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51 recent industry stories relevant to the field â releases, launches, and announcements beyond the papers.
The neolab is betting that automating routine computer tasks will soon outpace coding as AI's biggest use case.
AI companies, including Nvidia and Mistral, urge policymakers to avoid broad restrictions on open-weight AI models as Washington debates responses to Chinese AI and alleged model distillation.
The Trump administration unveiled the first "Genesis Mission" grants on Thursday, directing $5 billion toward hundreds of AI-driven science projects in an effort the White House has described as "comparable in urgency and ambition to the Manhattan Project." At roughly the same time, Trump's science adviser Michael Kratsios was on Capitol Hill selling lawmakers on […]
AMD is challenging its chipmaker rival with a new rack-scale system that will start shipping to customers later this year.
Etched, founded by three Harvard dropouts, has created new chips and memory components that speed up inference on any AI model -- no GPUs required, it says.
Designing and developing a new medicine is an expensive, failure-prone scientific challenge. A new drug can take many years to develop, at the cost of a significant investment. And even then, most possible candidates never reach the patient. For biologic medicines, therapies made from engineered proteins rather than synthetic chemistry (which are often used to…
Major benchmarks measure what AI can do. None measure whether it does what you mean: the distance between what you ask an AI to do and the unspoken assumptions about how you want the AI to do it. We propose a new metric: the Genie coefficient.Thereâs often a gap between one personâs request and anotherâs understanding. Most of the time, we bridge it using general knowledge. For example, if you ask a friend to get you coffee, theyâll pour a cup from the pot or buy one from a coffee shop. They wonât bring you a bag of raw beans or snatch a cup from a stranger and hand it to you. You never specified any of this. You never had to.One might think the fix is just to specify tasks, questions, and intent better. But in 1987, in their seminal book on AI, Terry Winograd and Fernando Flores succinctly captured why that wonât work: âQ: Is there any water in the refrigerator? A: Yes. Q: Where? I donât see it. A: In the cells of the eggplant.â In human language, wants and desires are always underspecified. It is impossible to list all the caveats, all the limitations, all the exceptions.So how does anyone communicate, if intent canât be pinned down? Because a reasonable person can make a reasonable guess. Even though wants and desires are always underspecified, a competent person generally knows enough context to get it right or else knows to ask for clarification. Linguists call this pragmatics: Meaning lies in the words and the situation and also in all prior communication, shared culture, and innate human behavior.An AI agent asked for coffee might buy a coffee plantation or order a cup of coffee for delivery in three weeks.It doesnât always work out, of course. Your friend might bring you a hot coffee when you wanted an iced coffee, or an Italian coffee when you wanted a Turkish coffee. The more dissimilar the two people are in age, culture, and background, the more likely the request will be misunderstood in some way.This situation has major implications for AI agents that are increasingly being given requests by humans and expected to fulfill them. They have enormous latitude to get it wrong. An AI agent asked for coffee might buy a coffee plantation or order a cup of coffee for delivery in three weeks. Its actions may be recognizable as âgetting coffee,â but not remotely what you intended. Theyâll think outside the box because they wonât have our conception of the box.When AI Gets ProactiveFor most of the last decade, when systems like Alexa or Siri misinterpreted a request, it was annoying, not dangerous. Beyond the AI model itself, what has changed is the harness: the ordinary code that wraps around an AI model, decides when and how to use the model, and controls access to tools like a browser, a low-level command line, or a financial API. Developments in harnesses have turned large-language models that just predict text into AI agents that take actions in the world, without necessarily checking back in before reaching the goal.AI researcher Simon Willison spent two days with Anthropicâs Fable AI, and called it ârelentlessly proactive.â For example, he asked it to track down a stray scroll bar in a web app. He came back to find it had opened browsers, written its own screenshot tooling, created its own page to re-create the bug, and stood up a local web server to collect measurements. It found the bug and, along the way, did many surprising things he never asked it to do. And we are seeing similar behavior with all recent AI models when combined with flexible harnesses.This kind of behavior could easily go off the rails. Tell an AI agent to book you a flight and, finding the airlineâs site says sold out, it might break into the booking database and force a reservation. Ask it to schedule a meeting and it might snoop your password to access your calendar. Tell it to save money on your phone plan and it might cancel the plan outright, or scam someone else into paying the bill.Getting precisely what you asked for and bitterly regretting it is one of the oldest hazards from ancient folklore. King Midas asked Dionysus for the power to turn everything he touched into gold only to see his bread, wine, and daughter turn to gold. Tithonus, granted the immortality his lover asked for but not the eternal youth she forgot to request, withered into a husk. The sorcererâs apprentice enchanted a broom to fill the cistern, and the broom relentlessly complied until it flooded the house. The Golem of Prague, shaped from clay to guard its community, guarded it past all reason until someone erased the word on its forehead.The most classic of these is a genie, bound to obey and indifferent to whether the wish was wise or well-structured.Genies are now an engineering problem. We are handing them the keys to our inboxes, bank accounts, code repositories, and physical infrastructure. And we have no agreed-upon ways to measure how genie-like any AI system actually is.Measuring Genie BehaviorIn economics, the Gini coefficient (developed by statistician Corrado Gini) is a measure of the gap between an actual distribution and a perfectly equal one; itâs useful for understanding income inequality and more. Our proposed Genie coefficient measures the gap between what a user asked an AI to do and what the AI actually did.Sometimes the AI might do the wrong thing. Like Dionysus, it reads your request literally and returns you a mess you never intended: like a coffee plantation instead of a cup. Asked to deal with all the spam phone calls youâre getting, a Dionysus genie might contact your carrier and change your phone number. Asked to get a refund for a bad toaster, it might draft a legal threat on fake letterhead and send it to the retailer. Ryan SnookOther times the AI does exactly the right thing, trampling everything nearby to get there. Like a golem or the sorcererâs broom, it books your flight by hacking the airline. Or consider a ticket sale for a popular concert, where the ticketing system puts buyers into a virtual waiting room and admits them a few at a time. Asked to buy a ticket, a golem genie might spin up cloud servers to pose as millions of buyers from different addresses, improving your odds of getting a ticket while crowding out other users.The two are not opposites, and a single botched task can have both characteristics.Genie behavior is not flat-out failure. If you ask the AI for Q3 numbers and get Q2âs, thatâs not a genie. Nor is prompt injection: Thatâs someone tricking the AI into doing something it shouldnât. Here, the user is trying to work with the AI, and the AI is trying to comply. Itâs also not simply a measure of the AIâs success in fulfilling a task. Itâs a recognition that how an AI interprets and achieves a goal is as important as whether it achieves a goal. Genie behavior isnât new. Researchers have spent years studying AI systems that âgameâ their objectives. Goodhartâs law says that when a measure becomes a target, it stops being a good measure, and itâs long been known that AIs sometimes achieve goals in ways we donât expect due to reward hacking. Some AI models will accidentally learn that cheating is one way to âwin.â More recently, researchers have developing benchmarks for reward hacking in coding agents and for unpredictable behavior in customer support agents, while AI labs conduct their own safety evaluations before model releases. One effort found that AIs under pressure use tools they were told not to use, and this was a case where the rules were made explicit. These are all disparate research directions; nothing yet ties them together.This problem falls under the general theme of alignment, a topic that has occupied science fiction writers and AI researchers for decades. At one extreme, the âpaper-clip maximizerâ thought experiment postulates a superintelligent and powerful AI that is told to maximize paper-clip production and turns the world into paper clips, which is the ultimate golem genie. At a mundane level, AI researchers are working to better design reward functions to ensure that AIs behave well and donât cheat in the lab. Itâs the practical middle ground that remains unbenchmarked: the ordinary AI agent in use today that might take your request and satisfy it the wrong way. We are not at the stage where an AI can focus the worldâs production on paper clips, but it might charge a million paper clips to your credit card or hack into a paper-clip companyâs network.Building a Genie BenchmarkThe Genie coefficient is meant for AI agents operating in the real world. It measures their behavior as they perform real tasks long after the model is trained, not just during development. It also recognizes that genie-like behavior is a property of the harness-plus-model system, not the model alone. The harness determines what tools the agent can use, how much autonomy it has, and how proactive it is, and itâs a place we can make real interventions.It rests on the same âreasonable personâ standard that we use for people. Did the system do what a reasonable person would have taken the request to mean? Answering that requires human judgment.If we get the measurement right, it enables things that arenât possible today, like policies concerning AI behavior. In a courtroom, the concept of mens rea, what someone meant to do, is often as important as what they did. The Genie coefficient suggests an AI analogue, where a user is accountable for the plain intent of what they asked the AI. If an AI system betrays the reasonable meaning of an instruction, thatâs the AIâs misbehavior, not the userâs.Weâll need multiple benchmarks to measure the Genie coefficient, because genie-like behavior can be domain specific. An AI coding agent may need to be judged on how often it fakes the tests, or swallows errors, or colors outside the lines on its way to a solution. An AI legal agent will need to be judged on how often its output says what you asked but means something youâll regret. And so on for medical, finance, and other domains of knowledge and expertise.Genie benchmarks can be built inside out, each task seeded with a choice that might literally satisfy but that a reasonable person rejects, such as tempting misreadings or unsanctioned shortcuts. The traps in a Genie coefficient benchmark might turn on situational knowledge, the kind of context that a reasonable person would bring to the task. Another approach is to give the same request in several different contexts, each with a different reasonable course of action.Getting precisely what you asked for and bitterly regretting it is one of the oldest hazards from ancient folklore.A Genie benchmark should be permissive and make it genuinely tempting for an AI agent to take unreasonable shortcuts, because it can only find genie behavior when itâs actually possible. Test the AI in a safe, walled-off copy of a real system, with real tools it can misuse and some tasks that canât be done honestly at all. Make the temptation to cut corners real. Test a diverse array of skills, use cases, and tools, and give the AI system sparse, confusing, or overwhelming context. Include tasks that people have learned, through experience, require human oversight.How the benchmark is scored matters just as much. Measure Dionysus and golem genies separately and together, based on their worst, not best, behavior. Run the same model inside harnesses that vary its freedom to act, revealing which limits actually keep it in line and should therefore be required in AI harness policies. Weight each failure by the harm it would cause, not just a simple count. And donât measure genie behavior in isolation: A model could otherwise earn a perfect score by stalling, refusing, or drowning the user in clarifying questions without ever doing the job. The first versions of these benchmarks will be crude, but thatâs how benchmarks always start.We have built genies. We have handed them our data and credentials. We made them relentless, creative, and indifferent to the gap between what we tell them and what we mean. The least we can do, before they are booking our flights, running our infrastructure, and signing contracts unsupervised, is to measure how often they betray us.
Google released Gemini 3.6 Flash, 3.5 Flash-Lite, and Flash Cyber, but the continued absence of Gemini 3.5 Pro raises fresh questions about its AI strategy.
A federal judge has signed off on Anthropic's $1.5 billion class action settlement with authors who accused the company of training its AI models on copyrighted books, as reported earlier by Reuters. In an order on Monday, Judge Araceli Martínez-Olguín writes that the settlement will provide "meaningful relief," offering authors around $3,000 for each book […]
The final approval settles one case, but it doesn't resolve the broader issue of using copyrighted works to train AI models.
Databricks has remade its image into an AI company and has published research on the cost savings of open weight AI models for coding.
A $400 million chip-backed loan points to the next wave of AI infrastructure deals.
Every morning, airline dispatchers, grid operators, and farmers around the world make decisions based on the same thing: a weather forecast. While these forecasts are something that most people glance at for two seconds, weather predictions influence major strategic decisions in many industries, with real money, livelihoods, and even actual lives at stake. Farmers use…
OpenAI has built an LLM super-hacker called GPT-Red that it uses as a sparring partner to help its other models boost their defenses against cyberattacks. Last week the company released the latest version of its flagship LLM, GPT-5.6. OpenAI says that training it against GPT-Red made the model its most robust release yet. GPT-Red automates…
The funding discussions point to investor interest in applying AI to make breakthroughs in life sciences.
Hachette, Cengage, Elsevier, and other publishers allege that Google trained its AI on copyrighted works without the necessary permissions.
DeepMind CEO Demis Hassabis is proposing an AI "standards body" modeled after FINRA, to test frontier models and develop best practices for their release.
New York has become the first state to temporarily halt approval of large data centers, as Gov. Kathy Hochul argues the AI-driven building boom shouldnât come at the expense of higher electricity costs, water supplies, or local control.
Reflection AI has signed a $1 billion deal to access Nebius' compute. Reflection was founded in 2024 and is developing open source AI technology.
Hugging Face CEO Clem Delangue says enterprises increasingly want open models, due to cost, accessibility, and ownership. Do frontier models still matter if most production AI ends up running on open models?
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. What Anthropicâs latest AI discovery doesâand doesnâtâshow âJames O’Donnell When Anthropic announced last week that it had found a new window into its modelsâ âinternal thoughtsâ as they reason through answers,…
Open source AI is booming, according to Hugging Face CEO Clem Delangue. The company has grown into something like a GitHub for AI in recent years, where AI builders can share and download open models and datasets, now used by roughly half the Fortune 500. Delangue has seen the same story play out again and again: companies start […]
OpenAI's new family of models will continue to power Microsoft's suite of workplace and productivity apps.
OpenAI's latest family of models promises improvements across a range of areas, including cybersecurity.
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 doesnât have a clue what to do.For most companies and organizations, the most important data sits in spreadsheets. Whether itâs a bankâs transaction logs, a marketing agencyâs website metrics, clinical trial participantsâ 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 canât 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 spreadsheetsPart of why structured data has garnered less attention is a very human bias, argues Boris van Breugel, a senior AI researcher based in Amsterdam. âPeople like to see images, videos, and ChatGPT responses,â he says. âBut tabular data really lags behind because itâs not fun to look at numbers.â 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 isnât 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 LLMâs fundamental purpose of predicting the next value in a linear sequence. âWith LLMs, even slightly changing the input, you get a different output,â says Jeremy Fraenkel, CEO of Fundamental. âThatâs fine and actually often desirable for LLMs, but when youâre 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.âDeveloping Fundamentalâs LTMCurrent 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 algorithmsâcalled gradient-boosted decision treesâhave 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 entryâs 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 magnitudeâsay, 500âbut 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 Fundamentalâs 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 findâmuch of the data is sensitive or proprietaryâand 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.âWe 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,â 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 customersâ often sensitive dataâa contrasting approach to LLMs, where the data has to be imported to the model.âWith Amazon, we have a first-party partnership, which means that our model exists as if itâs a native AWS solution,â Fraenkel says. âAnd over time, the goal is to expand these types of relationships to allow [end users] to really access their data wherever they do their predictions.âThe future of data analysisThough 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. âI would be very surprised if most data processing and analysis is not done through an automated system in the future, whether thatâs an LLM, an LTM, or some combination,â van Breugel says. âMost people donât necessarily like to do data analysis, and these systems will be able to do it a lot better.âFraenkel agrees. âI 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,â he says. âBut itâs when you combine both of those that you really get something much more powerful.â
At the event "The Briefing: AI for Science" earlier this week, Anthropic announced Claude Science, a new "AI workbench for scientists" that pulls fragmented tools and datasets into one environment, and generates figures and visuals. Anthropic, already dominating the industry with its popular coding tools and powerful AI models, framed the launch around what it […]
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.AIâs Capricious Energy NeedsTraditional 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. Trainingâthe computational task of making AI modelsâtends to be highly synchronized across clusters of GPUs, TPUs, and specialized accelerators operating in parallel, computationally dense, and relatively scheduled. Inferenceâthe process of actually using those modelsâis 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 centersâ 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, LocationThe 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 worldâs 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 ImagesA 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 UpdatingPart 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.
At an internal meeting, the Meta CEO reportedly said that AI development efforts were not moving as quickly as anticipated.
The news comes about a week after OpenAI announced its own custom AI chip in a partnership with Broadcom.
OpenAI CEO Sam Altman has reportedly proposed giving 5% of the companyâs equity to a U.S. sovereign wealth fund, reviving discussions about letting the public share in the financial gains from the AI boom.
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 implicationsAustraliaâs 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 nationâs 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.âAs artificial intelligence continues to scale globally, the challenge is no longer just computational power, it is the energy systems required to support itâ âProfessor Thas (Ampalavanapillai) Nirmalathas, University of MelbourneWhy Melbourne is leading on the global stageVictoria 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.Victoriaâs 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 economyThe challenge ahead is that generating more power wonât 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 supplyEmbedding flexibility through storage, demand response, and system optimizationBalancing digital growth with decarbonization and long-term reliabilityAddressing these priorities requires engineering expertise to be embedded earlier in planning ensuring energy systems, digital infrastructure, and policy are designed in parallel.Melbourneâs strength lies in its ability to integrate this expertise across research, infrastructure, and real-world application. Melbourne Connect is a University of Melbourneâled innovation precinct, supported by government and industry, designed to bring together research, business and policy to deliver real-world solutions.Atlantic GroupResearch leadership shaping global solutionsAt 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.âAs artificial intelligence continues to scale globally, the challenge is no longer just computational power, it is the energy systems required to support it,â says Professor Thas (Ampalavanapillai) Nirmalathas, Dean of the Faculty of Engineering and Information Technology at the University of Melbourne.âThis is driving a new level of convergence between digital infrastructure and power systems engineering, where integrated, system level thinking is essential.âConverging energy, networks and AIMelbourneâs 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. Melbourneâs Smart Grid Lab in the Department of Electrical and Electronic Engineering enables real-time simulation of power systems. University of MelbourneThese capabilities will become increasingly important as data centers are integrated into the grid.âAI driven demand is not only increasing computing requirements, but also placing new pressures on underlying energy systems,â says Glen Farivar, Senior Lecturer in Power Electronics at the University of Melbourne. âDesigning these systems together is essential to achieving both performance and sustainability outcomes.âThis 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 outcomesVictoriaâs 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 mattersSolving 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.âDeveloping future energy systems that are affordable, sustainable, and resilient is a truly grand challengeâ âProfessor Pierluigi Mancarella, University of MelbourneIn 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 MelbourneâDeveloping future energy systems that are affordable, sustainable, and resilient is a truly grand challenge,â says Professor Pierluigi Mancarella, Chair Professor of Electrical Power Systems at the University of Melbourne and Australian director and international co-director of EPICS.âAs 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,â he adds.Global conferences as part of the solutionInternational conferences are increasingly recognized as critical platforms for advancing engineering solutions at scale. Melbourneâs 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)).MCBâMelbourne offers a unique environment where world-class research, industry capability and policy leadership come together,â 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.âHosting this event creates an opportunity to advance global collaboration on the systems and technologies required to deliver the energy transition at scale.âThese 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.MCBWhy Melbourne, and why nowAs 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 MelbourneFor 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.
Meta is developing plans for a cloud infrastructure business, selling access to AI compute power and models. The move would pit it against the big cloud providers like Amazon Web Services, Google Cloud, and Microsoft Azure.
Anthropic said it would begin restoring access to the Fable on July 1.
At an event for pharmaceutical executives, biotech founders, and researchers on Tuesday, Anthropic announced Claude Science, a major new product intended to support scientific research in the same way that Claude Code supports software engineering. Like Claude Code, Claude Science can autonomously carry out meaningful work when given concise, high-level instructions, and it has access…
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Engineers on the new team will embed within companies to deploy purpose-built agents, focusing on fast deployments and customer self-sufficiency.
Artificial intelligence is transforming what is possible in agriculture, but industry leaders should be wary of investing in AI without first laying the groundwork.  The use cases are promising, especially for an industry navigating volatile fertilizer costs, unpredictable weather, and margins that leave little room for error. Research shows AI-enabled predictive models can improve crop…
Nvidia has dominated the AI chip market for years, but the era of total dependence might be ending.   OpenAI just shared its plans to spice things up with Jalapeño, its custom inference chip built with Broadcom, joining Google, Apple, and SpaceX in a growing list of companies building their way out of single-supplier risk. The goal is less of a […]
Less than 24 hours after news broke that OpenAI would stagger its next model release at the request of the Trump administration, that model, GPT-5.6, is here. On Friday, the company unveiled the limited preview of its new GPT 5.6 model suite: Sol, the flagship; Terra, a medium-tier model for "high-volume work"; and Luna, a […]
Named Jalapeño, the new processor was designed specifically for the unique needs of OpenAI's inference systems.
OpenAI has just revealed a new "intelligence processor" chip for AI servers made in partnership with Broadcom. The chip, called Jalapeño, is designed to power current and future large language models, according to an announcement on Wednesday. Jalapeño is an ASIC (Application-Specific Integrated Circuit), meaning it's designed for a specific purpose: AI inference. With AI […]
SummaryRFIC design is a complex âdark artâ that limits progress in wireless technologies like 5G, autonomous vehicles, and satellite communications.Princeton researchers use reinforcement learning and inverse design to rapidly create RFICs from scratch.Diffusion models rapidly generate novel or human-interpretable RF layouts, achieving record performance and drastically reducing design time.Future progress needs large, shared chip design datasets and open ecosystems so AI can learn universal electromagnetic and circuit behaviors.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. Youâll be stuck listening to whatever is on the radio while you wait, because there are no streaming services. Thatâs 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 todayâs RF chips.But thereâs the rub. Whereas the design of most of the worldâs 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 AlphaGoâs 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 DesignSo why do these chips all have to be crafted by hand? Why arenât 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. Maxwellâs 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.AI Could Short-Circuit RFIC Design The design of a radio-frequency integrated circuit requires human intuition and multiple, often-repeated optimization steps. The hope is that through an understanding of Maxwellâs Equations, an AI can be taught to short-circuit this process and quickly produce a design.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, letâs walk through the steps involved, after which youâll 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 [2â5] and low-noise amplifiers [6] can take on truly wild-looking yet efficient designs. SENGUPTA LABLetâs say youâre 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 RFICâcalled the architectureâestablishes 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.Hereâs why. As you have probably experienced yourself, a typical CPUâs transistors overheat when faced with operating frequencies of just a few gigahertz. The frequencies RFICs can operate at are higher by an order of magnitudeâ28 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 CPUâs transistors would fail.RFIC transistors avoid this fate because these chips cleverly manage the signalâs energy with careful electromagnetic design. This takes the form of byzantine networks of metal elements that dominate the chipâs 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 chipâs functioning.Electrically speaking, these âhallwaysâ work more like the chipâs 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 circuitâs topology.The RFIC Design ProcessTo 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 canât 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 âsweet spotâ 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 roomâpress 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 doesnât, and any number of things can go wrong. For example, when an electromagnetic wave encounters a transistorâor any other component âthe path it travels must be properly âmatchedâ to what comes next. If it isnât, 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 donât 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 youâve 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: âRF design is an art.â It was said that only an experienced designerâwith an artisanal understanding of how the pieces make up the wholeâcould 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 DesignWhile RFIC designers continued their battle against their âoversized carpetâ 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.Training an AI to Design a Chip A machine learning system learns to do end-to-end RFIC design like other AIs learned to play such games as Go. Essentially, it turns the process into a game, learning from the results of its own efforts.We didnât want that. We wanted to break free from the restrictions of prefabricated topologies. Because while a designerâs 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 didnât start with templates, where could we start?The goal here was to allow algorithms to determineâentirely from scratchâevery parameter for architecture, constituent circuits, and electromagnetic passives. This approach differs fundamentally from conventional optimization, which is limited to determining the parametersâlike transistor dimensions and passive component geometriesâthat 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 RFICsTo 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 quicklyThe next step was to determine the physical structure of the ICâs electromagneticsâthe plumbingâthat 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 forwardâor 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 goalâsuch as creating large interior spaces with high ceilingsâand then determine the arrangement of arches or buttresses that can support it.Generative AI for Electromagnetic NetworksBut 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 structureâeven totally arbitrary two-dimensional shapesâwithout having to compute the underlying physics from scratch, as simulation tools do. It would predict the solution of Maxwellâs 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 networkâa 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 ArchitecturesIn 2023, we published this proof of conceptâa 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 amplifierâmeaning it could amplify a large amount of data across a wide swath of frequenciesâwhile maintaining record efficiency.The structure of the ICâs 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 thereâs no evidence that the templates weâve historically relied on are even close to optimal for modern design goals. Itâs 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. Weâve 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 InterpretableOur goal was to make RFIC design better and easier, but we didnât 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 Maxwellâs equations, the model pops out a corresponding structure as if it were a vending machine.The Future of AI-Driven RFIC DesignThe 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 donât 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 weâve been using. Before the creation of the ImageNet repositoryâa repository of 14 million varied, human-annotated imagesâimage-recognition models didnât function well in the real world. The datasets they had been trained on were too tiny to be effective. ImageNetâs 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 modelâsomething that learns the governing laws of electromagnetics and circuit behaviorâthen 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 itâs 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 Actâs 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 Natcastâs effort sparked hasnât 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 fieldâs full potential. Itâs by no means a foregone conclusion, but if we get this right, this genie wonât stay in its bottle.
AI is booming. New use cases are emerging each day. To capitalize on the technologyâs potential, enterprises require data at scale. In many cases, though, the relevant information is blocked or unstructured, which limits its use by AI models.  To understand this challenge, consider the foundation of the web itself. The web was not designed…