20VC: Cohere Founder on How Cohere Compete with OpenAI and Anthropic $BNs | Why Counties Should Fund Their Own Models & the Need for Model Sovereignty | How Sam Altman Has Done a Disservice to AI with Nick Frosst
Episode
67 min
Read time
2 min
Topics
Career Growth, Productivity, Startups
AI-Generated Summary
Key Takeaways
- ✓Enterprise Model Differentiation: Cohere trains models specifically for enterprise tool use and business data integration rather than consumer engagement metrics, using synthetic data from fake companies, emails, and APIs to optimize workplace augmentation over conversational ability or entertainment value.
- ✓Scaling Law Limitations: Throwing more compute at models does not guarantee exponential progress, as evidenced by GPT-5's worse user experience with auto-selection delays. The industry still uses 2017 transformer architecture with minimal algorithmic changes, making data quality and product work more critical than raw compute power.
- ✓Efficient Model Training: Cohere trains models to fit on two GPUs, spending orders of magnitude less than competitors on foundational models. This efficiency addresses enterprise deployment bottlenecks where companies lack GPU access, making the sweet spot between performance, cost, and available infrastructure crucial for production deployment.
- ✓Benchmark Gaming Reality: Industry benchmarks like HellaSWAG and ARC AGI challenge do not reflect actual enterprise utility. Models can be trained specifically to perform well on benchmarks without improving real workplace value. Customer success depends on practical task completion, not leaderboard rankings or mathematical reasoning tests.
- ✓Forward-Deployed Engineering Value: Enterprise AI deployment requires forward-deployed engineers to customize models for specific business contexts, internal tools, and documentation. This approach is not poor technology but necessary infrastructure work, similar to how industrial revolution required labor policy alongside technological advancement to create sustainable productivity gains.
What It Covers
Nick Frosst, Cohere cofounder, discusses competing against OpenAI and Anthropic with enterprise-focused models, challenges Sam Altman's AGI predictions, explains why scaling laws have limits, and advocates for sovereign AI models and forward-deployed engineering approaches.
Key Questions Answered
- •Enterprise Model Differentiation: Cohere trains models specifically for enterprise tool use and business data integration rather than consumer engagement metrics, using synthetic data from fake companies, emails, and APIs to optimize workplace augmentation over conversational ability or entertainment value.
- •Scaling Law Limitations: Throwing more compute at models does not guarantee exponential progress, as evidenced by GPT-5's worse user experience with auto-selection delays. The industry still uses 2017 transformer architecture with minimal algorithmic changes, making data quality and product work more critical than raw compute power.
- •Efficient Model Training: Cohere trains models to fit on two GPUs, spending orders of magnitude less than competitors on foundational models. This efficiency addresses enterprise deployment bottlenecks where companies lack GPU access, making the sweet spot between performance, cost, and available infrastructure crucial for production deployment.
- •Benchmark Gaming Reality: Industry benchmarks like HellaSWAG and ARC AGI challenge do not reflect actual enterprise utility. Models can be trained specifically to perform well on benchmarks without improving real workplace value. Customer success depends on practical task completion, not leaderboard rankings or mathematical reasoning tests.
- •Forward-Deployed Engineering Value: Enterprise AI deployment requires forward-deployed engineers to customize models for specific business contexts, internal tools, and documentation. This approach is not poor technology but necessary infrastructure work, similar to how industrial revolution required labor policy alongside technological advancement to create sustainable productivity gains.
Notable Moment
Frosst directly criticizes Sam Altman for making obviously wrong predictions about AGI timelines and existential threats, arguing this world tour warning global leaders was academically disingenuous and damaged productive discourse about real AI risks like income inequality and workforce disruption rather than imagined digital gods.
Episode Transcript
I don't think Sam Altman has done a service to the world by talking about how close AGI is. I think he has made several predictions now that are wrong and that were obviously wrong at the time he made them. AI is gonna kill the whole world in two years. He did a world tour where he spoke to every major leader the world over to tell them, hey. This technology is gonna pose an existential threat. And I think that was academically disingenuous and I think did a disservice to the technology he loves. A lot of the world does not think scaling laws are super prevalent. This is 20 VC with me, Harry Stebbings. And today, we are joined by Nick Frost, Canadian AI researcher and entrepreneur, best known as the cofounder of Cohere, the enterprise focused LLM who has raised over $900,000,000, most recently raising a $500,000,000 round, bringing their valuation to 6,880,000,000.00. Today, we discuss how on earth they compete when competing against the billions of dollars that OpenAI and Anthropic have. Cohere has hit a 100,000,000 in enterprise ARR. And before founding Cohere, Nick was a researcher at Google Brain alongside the incredible Geoff Hinton. But before we dive into the show today, I love seeing the team come together to make this show happen. What I don't love is trying to keep track of all the information, the data, and the projects that we're working on across dozens of platforms, products, and tools. That's why we use Coda, the all in one collaborative workspace that's helped 50,000 teams all over the world get on the same page. Offering the flexibility of docs with the structure of spreadsheets, Coda facilitates deeper teamwork and quicker creativity. And their turnkey AI solution, the intelligence of Coda Brain, is a game changer. Powered by Grammarly, Coda is entering a new phase of innovation and expansion, aiming to redefine productivity for the AI era. Whether you're a startup looking to organize the chaos while staying nimble or an enterprise organization looking for better alignment, Coda matches your working style. Its seamless workspace connects to hundreds of your favorite tools including Salesforce, Jira, Asana, and Figma, helping your teams transform their rituals and do more faster. Head over to coda.io/20vc right now and get six months off the team plan for startups for free. That's coda, coda,.io/20vc and get six months off the team plan for free, coda.io/20vc. And while Coda keeps the engine running smoothly, let's talk about Brex, the ultimate financial stack for start ups. So when Brex was founded, it wasn't just about creating another financial product. It was about solving the really gritty challenges that founders face daily. Let's be honest. Building something from the ground up is hard enough without dealing with clunky outdated banks that pile on fees and leave your cash idle. Brex is different. It's the financial stack that scales with you no matter where you are in your journey. From …
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