience, such as quickly recalling anything you’ve opened or translating live video calls—the pitch for Windows running on RTX Spark seems more focused on using the Spark’s GPU to accelerate LLMs.

Microsoft announced an “early preview” of Windows SDK called Microsoft Execution Containers (MXC), which sandboxes AI agents, allowing them to work autonomously while isolating them from functions the user doesn’t want the agent to access.

Still, the real test for both Nvidia and Microsoft remains the same challenge Microsoft and Qualcomm faced: establishing Windows on Arm PCs as an alternative to Windows PCs powered by x86 chips from Intel and AMD. Whether RTX Spark will succeed in this remains to be seen.

“Even with all of the talk from Nvidia and Microsoft about the future of the PC and revolutionizing the PC, everybody understands that it needs to be a great general-purpose PC first,” says Shrout.

Quantum computers promise to one day solve problems beyond the most powerful supercomputers imaginable. But it’s often underappreciated how much classical computing it takes just to operate these machines. As qubit counts rise, innovations in this supporting infrastructure will be essential if they’re to live up to their promise.

To prepare for the scale of quantum computers the industry is working toward, many companies are also gearing up the classical hardware, and software, required to support them. In April, Nvidia announced new AI-based software to accelerate the classical tasks that enable quantum computers. Sydney-based quantum software company Q-CTRL has developed an automatic calibration algorithm for quantum computers, and is now leveraging Nvidia’s agent-based system. Other companies, including IBM Quantum, Cambridge, England–based Riverlane, which develops quantum-error correction, and Google Quantum AI, are developing similar tools.

The Role of Classical in Quantum

Digital computer chips are marvels of engineering, operating flawlessly out of the box and capable of trillions of operations without error. The quantum bits, or qubits, at the heart of a quantum computer, by contrast, are temperamental and unreliable, requiring regular calibration and complex error-correcting schemes to keep them on track.

Calibration and error-correction are fundamentally classical, not quantum, problems, and they require dedicated classical hardware to solve. As quantum computers get bigger, the scale of those resources will need to rise in lockstep. That means that for the foreseeable future, quantum computers are going to be hybrid devices with a healthy dose of classical computing on the side.

“The cheapest and fastest way to execute most computer programs is to run them on a classical computer—even if a quantum computer is available,” says Adam Zalcman, a quantum software engineer at Google Quantum AI. “This is true of most of the information processing involved in running a quantum computer itself.... Therefore, I expect that every practical and efficient quantum-computer architecture will incorporate fast classical devices.”

Tuning Quantum Hardware

While the transistor has cemented its place as the foundational component of classical chips, the qubits at the heart of a quantum computer come in many flavors—superconducting circuits, trapped ions, neutral atoms, even individual photons. Using them for computation requires a painstaking calibration process to turn the “bare metal” of the underlying hardware into a qubit that can be controlled to run quantum circuits, says Jay Guilmart, lead product manager at Q-CTRL.

Calibration has two stages. The first, known as “bring up,” determines the frequency at which each qubit resonates, how long it holds its quantum state, its sensitivity to control pulses, and the strength of its interactions with neighboring qubits. All of these factors determine its error propensity and response to control signals.

Done by hand, the process still requires someone with a Ph.D. and can take days or even weeks, says Guilmart. This isn’t a scalable solution and so there’s a growing drive to automate the process. This is challenging because every step relies on results from the previous step. So rather than relying on a predefined script, Q-CTRL has therefore built intelligent calibration software that examines the result of each measurement, diagnoses failures, and adjusts the approach before retrying.

“After each step, we analyze that data and we say, are we okay to proceed to the next step? Do we have to go back to the previous step? Do we have to re-recreate this step?” says Guilmart.

Calibration is also not a one-and-done process: key parameters drift over time, gradually degrading performance. Q-CTRL’s software performs “runtime recalibration” to nudge things back into place, but there’s a limit to how much on-the-fly adjustment is practical.

“If I’m running a recalibration, I’m not running a circuit,” he says. “Even though I’m maintaining some high system state and high fidelities, if it takes all of my uptime it’s worthless.”

Decoding Errors in Real Time

Even a well-calibrated quantum computer remains fault-prone, which is why companies are investing heavily in quantum error correction (QEC). This typically involves encoding quantum information across large numbers of physical qubits in their shared state—a “logical qubit“—so that errors in individual qubits can be detected and compensated for without destroying the encoded information.

Because measuring a qubit directly collapses its quantum state, errors are detected via parity checks, which query whether pairs of qubits share the same state. This produces a series of measurements known as a “syndrome,” which classical algorithms called decoders analyze to locate errors.

The process must happen extremely quickly. While many errors can be logged and corrected mathematically after an operation, some must be fixed immediately before the algorithm can proceed. Superconducting and silicon spin qubits can hold their quantum states only for microseconds or milliseconds, so errors must be decoded and corrected within that window.

These tight requirements mean decoders typically run on specialized silicon like field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs) optimized for speed, says Jerry Chow, CTO of quantum-centric supercomputing at IBM. “You need to be able to keep up and you need to be able to effectively decode on the fly,” he says. “The best way to do that is through very tightly integrated FPGA or ASIC decoder capabilities.”

To AI or Not to AI

There is growing interest in using AI to simplify quantum hardware control. In April, Nvidia released two models targeting calibration and decoding. The first uses a vision-language model to analyze calibration-measurement outputs—typically plotted as graphs—and passes that evaluation to an AI agent that decides how to tweak the processor. The second uses a convolutional neural network to identify the simpler, localized errors that make up the bulk of faults. More complex errors are passed to a traditional algorithmic decoder, but the first pass reduces computational load enough to deliver a 2x speedup.

The attraction of AI for decoding, says Sam Stanwyck, director of quantum product at Nvidia, is that while models are time-consuming to train, they are extremely fast at inference—and thanks to parallelization across many chips, that speed holds even as qubit counts grow.

But offloading to a GPU still introduces significant latency, says Marco Ghibaudi, vice president of engineering at Riverlane. “You can have a really fat pipe, but it’s really long,” he says. “Our job [approach] has always been to try to remove as many unnecessary steps and shorten the pipe, and then make every section of the pipe as fast as possible.”

IBM’s Chow agrees that GPU latency currently makes them infeasible for real-time decoding. He’s also cautious about AI for calibration, given its computational expense. The approach holds promise for understanding the physics of novel architectures or new kinds of circuits. But for well-characterized devices where you’re simply looking for small deviations, simpler physics-informed techniques can be considerably cheaper.

The two approaches aren’t mutually exclusive, however, says Google’s Zalcman. Neural networks excel at discovering hidden patterns in syndrome data that help identify complex errors algorithmic decoders sometimes miss. Google is therefore developing a hardware architecture that can incorporate both traditional and AI-based decoders, including its AlphaQubit 2 model.

In the long run, Andi Gu, a Harvard Ph.D. student working on AI decoders, thinks “the bitter lesson” will come for decoding. This refers to AI pioneer Richard Sutton’s argument that general-purpose learning methods consistently outperform handcrafted algorithms over time. “If you make the model large enough and you throw enough training data at it, it will learn to capture the hidden correlations better than any other handwritten algorithm,” says Gu.

Latency remains a barrier, but his group is researching ways to make AI decoders more efficient and smaller so that they can fit on an FPGA, cutting response times. This can degrade accuracy though, so finding the right balance is still a work in progress.

Regardless of which approach wins out, one thing is certain—future quantum computers will require massive classical support. Decoding is a continuous, computationally expensive process whatever technique you use, says Gu, so you will need a “healthy chunk” of classical hardware dedicated to that task.

Calibration compute overheads will similarly “blow up” as devices scale to thousands or millions of qubits, says Q-CTRL’s Guilmart. Current techniques are unlikely to scale, he adds, so new approaches will be needed. “We’re going to have to rearchitect and do things differently when we get to even 1,000 qubits,” he says. “So no one’s winning the battle today.”

New graduates’ careers are unfolding in an era when AI is not optional. The most successful engineers treat artificial intelligence as leverage, not competition.

Here are seven tips to help keep young professionals in demand no matter how quickly the field’s tools evolve.

1. Master the fundamentals first. AI tools can help you code, but you still need strong fundamentals in:

• Data structures and algorithms for problem-solving.
• Operating systems, databases, and networking for system-level understanding.
• Core programming languages such as C++, Java, and Python.

AI can autocomplete syntax, but if you don’t understand how things work under the hood, you’re likely to struggle to debug or optimize.

2. Learn how to work with AI, not against it. The best engineers will not try to out-code AI. Instead, they will learn to:

• Write clear prompts to generate better code snippets.
• Review and debug AI-generated code for accuracy, performance, and security.
• Use AI for productivity boosts while still exercising judgment.

Think of AI as a teammate. The real skill is knowing when to trust it and when not to.

3. Build projects that showcase end-to-end thinking. Employers increasingly look for engineers who can design and build systems, not just solve problems. Create projects that show you can:

• Define requirements clearly.
• Use AI tools responsibly within the workflow.
• Deliver a product that scales and is maintainable.

4. Sharpen your system design skills early. Even junior engineers are now asked questions about basic system design with AI. Expect to explain to prospective employers:

• How you would responsibly integrate AI into a system.
• How to design fallbacks when AI fails.
• How to ensure scalability and reliability.

5. Develop strong communication skills. Today’s engineers don’t just code in isolation. You will be expected to:

• Explain design choices to teammates and stakeholders.
• Document decisions clearly.
• Collaborate effectively in cross-functional teams.

This is one area where AI cannot replace you. Clear communication is a career accelerant.

6. Stay curious and keep learning. The tech industry moves fast, and AI is accelerating that pace. Cultivate habits such as:

• Following industry news, blogs, and open-source projects.
• Experimenting with new AI tools, frameworks, and libraries.
• Engaging in communities such as GitHub, IEEE Collabratec, LinkedIn, and Medium.

Employers value engineers who keep themselves sharp and relevant.

7. Think beyond coding. AI will increasingly handle routine coding tasks. The differentiators for you will be:

• Problem-framing: Can you take a vague idea and turn it into a solution?
• Architectural judgment: Can you design systems that scale and last?
• Ethical awareness: Can you spot risks in AI use and address them responsibly?

For more career advice, subscribe to the IEEE Spectrum Career Alert Newsletter. The biweekly newsletter features the latest information on jobs, education, management, and the engineering workplace.

As AI systems become more capable, a lot of resources and effort are being put toward measuring their abilities. Researchers look at technical evaluation metrics, subject AIs to reasoning tests, track their throughput, and much more. But there’s one key metric that often gets overlooked, and it’s arguably the most important of all: What is AI doing to humans?

Imran Khan leads psychosocial evaluation of AI at the nonprofit Center for Humane Technology. In a recent essay published on the organization’s Substack, Khan points out that we’re deploying AI tools capable of reshaping our cognition, relationships, and behavior, but with little systematic effort to measure the downstream impacts they’re having on us.

The push to look more closely at AI’s psychosocial effects is similar to debates that emerged around social media and its harms, but Khan believes AI could have even broader and more intimate effects. The focus on measuring AI performance and progress misses the question of whether the technology is ultimately helping humans flourish—or eroding some of our most fundamental capacities.

IEEE Spectrum spoke with Khan about why AI evaluation is so narrowly focused, what meaningful measurement of human outcomes might look like, and whether the AI industry has incentives to ask these questions at all.

The missing question about AI model performance

In your essay, you argue that we’ve become very good at measuring what AI systems can do, but bad at measuring what they do to humans. What made you realize this was the missing question?

Khan: If you spend any time in and around the AI development space, you see this amazing progress in terms of what models are capable of, with graphs of how well different models perform on tests like SWE-bench or humanity’s last exam or LLM arena. There’s a competitive dynamic to how AI companies want to progress and be known for their models being the best. You see that impressive data, but then you also see these scary and dangerous things that happen in the real world, like teenagers dying by suicide and people succumbing to AI psychosis.

So on the one hand, we’re devoting an incredible amount of energy to measuring how AI does on these sometimes quite abstruse things that have limited relevance to most people’s day-to-day lives. And on the other hand, AI is impacting human well-being, and we’re measuring that much less. It seemed like a strange paradox that the things we should care about most, we’re measuring least.

Your essay points out that with social media, harms were already entrenched by the time the evidence was strong enough to act on them. Do you think AI is already producing measurable harms at scale, or are we still in an early-warning phase? What differences might there be in how quickly harm from AI evolves?

Khan: There are some really high-profile cases that I think are the tip of the iceberg—the teen suicides, AI psychosis, people spending immense amounts of time or money engaging with these AI chatbots that are designed to be incredibly sycophantic. I think those harms are already there.

Yet there is plenty we can do. Because of public pressure, OpenAI had to tweak one of its ChatGPT models due to public concerns about sycophancy. It’s a high-profile example of how the labs will pay attention and respond to scrutiny. So there is potential to change the direction of the technology to make it still useful, but less harmful. If we can measure some of those harms, that’s part of the ammunition we’d have to inform that.

Where it feels trickier is the question of harms on the societal level. What’s going to happen to romantic relationships, to families, to teenagers’ identities as a result of people using AI every day for months and years? I worry that if we don’t start measuring those kinds of phenomena soon, it will become too late to make a difference.

AI companies would likely argue that their users value convenience and productivity above all else. What would you say to this claim?

Khan: If you put a doughnut in front of me right now, I would probably not have the willpower to not eat it. Yet I also want to control my sugar intake and eat healthy. But technology design often gets boiled down to “Well, we’re just trying to give the users what they want, and what the users want is defined by what choice they make in an individual moment.”

This is the complexity of what it means to be a human and a consumer: We want contradictory things. We need to understand not just the choice a user might make when they’re busy or in a high-stress moment, but what they want a healthy relationship with this technology to look like. In the moment we often want low friction. But I don’t think any of us believe that a low-friction life is the most fulfilling or gives us the most learning and agency. So I think it’s asking a subtly different question, which is not what people choose in the moment, but what we want for ourselves in the longer term.

Are there specific domains—education, therapy, companionship, workplace copilots—where you think psychosocial measurement is especially crucial?

Khan: Some of the domains that stand out most to me are the ones around companionship and emotional support. The most likely target consumer group for those uses also might be the most vulnerable to potential effects. When people are lonely and craving the kind of emotional support that a chatbot offers, what they really should have is another human, someone who actually cares about them. An AI can’t care about you because it doesn’t have feelings or empathy. It might be pulling people away from doing the hard thing of trying to foster and engage in human relationships.

Child and adolescent use is another one because it’s such a formative and neuroplastic time in people’s lives. We don’t know the long-term effects on the developing brain if you drop the friction for cognitive tasks or emotional engagement.

Friends of mine who are teachers or parents have all these questions about education. AI is likely both good and bad for our capacity to learn and engage with new material and be curious.

And lastly, crisis response. There’s been a lot of news stories about suicidal ideation in particular, and whether AIs respond in appropriate ways.

How to evaluate AI’s societal effects

Your essay points out that AI benchmarks are mostly short-term and task-based, but most human impacts emerge over months or years. How might we design evaluations for these long-horizon impacts?

Khan: This gets to the heart of the evaluation problem. Evaluating how good an AI is at doing a coding task, hacking into a system, or answering complex scientific questions are all focused around giving the AI a task and seeing if they can do it or not. But when it comes to evaluating psychosocial impacts, you’re trying to measure impact in an individual human mind, or in a relationship, community, or society. That requires long-term studies.

An analogy is pharmaceuticals. When the [U.S. Food and Drug Administration] is approving a new drug, you go through different stages of trials, but after the drug is released, the FDA still mandates that companies do post-deployment monitoring, looking at things that might crop up over a five- or 10-year horizon.

Similarly, we need to be looking at novel phenomena that crop up, like how people’s relationship with AI changes over a year or two by looking at chat logs. Right now the companies have that data, but external researchers don’t. Opening access to more data in a way that still preserves privacy for users is one of the critical things we need to do.

Are companies likely to share that data? You mention they have little incentive to study downstream harms. What would change that incentive structure?

Khan: I think for the industry as a whole, there is an incentive to share the data. The industry wants there to be safe products that people trust. For individual companies, there’s a first-mover disadvantage; you don’t want to open yourself up if other companies aren’t doing the same. But if multiple companies step forward and say, “We’re on the side of researchers who want to make this safer,” there’s potential. And we have seen some companies do that. It’s not as extensive as we’d like, but researchers have published data with Anthropic and OpenAI that digs into some of this.

One other lever is liability. We’ve seen harms that go to the extremes of suicide, and AI companies have been sued. They want to be in a place where they don’t have that threat, and they can get there by making their products safer.

Ideally we would have regulation that embeds that liability. If someone suffers harm from a product that’s known to be defective, the companies are responsible and can’t just claim it’s free speech; it’s not just speech, it’s a product. However, we don’t want to rely on regulation because it’s uncertain. No one can predict what the future political environment will look like.

Five years from now, what would success look like for the movement you’re advocating? What concrete institutional changes would tell you this field has matured?

Khan: Right now a lot of the harms we’re seeing from AI use are chatbot-based, but we’re already starting to see some users replace that with extended use of agents. Inevitably, we’re going to be having real-time, always-on audio conversations with these agents. There are already services where you can make a video avatar of your AI. I think we’ll be not just engaging with a text-based chatbot but talking and hearing from something that sounds increasingly human.

If we don’t at least get our foot in the door with trying to understand the human effect of these technologies, I worry we’ll be so far behind the curve that we won’t be able to assess those future things. Success would be bringing together the expertise of people from within AI laboratories, government, regulators, universities, and startups who all care about this problem of, what does a good relationship between humans and AI look like? And they’re able to create the techniques that give us the confidence to have that more humane relationship with AI.

I think we’re making progress. But is the technology advancing quicker than the progress we’re making? I worry that right now, the answer is yes.

Memory is arguably the most serious constraint on modern AI large language models (LLMs). According to one influential paper, LLM token generation is an inherently memory-bound task, meaning the rate at which models output text is limited by how quickly data can be read in from memory. The severity of this bottleneck grows with model size. This creates a “memory wall” that holds back LLM inference performance.

AI hardware startup Majestic Labs is taking a direct—and comprehensive—approach to solving this problem. It’s developing a new AI server, Prometheus, with up to 128 terabytes of memory. That’s over 60 times more than Nvidia’s DGX B300 server, a cutting-edge AI processing rack.

Sha Rabii, co-founder and president of Majestic Labs, believes that this drastic increase in memory will provide his company an edge. While he acknowledges that “Nvidia’s done a phenomenal job creating a system that can scale out,” he argues that it becomes less economical as models grow and “ends up greatly over-provisioning on compute and starving on memory.”

DRAM-Centric Architecture for LLM Memory

Majestic Labs plans to surmount the “memory wall” with an architecture that fundamentally differs from competitors’.

Nvidia’s current servers have fast high-bandwidth memory (HBM), which is typically used to read in an LLM’s model weights. In addition, there’s an often larger but slower pool of dynamic random access memory (DRAM), which handles LLM and server overhead. Majestic instead goes all in on DRAM (specifically LPDDR6) in a unified architecture.

Rabii says that most memory interfaces are designed to operate over a short physical distance—sometimes only a few millimeters. That limits how much memory can be placed. “You get this shoreline at the compute die where you can put your HBM. If you wanted to put more, you can’t,” Rabii explains.

To solve that, Majestic uses a proprietary memory interface constructed from miniature copper cables that’s effective up to a meter. This is paired with custom memory aggregation chips that sit physically next to memory modules and coordinate memory across the server.

“It’s an endpoint for that high-speed interface and fans out to many, many commodity DRAM chips,” explains Rabii. In addition to addressing large pools of memory, Majestic says this design offers memory bandwidth up to 25.6 terabytes per second.

Ignite AI Processor for LLM Acceleration

More memory is good, but it needs to be paired with AI acceleration, something akin to Nvidia’s GPU. Majestic’s solution to this is Ignite, a custom AI processing unit that serves as the server’s compute engine. The Prometheus server contains 12 Ignite chips.

Ignite combines data-center-class ARM application cores with RISC-V vector and tensor cores on a single die, all sharing the same memory space. The ARM cores act as an on-chip host processor to orchestrate the AI model. The RISC-V cores carry out the actual LLM processing. The result is a single chip that handles multiple aspects of LLM inference demands without handing off between processors. Majestic Labs has yet to reveal specific metrics for Prometheus’ compute performance.

Rabii acknowledges that software is important as well, given that many AI frameworks are already entrenched. “We’re trying to reduce friction as much as possible in every aspect of our customer adoption, whether it’s physical or software,” he says. Prometheus will support PyTorch, vLLM, and OpenAI’s Triton inference frameworks without requiring code modifications. That means existing models compatible with these frameworks can run as-is.

Prometheus Server Design and Pricing

All of this combines in the server itself, which is Open Compute Project-compliant. Up to four servers can fit in a server rack; power draw is expected to total up to 120 kilowatts per rack; and heat will be managed with cold-plate liquid cooling. The server’s memory design is modular, which means servers purchased with less than the maximum of 128 TB of memory can be upgraded at a later date.

Despite the breadth of the project, Majestic wants to position Prometheus on price, too—which might be a surprise given how much memory each server can contain. Majestic argues that this will be possible because it uses DRAM instead of HBM. Pricing has not yet been announced, as Prometheus is expected to ship in 2027.

“Our customers’ capital expenditure will come down by, depending on the workload, 10 to 50 times, and the power consumption comes down by a similar amount,” Rabii claims.

I have been an application-specific IC (ASIC) designer for almost three decades. Over that time, I’ve moved through the full academic trajectory, from graduate student to full professor; later, I transitioned to industry after an unsuccessful stint at entrepreneurship. When I made the switch to the private sector in 2019, I began focusing on a critically important aspect of the electronic industry: silicon intellectual property.

As much as 80 percent of the physical area in today’s most advanced chips is occupied by blocks that aren’t made for specific products or even designed by the consumer-facing companies that built them. Instead, chipmakers draw heavily on established silicon IP from companies like Arm, Cadence, Rambus, Synopsys, and the company I work for, Silicon Creations.

Throughout my career, I’ve designed chips for very different purposes, including enabling the research program in my academic lab and expanding the IP portfolio of my company. When I joined Silicon Creations, I had no idea how differently the industry approaches IC design and encountered a steep learning curve. Initially, it seemed that much of my two decades of academic research and training did not directly translate to the role. I had to learn new skills and adopt a new mindset.

Today, demand for ASICs is rapidly growing, driven by the need for specialized chips in the automotive sector, AI applications, and more. By one market estimate, the ASIC market is expected to grow from US $23.4 billion to $38.8 billion by 2033, and the semiconductor industry as a whole is projected to hit $1 trillion by 2030. The industry needs more chip designers—but if you’re coming from an academic background as I did, there are a few things you’ll need to know.

Different goals lead to different strategies

The differences between industry and academe begin with a divergence in purpose. In academia, my primary objective was to generate new knowledge: to propose a novel circuit technique, validate an unconventional architecture, or explore the limits of performance in a given domain. A successful chip is one that demonstrates a concept. In industry, it is not nearly enough to prove that something can work. The goal is to ensure that it works reliably, repeatedly, and at scale. Success is measured not by novelty but by whether the silicon meets specifications, yields as expected in production, and supports a competitive product delivered on schedule.

This leads to a stark contrast in risk tolerance. Academic designs often deliberately push into unproven territory, where even partial success can yield valuable insight. In industry, however, we systematically minimize risk. The cost of failure makes first-time silicon success a central requirement—especially at advanced technology nodes, where the lithography masks used to transfer circuit designs onto silicon wafers alone can cost tens of millions of dollars. As a result, industry design flows are built around eliminating uncertainty through conservative margins, extensive validation, and careful reuse of proven solutions.

“Academia explores the design space, asking what is possible, while industry exploits it, determining what is viable at scale.”

This paradigm has existed since the 1970s, when application-specific chip design was established. However, the gulf between academia and industry has expanded since the mid-2010s, when FinFET technology, a 3D architecture using vertical “fins” of silicon, was widely adopted in industry. System designs are also becoming increasingly modular with the advent of chiplets. This fundamentally altered the economics and complexity of ASIC development, with design costs rising by almost an order of magnitude. Initiatives like Taiwan Semiconductor Manufacturing Co.’s University FinFET Program and new government-funded chip-design hubs now let some well-resourced universities design for more advanced architectures, but the technology is still out of reach for many academics.

What the industry-academia split means in practice

Consider a startup developing an ASIC. Its engineering team may have deep expertise in a particular algorithm, sensor interface, or system architecture, the features that define its competitive advantage. But it is unlikely to possess world-class expertise in every supporting function. Developing each of these blocks internally would require significant time, capital, and specialized talent. Doing so could delay market entry beyond the startup’s viability.

Even large semiconductor companies face similar constraints. Advanced-node development demands intense focus. Allocating a team to redesign a standard interface block that has already been implemented elsewhere may be difficult to justify when differentiation lies at the system level, such as an inference chip’s ability to speed up neural network computations. The time it takes to move a new chip from conception to market and risk mitigation, not self-sufficiency, govern most decisions about in-house development versus outsourcing.

The economics of advanced IC manufacturing reinforce this reality. When the development cost of a leading-edge chip reaches hundreds of millions of dollars, minimizing risk becomes a central design imperative.

In this context, silicon IP emerged as a practical solution. Similar to how software developers rely on preexisting libraries rather than writing every function from scratch, ASIC designers license predesigned, preverified silicon blocks—such as processor cores, memory interfaces, and security engines—from highly specialized IP vendors. These blocks can then be integrated into larger, increasingly complex systems.

Design scope, verification, and time horizons

With the use of silicon IP, industry is able to widen the scope of its designs. Academic efforts tend to focus on block-level innovation: a new analog-to-digital converter architecture or an ultralow-noise amplifier, for instance. These designs typically abstract away many of the complexities of bringing a chip to market, such as packaging constraints, long-term reliability, and manufacturing yield.

In industry, the focus shifts to system-level integration. Modern systems on chips, or SoCs, incorporate dozens or even hundreds of functional blocks. Managing signal integrity, timing, firmware interaction, and system-level validation becomes as critical as the design of any individual block.

Verification philosophy also diverges sharply. In academia, the goal of verification is to demonstrate that the concept works under nominal conditions, which may not always reflect how it would perform in real applications. Even if only a fraction of fabricated chips from a multiproject wafer operates correctly, the design may still be considered a success if it validates the underlying idea.

At my academic lab for instance, we used to receive 40 chips from a TSMC prototyping service and started testing them in batches of five. If the first five or 10 chips proved functional, we had already collected more than enough data for a publication. If some of them failed, we weren’t required to mention this when publishing the results.

In industry, verification is exhaustive, critical, and often dominates the development schedule. Failures are measured in parts per million, and even rare anomalies are carefully analyzed and documented to identify root causes and prevent recurrence. When I started at Silicon Creations, I was surprised by the level of detail and scrutiny designs face.

Differences in time horizons and economic constraints reinforce each of these contrasts. Academic projects operate on flexible timelines aligned with research and funding cycles. If I missed a deadline, I just had to wait for the next cycle. Industry projects are driven by fixed product schedules and market windows, frequently targeting costly leading-edge nodes to achieve competitive performance, power, and area efficiency. Missing a deadline can negate the value of an entire design and may have major financial consequences along the entire supply chain.

In essence, academia explores the design space, asking what is possible, while industry exploits it, determining what is viable at scale. Both are indispensable, but they operate under fundamentally different definitions of success. As ASIC complexity continues to grow, understanding both perspectives will be essential for the next generation of engineers navigating the evolving semiconductor landscape.

This article appears in the June 2026 print issue.