The article surveys how artificial intelligence is likely to evolve over the next year, building on earlier trend predictions that largely came to pass, from world models and reasoning systems to artificial intelligence for science and growing ties between major labs and national security. It argues that the landscape in 2026 will be shaped by a mix of technological advances, geopolitical dynamics, regulatory conflict, and high-stakes legal fights that collectively push artificial intelligence deeper into critical infrastructure, consumer life, and the justice system. Across these shifts, the central tension is who controls the technology, who benefits, and who bears the risks.
One major prediction is that more Silicon Valley products will quietly run on Chinese large language models, especially open-weight systems like DeepSeek’s R1 and Alibaba’s Qwen family. Qwen2.5-1.5B-Instruct alone has 8.85 million downloads, making it one of the most widely used pretrained LLMs, and the breadth of Qwen variants for math, coding, vision, and instruction-following has turned it into an open-source powerhouse. Chinese firms such as Zhipu and Moonshot are embracing open source, while American players like OpenAI and the Allen Institute for Artificial Intelligence have released their own open models, narrowing the lag between Chinese releases and Western frontiers from months to weeks, and sometimes less. This open ecosystem, strengthened by goodwill in the global research community, is expected to give Chinese models a long-term trust edge even as US-China tensions deepen.
Regulation in the US is forecast to become a partisan battleground after President Donald Trump’s December 11 executive order aimed at weakening state artificial intelligence laws by threatening lawsuits or the loss of federal funding for states that resist his light-touch approach. Big Democratic states like California, which just enacted the nation’s first frontier artificial intelligence law requiring companies to publish safety testing for their artificial intelligence models, are expected to fight back in court while other states may retreat. Congress failed to pass a moratorium on state legislation twice in 2025, and observers do not expect it to deliver a comprehensive federal law in 2026, leaving artificial intelligence firms like OpenAI and Meta to lean on powerful super-PACs while pro-regulation groups build their own. In parallel, a separate wave of litigation will test whether artificial intelligence developers can be held liable when chatbots allegedly contribute to teen suicides, spread defamatory falsehoods, or otherwise cause harm, with a high-profile case against OpenAI set for trial in November and judges themselves increasingly turning to artificial intelligence tools.
On the consumer side, the piece argues that chatbots will change how people shop by acting as always-on personal buyers that can research products, compare features, and handle checkout within a single conversation. Salesforce recently said it anticipates that AI will drive ? billion in online purchases this holiday season, and it states that is some 21% of all orders, signaling a rapid shift toward “agentic commerce” where autonomous systems transact on users’ behalf. By 2030, between ? trillion and ? trillion annually will be made from agentic commerce, according to research from the consulting firm McKinsey. Companies from Google, whose Gemini app taps its Shopping Graph and can call stores, to OpenAI, which has integrated a ChatGPT shopping feature and deals with Walmart, Target, and Etsy, are racing to capture this behavior as time spent in chatbot interfaces climbs and traffic from search engines and social media declines.
In scientific discovery, researchers see large language models as tools that can expand human knowledge when embedded in carefully designed feedback loops rather than left to generate ideas in isolation. Google DeepMind’s AlphaEvolve system combined its Gemini model with an evolutionary algorithm that filtered and refined suggestions, yielding more efficient ways to manage power consumption by data centers and Google’s TPU chips that the article describes as important but not yet transformative. The publication notes a fast-growing open ecosystem around this approach, including OpenEvolve, an open-source clone released by engineer Asankhaya Sharma, Sakana Artificial Intelligence’s SinkaEvolve, and AlphaResearch from a US-Chinese team that claims to improve on one of AlphaEvolve’s better-than-human math results. Alongside alternative methods, such as efforts at the University of Colorado Denver to make reasoning models more creatively “outside the box,” hundreds of companies are investing billions of dollars to get artificial intelligence to tackle unsolved math problems, optimize computing, and invent new drugs and materials, and the expectation is that an influential breakthrough will emerge from this competition.
Taken together, these developments paint 2026 as a year when open-weight Chinese models underpin a growing share of Western software, US regulators and politicians fight over who sets the rules, retailers and platforms lean into conversational commerce, and courts grapple with the social harms of generative systems. The underlying message is that as artificial intelligence systems advance, the struggle to direct their trajectory, constrain their risks, and capture their economic value will intensify, with no simple resolution in sight.
