George Sivulka

Sivulka’s core market call: all AI companies are undervalued — and so is the S&P 500. His logic: if the computer created ~$100 trillion of stock-market value over 60 years, AI compute will create another $100 trillion over the next 60, with “more than 50% of GDP contributed by agentic applications in the next few decades” — while Harry thinks it happens faster than that. It’s additional value, not cannibalized value, and it lifts non-AI incumbents too: “computers made legacy businesses better if you use them correctly.”

The spiciest relative-value take: at OpenAI ~160, Anthropic ~40, xAI ~50, “xAI is the most undervalued company” — and it “might overtake OpenAI and Anthropic in value over the next 12 to 24 months.” His reasoning is Elon’s geopolitical positioning, operational talent, and a leaner business with less “administrative bloat” — plus a belief that governments are among the largest AI users.

The model layer will become commoditized; value will accrue at hardware and the application/agent layer. Nvidia’s real moat is people (CUDA-trained ML PhDs), which holds for training but less so for inference — so the macro shift from training to inference “will destabilize slightly the dominance of Nvidia chips.” His public-markets pick for the AI wave: “I would probably buy Nvidia… or AMD rather,” because AMD benefits from inference scaling “in an outsized way.”

From the man who first productionized RAG in 2020: “we actually don’t think RAG works at all.” ~90% of real enterprise queries aren’t findable in documents — they’re about documents (“is this company a good investment”) — and “90% of enterprise AI right now is almost like this vapor… fugazi fugazi,” including a likely Klarna staff-cut story (“I think it’s BS… an amazing marketing story”).

His replacement thesis, and what the $130M from Index and Thiel funds: scaling laws at inference — run hundreds or thousands of sub-model calls over every document rather than waiting for bigger models. The proof-by-anecdote: BloombergGPT, trained on “the best financial services training set of all time,” was destroyed “at every single finance task” by GPT-4 a few weeks later, though he said he didn’t know the exact timeline — verticalized fine-tuning “will ever catch up” to scaling, never.

Against the slow-enterprise-adoption consensus (his neighbor Daniel Dines’), he cites Excel hitting 90% penetration in finance in 18-24 months (1985-86, off the HP12C): finance is “the slowest moving, most lethargic leviathan… unless you’re providing outsized alpha, in which case finance moves faster than any other industry.”

Quickfire tells worth keeping: he would not sell Hebbia for $2B today, answered “no” flat when asked if he trusts Sam Altman, disagrees “completely” with SaaS’s claim that business apps collapse into agents, and thinks chat is the wrong interface — “it’s like asking if the TI-84 was the right interface for computers.”

🔗 Original source & video: George Sivulka, Co-Founder & CEO @Hebbia: The Future of Foundation Models | E1250