🐝Jensen Huang's Answer to the AI Slowdown Debate: "We Don't Need Regulation"

PLUS: A tiny marketing startup just became a unicorn twice in seven months, and a report confirms OpenAI, Anthropic, and Google have been quietly negotiating AI safety standards for weeks.

Welcome, busybees

Nvidia CEO Jensen Huang stood on stage at Salesforce’s Dreamforce conference yesterday and said the quiet part out loud: “We don’t need any new laws. We don’t need new regulations.” It’s the sharpest, most direct rejection yet of the AI safety pause that Anthropic’s Dario Amodei proposed just days ago, one that both Sam Altman and Elon Musk publicly backed at the time. Huang’s argument: safety is “an engineering problem, not a legal one,” and market pressure alone will keep companies from shipping unsafe products.

Meanwhile, a much quieter story confirms something bigger might actually be forming behind the scenes.

Here’s what happened in AI today:

• Jensen Huang told a live audience the AI industry doesn’t need new laws, just days after rivals called for a safety pause.

• A new report confirms OpenAI, Anthropic, and Google have been negotiating a joint AI safety framework for weeks.

• A two-year-old marketing startup hit unicorn status twice in seven months as AI search reshapes how brands get found.

• Factory lands $200M from elite tier-1 institutions to replace individual developer bots with fully autonomous software pipelines.

…and a whole lot more that you can read about below.

• Jensen Huang’s Answer to the AI Slowdown Debate: “We Don’t Need Regulation

Speaking at Salesforce’s Dreamforce conference on Tuesday alongside CEO Marc Benioff, Nvidia’s Jensen Huang rejected the idea that AI companies need new safety laws to govern their work. “Safety is an engineering problem, not a legal one,” he said. “We’re developing software after all. We’re developing computing systems after all. It’s a complicated computing system, but it’s ultimately a computing system.” Huang argued the choice between innovation and safety is a “false choice,” and that market forces alone will push companies to pull back if a product isn’t ready. He framed AI as fundamentally different from how some safety researchers describe it, rejecting the idea that it’s some kind of new “alien mind.” The comments come a day after Huang took a live call from Trump on stage at the All-In Summit, where Trump called AI safety warnings “a hoax” and Huang responded, “You’re right. We’re not going to let that happen, sir.” Microsoft CEO Satya Nadella struck a notably different tone at the same summit, saying China should “deeply care about the same safety concerns” the US does.

• OpenAI, Anthropic, and Google Have Reportedly Been Talking About This for Weeks

A new report confirms what looked like a rare, spontaneous moment of agreement last weekend was actually the visible tip of weeks of quiet negotiation. OpenAI, Anthropic, and Google have been in discussions about a joint approach to AI safety standards, according to reporting that names OpenAI’s head of global affairs, Chris Lehane, as directly involved. The talks reportedly aim to establish shared practices around independent safety evaluation access, the same commitment Anthropic made unilaterally last week and that Sam Altman said OpenAI would match. Why it matters that this wasn’t spontaneous: a coordinated, weeks-long negotiation between three direct competitors is a far more durable signal than three CEOs reacting to each other’s social media posts over a weekend, even if the public version looked more dramatic.

• A Two-Year-Old Marketing Startup Just Became a Unicorn Twice in Seven Months

Profound, which helps brands understand and improve how they show up in AI-generated search answers, raised a $180 million Series D at a $1.8 billion valuation on Tuesday, less than seven months after a $96 million round first made it a unicorn at roughly $1 billion. Sequoia Capital and Kleiner Perkins co-led the round, with Lightspeed Venture Partners, Khosla Ventures, and South Park Commons returning as investors. Profound reports 3x revenue growth in six months and now counts more than 1,000 enterprise customers, including Comcast, Estée Lauder, and Walmart. The category it operates in, sometimes called AEO or GEO (answer engine optimization / generative engine optimization), didn’t meaningfully exist three years ago, and now has enough enterprise demand to double a startup’s valuation twice in under a year.

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Today’s Job Board

Staff Full Stack Engineer | Sully
United States (Full-time)
Build the autonomous AI agents that run entire hospital operations.

Senior Software Engineer | Method Financial
Remote (Full-time)
Build the financial connectivity API that helps consumers manage their liabilities.

Senior Software Engineer | Metriport
United States (Full-time)
Build open-source healthcare data infrastructure for next-generation care delivery.

Forward Deployed AI Engineer, Backend | Draftwise
West Palm Beach, FL (Full-time)
Build the contract and negotiation AI platform built for lawyers.

Software Engineering Intern | Sentient OS
United States (Internship)
Build on-device AI that knows your entire life and does your work overnight.

Senior ML / AI Engineer | Confido
United States (Full-time)
Build the AI-enabled financial automation and intelligence platform used by CPG brands.

Software Engineer | Candid Health
United States (Full-time)
Build the autonomous revenue cycle management platform used by healthcare providers.

Engineering Manager | OneChronos
United States (Full-time)
Lead engineering for the AI-matched smart market used by institutional investors.

Staff AI Engineer, Agent Architecture & Behavior | Artisan
United States (Full-time)
Build AI employees that take on real sales work.

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Today’s Money Moves 💸

AI Coding Factory Platform Triples Valuation to $5 Billion via a Massive $200M Check

Proving that automated developer ecosystems are drawing the largest growth checks in the application sector, San Francisco-based startup Factory confirmed a massive $200 million funding round. The transactional block was commanded by Blackstone and Khosla Ventures, with active check support from Sequoia Capital and Insight Partners. Moving beyond basic auto-complete software extensions, Factory builds automated multi-agent systems that autonomously build, test, and maintain enterprise software codebases. The deal underscores a massive workforce shift as global organizations like Coinbase and Block reallocate engineering budgets into autonomous software generation platforms

Harvey Hits a $15.5 Billion Valuation, Up From $11 Billion in March

Legal AI startup Harvey raised $550 million at a $15.5 billion valuation, co-led by Diffusion and Lightspeed Venture Partners, crossing $400 million in annual recurring revenue with more than 3,000 paying customers.

ByteDance Spins Off AI Drug Unit Anew Labs via a Monumental $290 Million Funding Round

As the global healthcare sector pivots toward automated biological profiling, deeptech infrastructure units are commanding premium standalone valuations. TikTok parent firm ByteDance officially finalized a staggering $290 million financing execution for its newly independent AI drug discovery wing, Anew Labs. The equity transaction was co-directed by major international investment trusts, establishing an initial post-money valuation moat of $1.5 billion. ByteDance will maintain a 56% controlling stake in the entity while deploying the independent capital pool to build specialized algorithmic models designed to predict molecular behavior and accelerate drug development timelines.

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Skill of the Day

Turn Your Own AI Corrections Into a Personal Benchmark

Public benchmarks measure how a model performs on generic tests nobody actually asked for. A better approach: turn the corrections you’re already making by hand into a benchmark built around your actual job.

Pick one task you do repeatedly, and save the original prompt along with any source files you used. Look at the first output and turn each correction you make into a simple yes-or-no check, something like “one idea per slide” or “no sentence over 20 words.” Grade the output yourself against that checklist, then have the AI grade the same output on the same checklist without seeing your scores first. Wherever the two of you disagree, tighten the wording of that check, then rerun the same test across a few different models. One team running this approach found a smaller, cheaper model actually beat larger ones on their everyday tasks, proof that the benchmark that matters is the one built around the cleanup you personally keep doing by hand.

Prompt of the Day

The Personal Benchmark Builder

Help me turn my repeated corrections on this task into a personal benchmark I can reuse.

The task: [describe the recurring task] The original prompt I use: [paste it] A recent output and the corrections I made to it: [paste both]

Give me:

1. A list of 5-8 yes-or-no checks based on the corrections I actually made, not generic quality criteria.

2. The exact wording for each check, specific enough that another AI grading blind would interpret it the same way I do.

3. One check I should test first for disagreement between my judgment and AI’s judgment.

4. A short scoring template I can reuse across different models or prompt versions.

That’s a wrap for today, busybees.

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