What’s next for AI in 2026

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Narrated by NOAA Listen to more of the best articles from the world's biggest publishers on the NOAA app or@newsoveraudio.com Will Douglas Heaven, Rhiannon Williams, James O', Donnell, Tsai Wei Chen and Michael Kim Wright what's next for AI in 2026? In an industry in constant flux, sticking your neck out to predict what's coming next may seem reckless. AI Bubble what AI bubble? But for the last few years we've done just that. And we're doing it again. How did we do last time? We picked five hot AI trends to look out for in 2025, including what we called generative virtual playgrounds, aka world models. Check. From Google DeepMind's Genie 3 to World Labs, marble tech that can generate realistic virtual environments on the fly keeps getting better and better. So called reasoning models. Check. Need we say more? Reasoning models have fast become the new paradigm for best in class problem solving. A boom in AI for science? Check. OpenAI is now following Google DeepMind by setting up a dedicated team to focus on just that, AI companies that are cozier with national security. Check. OpenAI reversed position on the use of its technology for warfare to sign a deal with the defense tech startup Anduril to help it take down battlefield drones and legitimate competition for Nvidia. Check. Kind of China is going all in on developing advanced AI chips, but Nvidia's dominance still looks unassailable. For now at least. So what's coming in 2026? Here are our big bets for the next 12 1. More Silicon Valley products will be built on Chinese LLMs the last year shaped up as a big one for Chinese open source models. In January 2025, DeepSeek released R1, its open source reasoning model, and shocked the world with what a relatively small firm in China could do with limited resources. By the end of the year, Deep Seek moment had become a phrase frequently tossed around by AI entrepreneurs, observers and builders. An aspirational benchmark of sorts of it was the first time many people realized they could get a taste of top tier AI performance without going through OpenAI. Anthropic or Google open weight models like R1 allow anyone to download a model and run it on their own hardware. They are also more customizable, letting teams tweak models through techniques like distillation and pruning. This stands in stark contrast to the closed models released by major American firms, where core capabilities remain proprietary and access is often expensive. As a result, Chinese models have become an easy choice. Reports by CNBC and Bloomberg suggest that startups in the US have increasingly recognized and embraced what they can offer. One popular group of models is Quen, created by Alibaba, the company behind China's largest e commerce platform, Taobao. Quinn.2515b instruct alone has 8.85 million downloads, making it one of the most widely used pre trained LLMs. The Quinn family spans a wide range of model sizes alongside specialized versions tuned for math coding, vision and instruction, following a breadth that has helped it become an open source powerhouse. Other Chinese AI firms that were previously unsure about committing to open source are following Deepseek's playbook. Standouts include Zhipu's GLM and Moonshot's Kimi. The competition has also pushed American firms to open up, at least in part. In August 2025, OpenAI released its first open source model. In November, the Allen Institute for AI, a Seattle based nonprofit, released its latest open source model, Olmo3. Even amid growing US China antagonism, Chinese AI firms near unanimous embrace of open source has earned them goodwill in the global AI community and a long term trust advantage. In 2026, expect more silicon Valley apps to quietly ship on top of Chinese open models and look for the lag between Chinese releases in the Western frontier to keep shrinking from months to weeks and sometimes less. 2. The US will face another year of regulatory tug of war. The battle over regulating artificial intelligence is heading for a showdown. On December 11, 2025, President Donald Trump signed an executive order aiming to neuter state AI laws, a move meant to handcuff states from keeping the growing industry in check. In 2026, expect more political warfare. The White House and states will spar over who gets to govern the booming technology, while AI companies wage a fierce lobbying campaign to crush regulations. Armed with the narrative that a patchwork of state laws will smother innovation and hobble the US in the AI arms race against China. Under Trump's executive order, states may fear being sued or starved federal funding if they clash with his vision for light touch regulation, big Democratic states like California, which just enacted the nation's first frontier AI law requiring companies to publish safety testing for their AI models, will take the fight to court, arguing that only Congress can override state laws, but states that can't afford to lose federal funding or fear getting in Trump's crosshairs might fold. Still, expect to see more state lawmaking on hot button issues, especially where Trump's order gives states a green light to legislate. With chatbots accused of triggering teen suicides and data centers sucking up more and more energy, states will face mounting public pressure to push for guardrails in place of state laws. Trump promises to work with Congress to establish a federal AI law. Don't count on it. Congress failed to pass a moratorium on state legislation twice in 2025, and we aren't holding out hope that it will deliver its own bill in 2026. AI companies like OpenAI and Meta will continue to deploy powerful super PACs to support political candidates who back their agenda and target those who stand in their way. On the other side, Super PACs supporting AI regulation will build their own war chests to counter watch them duke it out at 2027's midterm elections. The further AI advances, the more people will fight to steer its course, and 2026 will be another year of regulatory tug of war with no end in sight. Three Chatbots will change the way we shop Imagine a world in which you have a personal shopper at your disposal 247 an expert who can instantly recommend a gift for even the trickiest to buy for friend or relative. Or trawl the web to draw up a list of the best bookcases available within your tight budget. Better yet, they can analyze a kitchen appliance's strengths and weaknesses, compare it with its seemingly identical competition, and find you the best deal. Then, once you're happy with their suggestion, they'll take care of the purchasing and delivery details, too. But this ultra knowledgeable shopper isn't a clued up human at all. It's a chatbot. This is no distant prediction, either. Salesforce recently said it anticipates that AI will drive $263 billion in online purchases this holiday season. That's some 21% of all orders, and experts are betting on AI enhanced shopping becoming even bigger business within the next few years. By 2030, between 3 and $5 trillion annually will be made from agentic commerce, according to research from the consulting firm McKinsey. Unsurprisingly, AI companies are already heavily invested in making purchasing through their platforms as frictionless as possible. Google's Gemini app can now tap into the company's powerful shopping graph data set of products and sellers, and can even use its agentic technology to call stores on your behalf. Meanwhile, back in November 2025, OpenAI announced a ChatGPT shopping feature capable of rapidly compiling buyer's guides, and the company has struck deals with Walmart, Target, and Etsy to allow shoppers to buy products directly within chatbot interactions. Expect plenty more of these kinds of deals to be struck within the next year, as consumer time spent chatting with AI keeps on rising and web traffic from search engines and social media continues to plummet. 4. An LLM will make an important new discovery I'm going to hedge here right out of the gate, but it's no secret that large language models spit out a lot of nonsense, unless it's with monkeys and typewriters. Luckily, LLMs won't discover anything by themselves, but LLMs do still have the potential to extend the bounds of human knowledge. We got a glimpse of how this could work in May 2025 when Google DeepMind revealed Alpha Evolve, a system that used the firm's Gemini LLM to come up with new algorithms for solving unsolved problems. The breakthrough was to combine Gemini with an evolutionary algorithm that checked its suggestions, pick the best ones, and fed them back into the LLM. To make them even better, Google DeepMind used Alpha Evolve to come up with more efficient ways to manage power consumption by data centers and Google's TPU chips. Those discoveries are significant, but not game changing. Yet researchers at Google DeepMind are now pushing their approach to see how far it will go, and others have been quick to follow their lead. A week after Alpha Evolve came out, Asanki is Sharma, an AI engineer in Singapore, shared Open Evolve, an open source version of Google DeepMind's tool. In September 2025, the Japanese firm Sakana AI released a version of the software called Synca Evolve. And in November, a team of US And Chinese researchers revealed Alpha Research, which they claim improves on one of AlphaVolve's already better than human math solutions. There are alternative approaches too. For example, researchers at the University of Colorado Denver are trying to make LLMs more inventive by tweaking the way so called reasoning models work. They have drawn on what cognitive scientists know about creative thinking in humans to push reasoning models toward solutions that are more outside the box than their typical safe bet suggestions. Hundreds of companies are spending billions of dollars looking for ways to get AI to crack unsolved math problems, speed up computers, and come up with new drugs and materials. Now that Alpha Evolve has shown what's possible with LLMs, expect activity on this front to ramp up fast. Five legal fights heat up For a while, lawsuits against AI companies were pretty predictable. Rights holders like authors or musicians would sue companies that trained AI models on their work, and the courts generally found in favor of the tech giants. AI's upcoming legal battles will be far messier. The fights center on thorny, unresolved questions. Can AI companies be held liable for what their chatbots encourage people to do, as when they help teens plan suicides? If a chatbot spreads patently false information about you, can its creator be sued for defamation? If companies lose these cases, will insurers shun AI companies as clients? In 2026, we'll start to see the answers to these questions, in part because some notable cases will go to trial. The family of a teen who died by suicide will bring OpenAI to court in November. At the same time, the legal landscape will be further complicated by President Trump's executive order from December 2025. No matter what, we'll see a dizzying array of lawsuits in all directions, not to mention some judges even turning to AI amid the deluge. You are listening to MIT Technology Review. Where will Douglas Heaven, Rhiannon Williams, James o', Donnell, Tsai Wei Chen and Michelle Kim write What's next for AI in 2026? This article was published on 5 January 2026 and was read by Sam Scholl for Noah.