Sarah Guo refutes AI investment despair: what can be quantified will eventually be cheap, and private correctness is the true moat
Sara Guo refutes the “AI investment hopelessness” claim: quantifiable work is rapidly being commoditized, while the premium is shifting toward implicit work with high verification costs and private correctness. Taking software as an example, in 2024 only 13% of evaluation tasks were solved, and top-tier intelligent agents in swe-bench surpassed 80%; MIT said programming output increased by 180%, while real-world integration only increased by 30%. When you code into cheap commodities, the only way to ensure that complex systems run in reality is to have a moat for capital. Models cannot sign for responsibility—this is the investment logic of Baseten, OpenEvidence, and Harvey. The Conviction Fund invests in 27 companies, with 6 AI unicorns whose combined valuation totals over $620 billion.
