Sivesh and I recently hosted a roundtable on the future of software engineering with Anthropic’s Ash Prabaker and we were joined by engineering leaders from Stripe, NVIDIA, Microsoft, Google DeepMind, xAI, Apple, Scale AI, as well as the legend Peter Steinberger of OpenClaw/OpenAI.

… A major thread throughout the discussion was “closed-loop” development. One participant described a setup at their company where bug reports are automatically triaged by an agent, bucketed by severity, checked against an eval set, and then a fix PR is opened — much of it running with minimal human touch. The room broadly agreed that this kind of loop is where compounding gains actually come from: better coding tools improve the models, better models improve the coding tools. Several people noted their companies are prioritizing coding specifically because of this dynamic.

… The room converged on long-horizon tasks as the real frontier problem. One participant noted that product engineering has started to go exponential for them, but closing the loop on more complex research workflows isn’t there yet. The open questions everyone shared: what do you actually assign an agent for a four- or five-hour run? How do you observe it? How do you keep a human in the loop without babysitting? Nobody had a clean answer. — Read More

Language models can solve tough math problems at research grade but struggle on simple computational tasks that involve reasoning over many steps and long context. Even multiplying two numbers or solving small Sudokus is nearly impossible unless they rely on external tools.

But what does it take for an LLM itself to be as reliable and efficient as a computer?

We answer this by literally building a computer inside a transformer. We turn arbitrary C code into tokens that the model itself can execute reliably for millions of steps in seconds. — Read More

Ask an AI coding assistant to, say, “build a React component with a dropdown menu,” and you’ll probably get something impressive in seconds—clean code, proper hooks, accessible markup. It’s the kind of demo that makes CTOs lean forward in their chairs.

Now ask that same AI about your company’s internal API for user authentication. Ask it to integrate with your legacy billing system. Ask it why your team deprecated a particular approach last quarter. Watch it hallucinate with confidence, suggesting endpoints that don’t exist, recommending patterns your architecture explicitly forbids, and generally ignoring the hard-won institutional knowledge that makes your systems actually, you know, work.

This is the enterprise AI paradox: Foundation models know everything about public libraries but precious little about the specifics that matter for your business. They’re trained on millions of open source repositories, but they’ve never seen your codebase. They can regurgitate best practices from popular engineering blogs, but they fail to grasp why those practices might be impossible in your environment. Without context—the community-vetted, institutional knowledge behind business decisions—AI assistants remain dangerously confident when they shouldn’t be. — Read More

In 2025, Google, Amazon, Microsoft and Meta collectively spent US$380 billion on building artificial-intelligence tools. That number is expected to surge still higher this year, to $650 billion, to fund the building of physical infrastructure, such as data centers (see go.nature.com/3lzf79q). Moreover, these firms are spending lavishly on one particular segment: top technical talent.

Meta reportedly offered a single AI researcher, who had cofounded a start-up firm focused on training AI agents to use computers, a compensation package of $250 million over four years (see go.nature.com/4qznsq1). Technology firms are also spending billions on “reverse-acquihires”—poaching the star staff members of start-ups without acquiring the companies themselves. Eyeing these generous payouts, technical experts earning more modest salaries might well reconsider their career choices.

Academia is already losing out.  — Read More

AI just made every individual 10x more productive.

No company became 10x more valuable as a result.

Where did the productivity go?

This isn’t the first time this has happened.

In the 1890s, electricity promised enormous productivity gains.

Textile mills in New England, built to harness the rotational power of steam engines, quickly installed faster electric motors in their place.

But for thirty years, electrified mills saw almost no increase in output. The technology was far superior. But the organization was not.

It wasn’t until the 1920s, when factories completely redesigned the mills once again, with assembly lines, individual motors within every piece of equipment, and workers and machines executing drastically different jobs, that electrification produced meaningful returns. — Read More

Over the past few months, a new phrase has been circulating across tech, venture capital, and public markets:

“The SaaSpocalypse.”

The narrative is straightforward, and a bit alarming for SaaS operators. What’s real and what’s clickbait?

We know this. AI agents are improving rapidly. Coding tools can generate entire applications. AI can automate workflows once performed inside SaaS products.

If software can now be generated on demand, the logic goes: why pay recurring subscriptions for SaaS at all? — Read More

Executives are increasingly enamored with the promise of an AI-driven transformation and have invested accordingly. Most large-scale companies have initiated hundreds of pilots and provided widespread access to tools like Copilot and ChatGPT.

But while many of these pilots have succeeded individually—they’ve saved time and money, made processes more efficient—those gains haven’t scaled across the organization. Few companies have been able to fundamentally change their operating and business models around AI. — Read More

Three years ago, we published the first edition of this list with a simple goal: identify which generative AI products were actually getting used by mainstream consumers. At the time, the distinction between “AI-first” companies and everything else was clear. ChatGPT, Midjourney, and Character.AI were purpose-built around foundation models. The rest of the software world was still figuring out what to do with the technology.

That distinction no longer holds. …From this edition onward, we’re broadening the aperture to include any consumer product where generative AI has become a core part of the experience — including CapCut, Canva, Notion, Picsart, Freepik, and Grammarly. The result is what we believe is a more accurate picture of how people actually use AI, though the bulk of the top products continue to be AI-native. — Read More

The rapid diffusion of AI is generating a wave of research measuring and forecasting its impacts on labor markets. But the track record of past approaches gives reason for humility.

… In this paper, we present a new framework for understanding AI’s labor market impacts, and test it against early data, finding limited evidence that AI has affected employment to date. Our goal is to establish an approach for measuring how AI is affecting employment, and to revisit these analyses periodically. This approach won’t capture every channel through which AI could reshape the labor market, but by laying this groundwork now, before meaningful effects have emerged, we hope future findings will more reliably identify economic disruption than post-hoc analyses. — Read More