From a pure AI perspective, nothing Apple showcased during their WWDC keynote yesterday was particularly groundbreaking. In fact, much of it featured capabilities long since available in other AI tools and services – in some cases, years ago. And guess what? That doesn’t matter. Based on what we saw yesterday, Apple is set to win in AI. At least from a consumer perspective.

I know how crazy this sounds. It’s not just that Apple has been viewed as behind in AI for the past few years, it’s that they’ve been more or less a laughingstock given how they tried to roll out ‘Apple Intelligence’ two years ago and failed to the point of settling lawsuits around false advertising. But if Apple is actually able to roll out what they showcased yesterday – I’ll get to the caveats below – and there’s reason to believe they can this time, they’re about to infuriate many people and companies across a wide swath of industries. That’s because Apple seems on the verge of doing what they always do: watching new products and services come about and then jumping in later with a better user experience to win the day. — Read More

Frontier AI systems are closing the gap between model intelligence and real-world utility. New models, compute architectures, and orchestration patterns are enabling these systems to accomplish tasks deemed impossible just a few months ago.

This rapid innovation has proved a boon to AI users by magnifying their leverage and agency. Yet it has also created a lag between the technological frontier and our understanding of precisely how knowledge work is evolving in response. How does frontier AI change the nature of knowledge work across professions? Which structural and economic transformations in this work might we expect?

… This article presents the highlights from our study. Detailed methodology and findings are available in our technical report. — Read More

Product Discovery has two modes:

— Exploring
— Exploiting

Most teams are great at exploiting. But few teams are really good at exploring.

Exploring what’s possible. Gathering insights that shape their product strategy and uncover untapped opportunities (potentially whole new products!) — Read More

For most of AI’s history, humans drove every step in its development cycle. But at Anthropic, we are delegating a growing share of AI development to AI systems themselves, which is speeding up our work.

Taken far enough, and given enough compute, that trend points to an AI system capable of fully autonomously designing and developing its own successor. This is called recursive self-improvement. We are not there yet, and recursive self-improvement is not inevitable. But it could come sooner than most institutions are prepared for.

Using public benchmarks and previously unreported data from within Anthropic, The Anthropic Institute is showing that AI is already accelerating the development of AI systems. To take just one example: today, Anthropic engineers on average ship 8x as much code per quarter as they did from 2021-2025.

The technical trends discussed in this piece suggest that AI systems are going to become much more capable in coming years. — Read More

There was a stretch of time when writing code felt like holding a lottery ticket that kept winning. Companies couldn’t hire fast enough. Recruiters spammed your inbox like it was a contest. Compensation packages looked unreal, and nobody wanted to say out loud that it might not make sense for the long run.

If you were in the industry then, you remember what it felt like. You could jump teams, switch companies, negotiate without sweating. The whole thing ran on this belief that the future was obvious and we’d just ride it forever. Infinite growth. Infinite headcount. Infinite snacks. Even the office plants seemed optimistic.

That version of the industry is gone. Nobody issued a statement about it. It just faded out while everyone pretended not to notice. —  Read More

The largest debate that’ll define the future balance of power between the open and closed AI model ecosystems is primarily economic — it’s if users of AI will continue to pay dramatically more, i.e. large margins, for the top closed models. Early 2026 is a seminal time for the AI industry, as the coding agents1 have shown the first area where a huge AI market will continue to pay a substantial premium for better intelligence.

The other side of this dichotomy is the inevitable decay of API businesses at these same labs. These labs will realize they need to protect their best models, rolling them out later in APIs to both protect token supply, avoid distillation, and stick to use-cases with higher margins. All of these effects will be clearly visible in 5-10 year timelines, as in the near term markets, prices, margins, and demand will be dictated by a rapid buildout of compute (supply-limited in the near term) and mass subsidization of tokens (through continued investment in new AI companies). — Read More

There’s a new paper making the rounds called “The AI Layoff Trap”, by Brett Hemenway Falk and Gerry Tsoukalas, and it arrives with a headline that’s catnip for anyone looking to wave off basic income. Among the policies that can’t fix the problem of AI-driven layoffs, they list universal basic income. Right there in the abstract, sitting next to capital income taxes and upskilling and worker equity. Only one tool makes the cut: a Pigouvian tax on automation.

I want to take this paper seriously, because the core idea is genuinely good. And then I want to show you exactly where it goes wrong about UBI, because it’s the kind of error that’s easy to miss. It’s simply a flawed assumption. — Read More

I know that among students and professionals, there is a lot of chaos and confusion regarding the future of the job market. There is no clear direction. Some people say AI will not take jobs, but then layoffs happen, which creates even more confusion.

In this article, I will share three data-backed predictions about the future of the job market, especially for software engineers and freelancers.

Please read carefully — I will explain in detail why I am making these predictions. — Read More

Executives have long relied on simple categories to frame how technology fits into organizations: Tools automate tasks, people make decisions, and strategy determines how the two work together. That framing is no longer sufficient. A new class of systems — agentic AI — complicates these boundaries. These systems can plan, act, and learn on their own. They are not just tools to be operated or assistants waiting for instructions. Increasingly, they behave like autonomous teammates, capable of executing multistep processes and adapting as they go. Notably, 76% of respondents to our global executive survey say they view agentic AI as more like a coworker than a tool.

For strategists, agentic AI’s dual nature as both a tool and coworker creates new dilemmas. A single agent might take over a routine step, support a human expert with analysis, and collaborate across workflows in ways that shift decision-making authority. This tool-coworker duality breaks down traditional management logic, which assumes that technology either substitutes or complements, automates or augments, is labor or capital, or is a tool or a worker, but not all at once. Organizations now face an unprecedented challenge: managing a single system that demands both human resource approaches and asset management techniques.

The separation of technology and strategy inside most organizations exacerbates this challenge.  — Read More

Three kinds of companies come to me for help with AI. While they are all in different places on their AI-path, they all have the same underlying challenge: how to effectively understand and manage business risk for systems that contain AI-based components.

The first kind of company is on the outside looking in. … The other two kinds of companies are already on the inside, with different problems. One built a working proof of concept, [t]he other already crossed that bridge, shipped something real, and got burned. … None of these companies can see their actual business risk surface clearly enough to make decisions about it. — Read More