TL;DR
Rauch said an AI-accelerated attack exposed Vercel’s weak link, while Kothari warned AI will supercharge attacks too. Garry Tan called Claude Code the new app factory, and Peter Yang noted agents still flaked on boring cron jobs.
He says a Vercel employee was compromised through a breached AI platform customer account, then the attacker used that foothold to reach Vercel environments. The company thinks the group was highly sophisticated — and possibly sped up by AI — but says customer impact looks limited and it’s already pushing new env-var security controls plus secret rotation guidance.
AI productivity gains won’t replace most roles so much as raise the bar: when everyone gets better tools, the job itself expands. He argues the engineer, paralegal, editor, and analyst of the future will be judged on bigger, more complex work — not the old baseline tasks.
She argues product teams should spend way more time talking to users and customers, because AI is making execution cheap while problem selection gets more important. With smaller teams and fewer internal meetings, the real edge becomes understanding the problem — then handing the messy implementation to agents.
Cybersecurity is headed for a bigger market because attack volume will keep rising as model capabilities improve. The real weak point stays the same: humans, which means infra providers and security teams are about to get a lot more pressure.
He says OpenClaw + GPT couldn’t reliably handle a simple weekly stats recap email, even after a lot of back-and-forth and model switching. The takeaway is blunt: agentic workflows still feel brittle for mundane follow-through, and he’s hoping GPT-5.5 or similar finally makes them dependable.
He jokes that VCs should do more diligence, but software has gotten so abstract — first serverless, now headless — there’s less surface area left to inspect. The real point: in a world of thinner infrastructure, investors need sharper judgment, not just more checklists.
He says he spots a need in his own workflow, has Claude Code build it, then ships it open source. The latest example is GStack v1.4, which adds a new /make-pdf skill and is built to work well with OpenClaw/Hermes and Claude Code as a tool. He’s also pushing OpenClaw to replace crons and subagents where possible, with better plugin APIs as the real fix.
He argues that sounding bad at PR can actually make people trust you more, because it reads as less polished and less deceptive. He also frames some press releases as basically asking for a fake choice — like picking a red or green Lambo instead of questioning the purchase itself.
OpenAI is betting on longer-horizon autonomy, not just smarter chatbots
The Takeaway: The real frontier isn’t chatty AI — it’s models that can work for days, verify progress, and discover things.
Ako Paioki, OpenAI’s chief scientist, sounds less interested in hype than in the mechanics of making AI useful. His view is that coding tools like Codex are a signal, not the destination: OpenAI already uses them for most actual coding, and he expects the pattern to extend into research workflows. The same goes for math. Benchmarks like IMO problems mattered because they were a clean North Star — “Math is very measurable,” he says — but the deeper value was training models to reason over long, difficult, verifiable tasks.
That’s why he keeps returning to horizon length. A model doesn’t need to be told “go solve alignment” tomorrow; it needs to get better at making partial progress on a long project, checking itself, and staying useful over time. He thinks RL will matter beyond code, but not as a copy-paste of today’s pipelines. The bigger shift may be models that adapt through context and existing interfaces — Slack, tools, workflows — rather than forcing companies to build bespoke harnesses around them.
The most revealing line: “We are no longer really purely building brains in the sky.” The message is clear: the company is optimizing for models that can touch the real world, accelerate research, and eventually become collaborators, not just assistants.
Rauch said best product wins and AI’s just the lever. Thibault Sottiaux called GPT-5.5 the best model yet, while Claude’s Managed Agents added dreaming, outcomes, and orchestration. Peter Steinberger noted agent prompts now ran for hours, not minutes.