Suma Krishnan, Founder & President of Krystal Biotech, which is developing genetic medicines for rare diseases, on using AI to help automate business and manufacturing processes. ⁣ From the @america250 x Forbes America Innovates stage.⁣ ⁣ #Forbes250# #A250#

An interview with Sriram Krishnan, who says "there will not be an FDA for AI" under Trump, blames the AI backlash on the industry's "doomer" messaging, and more (Financial Times) (Visit Techmeme dot com for the link and full context!)

📹WATCH: Anthropic CFO Krishna Rao's First Podcast Appearance Krishna reveals how Anthropic uses Claude internally to make business process effective including automating financial statements and monthly reviews that are now reportedly “90% complete” before human oversight. Rao added that Anthropic’s Head of Tax has become the company’s biggest Claude power user, using the model to help build an internal tax policy engine.

Anthropic CFO Krishna Rao: "People tend to think about model intelligence as IQ, a single number. We think of it differently. Intelligence for us is multi-dimensional." "It's not just a score. We started the year with about $9 billion of run rate revenue and ended the quarter with north of $30 billion." "That kind of change is really enabled by these model intelligence leaps. That's what I mean by the returns to frontier intelligence are really high." credit @InvestLikeBest

Vals AI co-founder and CEO Rayan Krishnan says investment in testing and evaluating AI hasn’t kept pace with the rapid improvement in model capabilities

Vals AI co-founder and CEO Rayan Krishnan with a16z's Ben Horowitz and Jennifer Li on grading AI, what it costs, and who gets to make the rules: Every big industry eventually grows an independent testing layer. AI has credit ratings to learn from and Enron to avoid. Model capability today is still mostly self-reported. As public benchmarks saturate and models get better at optimizing for the tests themselves, Rayan makes the case for independent, continuously evolving evaluations. The harder problem is geopolitical. Reagan's "trust but verify" worked during the Cold War because you could fly over and count the missiles. No simple equivalent for AI models exists. In this conversation with Erik Torenberg, they get into how you measure a model's ability to improve itself, why every good benchmark eventually has to be retired, and what happens when token spend begins to rival employee salaries. 00:00 Intro 02:20 Llama 4 on public vs private benchmarks 05:24 Nobody agreed how to test humans either 06:55 What movie ratings teach us about AI 08:55 The Enron problem in benchmarking 11:36 Why a good benchmark has to be retired 13:22 Evals that run for weeks, not seconds 16:20 Where the real workday starts at 4pm 18:08 A firm really is just its evals 20:35 Why Sonnet can cost more than Opus 22:42 One engineer, 6 billion tokens in a day 25:05 Who should set the rules for models 28:55 Public sector enforces, private verifies 33:32 Why sovereign AI is inefficient and happening anyway 35:00 The AI version of trust-but-verify 37:15 Where cyber evals have to go next YouTube: @RayanKrishnan @ValsAI @bhorowitz @JenniferHli @eriktorenberg