AI predictions for 2026; Startup Coalition publishes the UK's AI Index; Microsoft reshuffles GitHub team; OpenAI says AI will do more housework; how Google got its AI groove back
AI chip startups see a brighter future; some AI startups are reimagining the web browser; AI models are starting to learn by asking themselves questions; Grok generates abusive images at scale
A few weeks ago, the hosts of the Crashed podcast invited me to send in my predictions for 2026. Despite having a really bad sinus infection, I agreed and recorded a video for them, a portion of which was included in the final edit. I’m embedding the clip below which also includes the hosts’ reaction to what I said.
(If you want to listen to the whole episode, you can do so here.)
If you’ve been anywhere near the AI industry for the past three years, the feeling you’ve probably had was that of living in a split-screen reality. In one window, labs shipping ever more capable foundation models, each launch framed like a moon landing. In the other: the familiar reality of a mostly AI-less workplace, with maybe a chatbot feature added to a tool you already hated. The result has made even the more AI-pilled insiders scratch their heads and ask: how can the technology feel so futuristic, and daily life feel so unchanged?
The explanation, in my view, is that we’ve spent all this time building a very thick infrastructure layer and a very thin application layer. Models got big but everything around them stayed small. The hard, unglamorous work of mapping models onto specific business processes, integrating with legacy systems, enforcing governance and compliance, redesigning workflows so humans and machines don’t trip over each other has been the bottleneck, with only a few companies brave (or capable) enough to take it on. When the application layer is missing, impact gets trapped where the friction is lowest: demos, hackathons, experimental teams, and the occasional department head who duct-tapes a workflow together and calls it digital transformation.
However, over the last year, the application and orchestration layers have been filling in at speed. There’s a growing ecosystem of companies building vertical AI software that doesn’t merely answer prompts, but actually runs chunks of work. And once you have enough of that scaffolding, the story stops being about what a model can do in theory and starts being about how an organization defaults to getting things done.
In practice, that means 2026 is the year AI stops being a tool employees sometimes open, and becomes the default path work takes through a company. Not because executives suddenly become enlightened, but because the path of least resistance changes. If the AI-native workflow is faster, cheaper, and safer than the manual one, it becomes the new normal the same way cloud software did: quietly, then all at once.
The clearest shift will be from one-off prompts to end-to-end workflows. Right now, most corporate AI use is still a string of little favors: draft this email, summarize that meeting, suggest a block of code, rewrite this slide. Helpful, sure, but it’s the productivity equivalent of buying a better stapler. In 2026, more companies will hand models ownership of entire low-risk processes, with humans supervising exceptions rather than executing every step.
That’s also where the first real productivity gains arrive. Not the cute kind (“ChatGPT saved me a few minutes on drafting a Slack message”) but the kind that shows up as cycle time collapsing, queues shrinking, and managers realizing they didn’t hire three extra people this quarter because the work simply didn’t accumulate. Companies don’t get measurably more productive because employees write faster. They get more productive because work moves through fewer bottlenecks, fewer handoffs, and fewer rework loops. End-to-end workflow ownership is the difference between AI as autocomplete and AI as operations.
As this becomes normal, job content will mutate in ways that feel obvious only in hindsight. I’m skeptical that 2026 is the year of mass unemployment. But I do think it will be the year when job descriptions start to read like they come from a slightly different economy. Analysts spend less time constructing models in Excel and more time interrogating AI-generated scenarios, pressure-testing assumptions, and deciding what to believe. Sales and support reps spend less time typing and more time handling nuance: negotiation, relationship management, and the messy edge cases that don’t fit the template. Operations teams shift from “do the process” to “design, monitor, and tune the process the AI runs.”
You can already see the shape of this in places you wouldn’t necessarily expect. Consider sell-side research: the regulated, prestige-heavy world of analyst reports and client communications. One argument goes that the written report is the first thing to automate, while the higher-touch one-on-one interactions survive longer because they rely on trust, context, and human nuance. And yet even there, firms are experimenting with turning analysts into AI avatars to scale video summaries, offloading the performative, repetitive part so humans can focus on the higher-value work.
Which brings me to the new career superpower for 2026: managing workflows and systems, not just tasks. For a decade, the status game for individual contributors involved in office work has been about output: how fast you respond, how clean your slide deck is, how many tickets you clear. In an AI-default organization, what matters more is whether you can design a process that keeps quality high while throughput increases, whether you can set guardrails that make automation safe, and whether you can diagnose failures without melting down the whole pipeline. The winners won’t be the people who “use AI.” They’ll be the people who can make AI reliably do something useful inside the constraints of their business.
A second-order consequence is that small and midsize businesses get enterprise-grade leverage “for free.” Big companies have been piloting AI, hiring AI leads (or “chief AI officers”), and paying consultants to tell them what they already suspect: their data is messy and their processes are worse. Smaller firms, meanwhile, have mostly been stuck with generic tools that help a bit but don’t change the game. As the application layer matures, a small team using AI-native tools will be able to do things that recently required a whole operations department: automated outreach, first-line customer support, basic forecasting, content production, even light finance workflows. That levels the playing field in a way incumbents rarely enjoy. It also puts pressure on mid-tier companies that don’t have the brand moat of giants or the agility of startups: the ones most likely to say “we’re using AI” while still running 2016 processes underneath.
Basically, 2026 is the year when we finally see differentiation. For the last couple of years, “we’re using AI” has been as meaningful as “we have a website.” Everyone says it. It tells you almost nothing. In 2026, the gap in outcomes will widen enough that you can spot it from the outside. Companies that actually rebuilt workflows around AI will ship faster, respond to customers faster, and run leaner teams without feeling understaffed. Companies that stapled an AI chatbot onto the front of the old process will wonder why nothing really changed, except that their customers now have a new way to get frustrated.
The technology will keep improving, of course. Models will get cheaper, faster, and more capable; AI systems will feel less like party tricks and more like infrastructure. But the competitive advantage won’t come primarily from having the slightly better model. It will come from execution: who integrates well, who governs data responsibly, who redesigns work instead of sprinkling prompts on top of it, and who treats AI in the business as an operating model rather than a feature.
And now, here are this week’s news:
❤️Computer loves
Our top news picks for the week - your essential reading from the world of AI
MIT Technology Review: What’s next for AI in 2026
Startup Coalition: The AI Index
Business Insider: Microsoft reshuffles teams to bolster GitHub as AI coding and agent wars heat up
FT: AI will free households from chores and boost hidden productivity, says OpenAI
Business Insider: A future without work? What Elon Musk, Bill Gates, and others in AI are saying about the future.
FT: AI start-ups take on Google in fight to reshape web browser market
WSJ: How Google Got Its Groove Back and Edged Ahead of OpenAI
Sifted: ElevenLabs founder Mati Staniszewski: ‘It’s not just about voice anymore’
The New York Times: Can A.I. Match Molière’s Wit? These Researchers Think So.
Wired: AI Models Are Starting to Learn by Asking Themselves Questions
