At the edge, our E2B and E4B models redefine on-device utility, prioritizing multimodal capabilities, low-latency processing and seamless ecosystem integration over raw parameter count.Powerful, accessible, openTo power the next generation of pioneering research and products, we’ve sized the Gemma 4 models specifically to run and fine-tune efficiently on hardware — from billions of Android devices worldwide, to laptop GPUs, all the way up to developer workstations and accelerators.By using these highly optimized models, you can fine-tune Gemma 4 to achieve state-of-the-art performance on your specific tasks. We’ve already seen incredible success with this approach; for instance, INSAIT created a pioneering Bulgarian-first language model (BgGPT), and we worked with Yale University on Cell2Sentence-Scale to discover new pathways for cancer therapy, among many others.Here is what makes Gemma 4 our most capable open model family yet:Advanced reasoning: Capable of multi-step planning and deep logic, Gemma 4 demonstrates significant improvements in math and instruction-following benchmarks that require it.Agentic workflows: Native support for function-calling, structured JSON output, and native system instructions enables you to build autonomous agents that can interact with different tools and APIs and execute workflows reliably.Code generation: Gemma 4 supports high-quality offline code, turning your workstation into a local-first AI code assistant.Vision and audio: All models natively process video and images, supporting variable resolutions, and excelling at visual tasks like OCR and chart understanding. Additionally, the E2B and E4B models feature native audio input for speech recognition and understanding.Longer context: Process long-form content seamlessly. The edge models feature a 128K context window, while the larger models offer up to 256K, allowing you to pass repositories or long documents in a single prompt.140+ languages: Natively trained on over 140 languages, Gemma 4 helps developers build inclusive, high-performance applications for a global audience.Versatile models for diverse hardwareWe are releasing the Gemma 4 model weights in sizes tailored for specific hardware and use cases, ensuring you get frontier-class reasoning wherever you need it:26B and 31B models: Frontier intelligence, offline on your personal computersOptimized to provide researchers and developers with state-of-the-art reasoning on accessible hardware, our unquantized bfloat16 weights fit efficiently on a single 80GB NVIDIA H100 GPU. For local setups, quantized versions run natively on consumer GPUs to power your IDEs, coding assistants and agentic workflows. Our 26B Mixture of Experts (MoE) focus on latency, activating only 3.8 billion of its total parameters during inference to deliver exceptionally fast tokens-per-second, while our 31B Dense is maximizing raw quality and provides a powerful foundation for fine-tuning.

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. Fuel prices are soaring. Plastic could be next.  As the war in Iran continues, one of the most visible global economic ripple effects has been fossil-fuel prices. But looking ahead, further consequences could be looming for plastics.  Plastics are made from petrochemicals, and the supply chain impacts from the conflict are starting to build up. Americans will likely feel the ripples.   Read the full story to grasp the unpredictable impacts. 
—Casey Crownhart  This story is from The Spark, our weekly climate newsletter. Sign up to get it in your inbox every Wednesday. 
The must-reads  I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.  1 SpaceX has filed for an IPO It’s set to be the largest ever, targeting a $1.75 trillion valuation. (NYT $)  + Which would make Elon Musk the world’s first trillionaire. (Al Jazeera) + But the IPO could hinge on the success of Moon missions. (LA Times $) + And the conflicts of interest are staggering. (The Next Web) + Meanwhile, rivals are rising to challenge SpaceX. (MIT Technology Review)   2 Artemis II is on its way to the Moon NASA successfully launched the four astronauts on its rocket yesterday. (Axios) + The lunar plans could violate international law. (The Verge) + But the potential scientific advances are tremendous. (Nature)  + Check out our roundtable on the next era of space exploration. (MIT Technology Review)   3 Iran has struck Amazon’s cloud business in Bahrain again It promised to hit US companies only yesterday. (FT $) + Other targets include Google, Microsoft, Apple, and Nvidia. (CNBC) + AWS data centers in Bahrain were also hit last month. (Reuters $)  4 OpenAI was secretly behind a child safety campaign group It pushed for age verification requirements for AI. (The San Francisco Standard $) + OpenAI had backed the legislation as a compromise measure. (WSJ $) + Coincidentally, Sam Altman heads a company providing age verification. (Engadget)  5 Anthropic is scrambling to limit the Claude Code leak It’s trying to remove 8,000 copies of the exposed code from GitHub. (Gizmodo) + An executive blamed the leak on “process errors.” (Bloomberg $) + Here’s what it reveals about Anthropic’s plans. (Ars Technica) + AI is making online crimes easier—and it could get much worse. (MIT Technology Review)  6 A new Russian “super-app” aims to emulate China’s WeChat And give the Kremlin new surveillance powers. (WSJ $) 

7 America’s AI boom is leaving the rest of the world behind  And it’s concentrating power and wealth in a handful of companies. (Rest of World)  8 Chinese chipmakers have claimed nearly half the country’s market Nvidia’s lead is shrinking rapidly. (Reuters $)  9 The first quantum computer to break encryption is imminent  New research reveals how it could happen. (New Scientist)  10 The world’s oldest tortoise has been embroiled in a crypto scam Reports that Jonathan died at just 194 years old are thankfully false. (Guardian)  Quote of the day  “Starlink is the only reason this valuation is defensible.”  —Shay Boloor, chief market strategist at Futurum Equities, tells Reuters why SpaceX has such high hopes for its IPO.  One More Thing  These companies are creating food out of thin air  Dried cells—it’s what’s for dinner. At least that’s what a new crop of biotech startups, armed with carbon-guzzling bacteria and plenty of capital, are hoping to convince us.  
Their claims sound too good to be true: they say they can make food out of thin air. But that’s exactly how certain soil-dwelling bacteria work.  Startups are replicating the process to turn abundant carbon dioxide into nutritious “air protein.” They believe it could dramatically lower farming emissions—and even disrupt agriculture altogether. Read the full story. 
—Claire L. Evans  We can still have nice things  A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line.)  + Need more Artemis II in your life? This site takes you inside the flight. + Here’s a fascinating look at the recording errors that improved songs. + Good news: the elusive Nightjar bird is making a comeback. + Finally, a master chef has baked clam chowder donuts. 

As the war in Iran continues to engulf the Middle East and the Strait of Hormuz stays closed, one of the most visible global economic ripple effects has been fossil-fuel prices. In particular, you can’t get away from news about the price of gasoline, which just topped an average of $4 a gallon in the US, its highest level since 2022. But looking ahead, further consequences for the global economy could be looming in plastics. Plastics are made using petrochemicals, and the supply chain impacts of the oil bottleneck near Iran are starting to build up.  Plastic production accounts for roughly 5% of global carbon dioxide emissions today. And our current moment shows just how embedded oil and gas products are in our lives. It goes far beyond their use for energy.  As I write this, I’m wearing clothes that contain plastic fibers, typing on a plastic keyboard, and looking through the plastic lenses of my glasses. It’s hard to imagine what our world looks like without plastic. And in some ways, moving away from fossil-derived plastic could prove even more complicated than decarbonizing our energy system. 
Crude oil prices have been on a roller-coaster in recent weeks, and prices have recently topped $100 a barrel. Crude oil contains a huge range of hydrocarbons, and it’s typically refined by putting it through a distillation unit that separates the raw material into different fractions according to their boiling point. Those fractions then go on to be further processed into everything from jet fuel to asphalt binder. We’ve already seen the price spikes for some materials pulled out of crude oil, like gasoline and jet fuel.
Let’s zoom in on another component, naphtha. It can be added to gasoline and jet fuel to improve performance. It can also be used as a solvent or as a raw material to make plastics. The Middle East currently accounts for about 20% of global naphtha production­ and supplies about 40% of the market in Asia, where prices are already up by 50% over the last month. We’re starting to see these effects trickle down already. The price of polypropylene (which is made from naphtha and used for food containers, bottle caps, and even automotive parts) is climbing, especially in Asia.   Typically, manufacturers have a bit of stock built up, but that’ll be exhausted soon, likely in the coming weeks. The largest supplier of water bottles in India recently announced that it would raise prices by 11% after its packaging costs went up by over 70%, according to reporting from Reuters. Toys could be more expensive this holiday season as manufacturers grapple with supply chain concerns. Americans will likely feel these ripples especially hard if disruptions continue. The average US resident used over 250 kilograms of new plastics in 2019, according to a 2022 report from the Organization for Economic Cooperation and Development. That’s an absolutely massive number—the global average is just 60 kilograms. The effects of higher prices for both fuels and feedstocks could compound and multiply, and alternatives aren’t widely available. Bio-based plastics made with materials like plant sugars exist, but they still make up a vanishingly tiny portion of the market. As of 2025, global plastics production totaled over 431 million metric tons per year. Bio-based and bio-degradable plastics made up about 0.5% of that, a share that could reach 1% by 2030. Bio-based plastics are much more expensive than their fossil-derived counterparts. And many are made using agricultural raw materials, so scaling them up too much could be harmful for the environment and might compete with other industries like food production. Recycling isn’t the easy answer either. Mechanical recycling is the current standard method used for materials like the plastics that make up water bottles and disposable coffee cups. But that degrades the materials over time, so they can’t be used infinitely. Chemical recycling has its own host of issues—the facilities that do it can be highly polluting, and today plastics that go into advanced recycling plants largely don’t actually go into new plastics.

There’s been a lot of talk in recent weeks about how this energy crisis is going to push the world more toward renewable energy. Solar panels, electric vehicles, and batteries could suddenly become more attractive as we face the drastic consequences of a disruption in the global fossil-fuel supply. But when it comes to plastic, the future looks far more complicated. Even though the plastics industry is facing much the same disruptions as the energy sector, there aren’t the same obvious alternatives available for a transition. Our lives are tied up in plastic, with uses ranging from the essential (like medical equipment) to the mundane (my to-go coffee cup). Soon, our economy could feel the effects of just how much we rely on fossil-derived plastics, and how hard it’s going to be to replace them.  This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here. 

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. The gig workers who are training humanoid robots at home  When Zeus, a medical student in Nigeria, returns to his apartment from a long day at the hospital, he straps his iPhone to his forehead and records himself doing chores.  Zeus is a data recorder for Micro1, which sells the data he collects to robotics firms. As these companies race to build humanoids, videos from workers like Zeus have become the hottest new way to train them.   Micro1 has hired thousands of them in more than 50 countries, including India, Nigeria, and Argentina. The jobs pay well locally, but raise thorny questions around privacy and informed consent. The work can be challenging—and weird. Read the full story. 
—Michelle Kim  Our readers recently voted humanoid robots the “11th breakthrough” to add to our 2026 list of 10 Breakthrough Technologies. Check out what else officially made the cut. 
AI benchmarks are broken. Here’s what we need instead.  For decades, AI has been evaluated based on whether it can outperform humans on isolated problems. But it’s seldom used this way in the real world.  While AI is assessed in a vacuum, it operates in messy, complex, multi-person environments over time. This misalignment leads us to misunderstand its capabilities, risks, and impacts.  We need new benchmarks that assess AI’s performance over longer horizons within human teams, workflows, and organizations. Here’s a proposal for one such approach: Human–AI, Context-Specific Evaluation.   —Angela Aristidou, professor at University College London and faculty fellow at the Stanford Digital Economy Lab and the Stanford Human-Centered AI Institute.  MIT Technology Review Narrated: can quantum computers now solve health care problems? We’ll soon find out.  In a laboratory on the outskirts of Oxford, a quantum computer built from atoms and light awaits its moment. The device is small but powerful—and also very valuable. Infleqtion, the company that owns it, is hoping its abilities will win $5 million at a competition.   The prize will go to the quantum computer that can solve real health care problems that “classical” computers cannot. But there can be only one big winner—if there is a winner at all.  —Michael Brooks  This is our latest story to be turned into an MIT Technology Review Narrated podcast, which we’re publishing each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released. 

The must-reads  I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.  1 OpenAI just closed the biggest funding round in Silicon Valley history It raised $122 billion ahead of its blockbuster IPO, which is expected later this year. (WSJ $) + It’s also prepping a push to “rethink the social contract.” (Vanity Fair $) + Campaigners are urging people to quit ChatGPT. (MIT Technology Review)    2 Iran has threatened to attack 18 US tech companies  It’s eyeing their operations in the Middle East. (Politico) + Targets include Nvidia, Apple, Microsoft, and Google. (Engadget) + Iran struck AWS data centers earlier this month. (Reuters $)  3 Artemis II is about to fly humans to the Moon. Here’s the science they’ll do Their experiments will set the stage for future explorers. (Nature) + You can watch the launch attempt today. (Engadget)   4 Putin is trying to take full control of Russia’s internet New outages and blockages are cutting the country off from the world. (NYT $) + Can we repair the internet? (MIT Technology Review)  5 A robotaxi outage in China left passengers stranded on highways  Baidu vehicles froze on the streets of Wuhan. (Bloomberg $) + Police are blaming a “system failure.” (Reuters $)  6 US government requests for social media user data are soaring They’ve skyrocketed by 770% in the past decade. (Bloomberg $) + Is the Pentagon allowed to surveil Americans with AI? (MIT Technology Review) 
7 Tesla has admitted that humans sometimes drive its robotaxis Remote drivers occasionally control them completely. (Wired $)  8 A satellite-smashing chain reaction could spiral out of control This data visualization captures the dangers of space collisions. (Guardian) + Here’s all the stuff we’ve put into space. (MIT Technology Review) 
9 Meta’s smartglasses can turn you into a creep According to one journalist who wore them for a month. (Guardian)  10 A Claude Code leak has exposed plans for a virtual pet  We could be getting a Tamagotchi for the GenAI era. (The Verge)  Quote of the day  “From now on, for every assassination, an American company will be destroyed.”  —Iran’s Islamic Revolutionary Guard Corps (IRGC) threatens US tech firms in an affiliated Telegram, per CNBC.  One More Thing  ACKERMAN + GRUBER How one mine could unlock billions in EV subsidies  In a pine farm north of the tiny town of Tamarack, Minnesota, Talon Metals has uncovered one of America’s densest nickel deposits. Now it wants to begin mining the ore. 
Products made from the nickel could net more than $26 billion in subsidies through the Inflation Reduction Act (IRA), which is starting to transform the US economy. To understand how, we tallied up the potential tax credits available. Read the full story to find out what we discovered.  —James Temple  We can still have nice things  A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line.)  + A selfless group of gluttons tried to taste-test every potato chip in the world.  + Get romantic inspiration from these penguins’ engagement pebbles. + Good news: global terrorism has hit a 15-year low. + Enjoy endless new views through these windows around the world.  

When Zeus, a medical student living in a hilltop city in central Nigeria, returns to his studio apartment from a long day at the hospital, he turns on his ring light, straps his iPhone to his forehead, and starts recording himself. He raises his hands in front of him like a sleepwalker and puts a sheet on his bed. He moves slowly and carefully to make sure his hands stay within the camera frame.  Zeus is a data recorder for Micro1, a US company based in Palo Alto, California that collects real-world data to sell to robotics companies. As companies like Tesla, Figure AI, and Agility Robotics race to build humanoids—robots designed to resemble and move like humans in factories and homes—videos recorded by gig workers like Zeus are becoming the hottest new way to train them.  Micro1 has hired thousands of contract workers in more than 50 countries, including India, Nigeria, and Argentina, where swathes of tech-savvy young people are looking for jobs. They’re mounting iPhones on their heads and recording themselves folding laundry, washing dishes, and cooking. The job pays well by local standards and is boosting local economies, but it raises thorny questions around privacy and informed consent. And the work can be challenging at times—and weird. Zeus found the job in November, when people started talking about it everywhere on LinkedIn and YouTube. “This would be a real nice opportunity to set a mark and give data that will be used to train robots in the future,” he thought. 
Zeus is paid $15 an hour, which is good income in Nigeria’s strained economy with high unemployment rates. But as a bright-eyed student dreaming of becoming a doctor, he finds ironing his clothes for hours every day boring.  “I really [do] not like it so much,” he says. “I’m the kind of person that requires … a technical job that requires me to think.” 
Zeus, and all the workers interviewed by MIT Technology Review, asked to be referred to only by pseudonyms because they were not authorized to talk about their work. Humanoid robots are notoriously hard to build because manipulating physical objects is a difficult skill to master. But the rise of large language models underlying chatbots like ChatGPT has inspired a paradigm shift in robotics. Just as large language models learned to generate words by being trained on vast troves of text scraped from the internet, many researchers believe that humanoid robots can learn to interact with the world by being trained on massive amounts of movement data.  Editor’s note: In a recent poll, MIT Technology Review readers selected humanoid robots as the 11th breakthrough for our 2026 list of 10 Breakthrough Technologies. Robotics requires far more complex data about the physical world, though, and that is much harder to find. Virtual simulations can train robots to perform acrobatics, but not how to grasp and move objects, because simulations struggle to model physics with perfect accuracy. For robots to work in factories and serve as housekeepers, real-world data, however time-consuming and expensive to collect, may be what we need.  Investors are pouring money feverishly into solving this challenge, spending over $6 billion on humanoid robots in 2025. And at-home data recording is becoming a booming gig economy around the world. Data companies like Scale AI and Encord are recruiting their own armies of data recorders, while DoorDash pays delivery drivers to film themselves doing chores. And in China, workers in dozens of state-owned robot training centers wear virtual-reality headsets and exoskeletons to teach humanoid robots how to open a microwave and wipe down the table.  “There is a lot of demand, and it’s increasing really fast,” says Ali Ansari, CEO of Micro1. He estimates that robotics companies are now spending more than $100 million each year to buy real-world data from his company and others like it. A day in the life Workers at Micro1 are vetted by an AI agent named Zara that conducts interviews and reviews samples of chore videos. Every week, they submit videos of themselves doing chores around their homes, following a list of instructions about things like keeping their hands visible and moving at natural speed. The videos are reviewed by both AI and a human and are either accepted or rejected. They’re then annotated by AI and a team of hundreds of humans who label the actions in the footage. “There is a lot of demand, and it’s increasing really fast.” Ali Ansari, CEO of Micro1  Because this approach to training robots is in its infancy, it’s not clear yet what makes good training data. Still, “you need to give lots and lots of variations for the robot to generalize well for basic navigation and manipulation of the world,” says Ansari.

But many workers say that creating a variety of “chore content” in their tiny homes is a challenge. Zeus, a scrappy student living in a humble studio, struggles to record anything beyond ironing his clothes every day. Arjun, a tutor in Delhi, India, takes an hour to make a 15-minute video because he spends so much time brainstorming new chores. “How much content [can be made] in the home? How much content?” he says.  There’s also the sticky question of privacy. Micro1 asks workers not to show their faces to the camera or reveal personal information such as names, phone numbers, and birth dates. Then it uses AI and human reviewers to remove anything that slips through.  But even without faces, the videos capture an intimate slice of workers’ lives: the interiors of their homes, their possessions, their routines. And understanding what kind of personal information they might be recording while they’re busy doing chores on camera can be tricky. Reviews of such footage might not filter out sensitive information beyond the most obvious identifiers. For workers with families, keeping private life off camera is a constant negotiation. Arjun, a father of two daughters, has to wrangle his chaotic two-year-old out of frame. “Sometimes it’s very difficult to work because my daughter is small,” he says.  Sasha, a banker turned data recorder in Nigeria, tiptoes around when she hangs her laundry outside in a shared residential compound so she won’t record her neighbors, who watch her in bewilderment. “It’s going to take longer than people think.”Ken Goldberg, UC Berkeley While the workers interviewed by MIT Technology Review understand that their data is being used to train robots, none of them know how exactly their data will be used, stored, and shared with third parties, including the robotics companies that Micro1 is selling the data to. For confidentiality reasons, says Ansari, Micro1 doesn’t name its clients or disclose to workers the specific nature of the projects they are contributing to. “It is important that if workers are engaging in this, that they are informed by the companies themselves of the intention … where this kind of technology might go and how that might affect them longer term,” says Yasmine Kotturi, a professor of human-centered computing at the University of Maryland.
Occasionally, some workers say, they’ve seen other workers asking on the company Slack channel if the company could delete their data. Micro1 declined to comment on whether such data is deleted. “People are opting into doing this,” says Ansari. “They could stop the work at any time.”
Hungry for data With thousands of workers doing their chores differently in different homes, some roboticists wonder if the data collected from them is reliable enough to train robots safely.  “How we conduct our lives in our homes is not always right from a safety point of view,” says Aaron Prather, a roboticist at ASTM International. “If those folks are teaching those bad habits that could lead to an incident, then that’s not good data.” And the sheer volume of data being collected makes reviewing it for quality control challenging. But Ansari says the company rejects videos showing unsafe ways of performing a task, while clumsy movements can be useful to teach robots what not to do. Then there’s the question of how much of this data we need. Micro1 says it has tens of thousands of hours of footage, while Scale AI announced it had gathered more than 100,000 hours. “It’s going to take a long time to get there,” says Ken Goldberg, a roboticist at the University of California, Berkeley. Large language models were trained on text and images that would take a human 100,000 years to read, and humanoid robots may need even more data, because controlling robotic joints is even more complicated than generating text. “It’s going to take longer than people think,” he says. When Dattu, an engineering student living in a bustling tech hub in India, comes home after a full day of classes at his university, he skips dinner and dashes to his tiny balcony, cramped with potted plants and dumbbells. He straps his iPhone to his forehead and records himself folding the same set of clothes over and over again.  His family stares at him quizzically. “It’s like some space technology for them,” he says. When he tells his friends about his job, “they just get astounded by the idea that they can get paid by recording chores.” Juggling his university studies with data recording, as well as other data annotation gigs, takes a toll on him. Still, “it feels like you’re doing something different than the whole world,” he says. 

In partnership withMistral AI In the early days of large language models (LLMs), we grew accustomed to massive 10x jumps in reasoning and coding capability with every new model iteration. Today, those jumps have flattened into incremental gains. The exception is domain-specialized intelligence, where true step-function improvements are still the norm. When a model is fused with an organization’s proprietary data and internal logic, it encodes the company’s history into its future workflows. This alignment creates a compounding advantage: a competitive moat built on a model that understands the business intimately. This is more than fine-tuning; it is the institutionalization of expertise into an AI system. This is the power of customization. Intelligence tuned to context Every sector operates within its own specific lexicon. In automotive engineering, the “language” of the firm revolves around tolerance stacks, validation cycles, and revision control. In capital markets, reasoning is dictated by risk-weighted assets and liquidity buffers. In security operations, patterns are extracted from the noise of telemetry signals and identity anomalies. Custom-adapted models internalize the nuances of the field. They recognize which variables dictate a “go/no-go” decision, and they think in the language of the industry.
Domain expertise in action The transition from general-purpose to tailored AI centers on one goal: encoding an organization’s unique logic directly into a model’s weights. Mistral AI partners with organizations to incorporate domain expertise into their training ecosystems. A few use cases illustrate customized implementations in practice:
Software engineering and assisting at scale: A network hardware company with proprietary languages and specialized codebases found that out-of-the-box models could not grasp their internal stack. By training a custom model on their own development patterns, they achieved a step function in fluency. Integrated into Mistral’s software development scaffolding, this customized model now supports the entire lifecycle—from maintaining legacy systems to autonomous code modernization via reinforcement learning. This turns once-opaque, niche code into a space where AI reliably assists at scale. Automotive and the engineering copilot: A leading automotive company uses customization to revolutionize crash test simulations. Previously, specialists spent entire days manually comparing digital simulations with physical results to find divergences. By training a model on proprietary simulation data and internal analyses, they automated this visual inspection, flagging deformations in real time. Moving beyond detection, the model now acts as a copilot, proposing design adjustments to bring simulations closer to real-world behavior and radically accelerating the R&D loop. Public sector and sovereign AI: In Southeast Asia, a government agency is building a sovereign AI layer to move beyond Western-centric models. By commissioning a foundation model tailored to regional languages, local idioms, and cultural contexts, they created a strategic infrastructure asset. This ensures sensitive data remains under local governance while powering inclusive citizen services and regulatory assistants. Here, customization is the key to deploying AI that is both technically effective and genuinely sovereign. The blueprint for strategic customization Moving from a general-purpose AI strategy to a domain-specific advantage requires a structural rethinking of the model’s role within the enterprise. Success is defined by three shifts in organizational logic. 1. Treat AI as infrastructure, not an experiment.  Historically, enterprises have treated model customization as an ad hoc experiment—a single fine-tuning run for a niche use case or a localized pilot. While these bespoke silos often yield promising results, they are rarely built to scale. They produce brittle pipelines, improvised governance, and limited portability. When the underlying base models evolve, the adaptation work must often be discarded and rebuilt from scratch.In contrast, a durable strategy treats customization as foundational infrastructure. In this model, adaptation workflows are reproducible, version-controlled, and engineered for production. Success is measured against deterministic business outcomes. By decoupling the customization logic from the underlying model, firms ensure that their “digital nervous system” remains resilient, even as the frontier of base models shifts. 2. Retain control of your own data and models. As AI migrates from the periphery to core operations, the question of control becomes existential. Reliance on a single cloud provider or vendor for model alignment creates a dangerous asymmetry of power regarding data residency, pricing, and architectural updates. Enterprises that retain control of their training pipelines and deployment environments preserve their strategic agency. By adapting models within controlled environments, organizations can enforce their own data residency requirements and dictate their own update cycles. This approach transforms AI from a service consumed into an asset governed, reducing structural dependency and allowing for cost and energy optimizations aligned with internal priorities rather than vendor roadmaps. 3. Design for continuous adaptation. The enterprise environment is never static: regulations shift, taxonomies evolve, and market conditions fluctuate. A common failure is treating a customized model as a finished artifact. In reality, a domain-aligned model is a living asset subject to model decay if left unmanaged.

Designing for continuous adaptation requires a disciplined approach to ModelOps. This includes automated drift detection, event-driven retraining, and incremental updates. By building the capacity for constant recalibration, the organization ensures that its AI does not just reflect its history, but it evolves in lockstep with its future. This is the stage where the competitive moat begins to compound: the model’s utility grows as it internalizes the organization’s ongoing response to change. Control is the new leverage We have entered an era where generic intelligence is a commodity, but contextual intelligence is a scarcity. While raw model power is now a baseline requirement, the true differentiator is alignment—AI calibrated to an organization’s unique data, mandates, and decision logic. In the next decade, the most valuable AI won’t be the one that knows everything about the world; it will be the one that knows everything about you. The firms that own the model weights of that intelligence will own the market. This content was produced by Mistral AI. It was not written by MIT Technology Review’s editorial staff.