Matt Fitzpatrick

The core call: enterprise AI adoption is “a decade, not two years.” Models improved 40–60% on public benchmarks in two years and KPMG says 60% of consumers use genAI weekly — yet MIT finds only 5% of genAI deployments working in any form and Gartner sees 40% of enterprise projects canceled by 2027. Fitzpatrick’s diagnosis: the bottleneck is data infrastructure, workflow redesign, ownership and “most importantly, trust” — banks will run genAI through model-risk-management-style validation first, the rest of the enterprise five to six years after.

“You just cannot do this with out-of-the-box SaaS.” Invisible sells nothing upfront — free 8-week solution sprints, payment only at user acceptance testing, and zero charge for forward-deployed engineers (450 people, 8 offices). The structural point for SaaS investors: “the minute you had to bring in FDEs in a SaaS context, your economics broke instantly,” and Fitzpatrick argues out-of-the-box software “has always been a lie to some degree” — the endgame is hyperpersonalized software, not boxes.

Internal builds are losing: MIT’s data shows externally driven builds are 2x as effective as internal ones. Exhibit A: an e-commerce retailer spent $25M on a returns agent, built its own eval on call-resolution speed plus sentiment (a hallucinated “$2 million refund” scores perfectly), then shut it down and reverted to a deterministic flow. His fix: three to four initiatives, led by operational leaders — “don’t locate it in the tech function” — paid as it works.

The biggest industry misnomer: synthetic data will not replace human feedback. Synthetic works for base-truth domains like math; multi-step reasoning across 45 languages and multimodal contexts is “in the first inning,” and legal-grade data sits inside big law firms, not public corpora. Unlike ML, genAI requires humans in the loop for statistical validation — “you are going to need humans in the loop for decades to come.”

Data labeling shakeout: 3–5 players, not winner-take-all. Concentration is structural (few LLM builders exist), but “people are willing to pay for good data” because one week of bad data burns enormous compute; claims of total price insensitivity are “an exaggeration.” The moat is Helmer-style institutional memory — a digital assembly line that sources 26,000 selected experts on 24 hours’ notice — and yes, he insists the big numbers are revenue, not GMV.

Capital posture flipped: Invisible raised only $7M primary in nine years; it has now raised $130M and will not be profitable this year — “the greatest environment for growth that has ever existed… I hope we never get to the harvest stage.” Next frontier: physical-world data (FDEs dropping Starlink terminals on farms for herd-safety computer vision) and robotics.

Agent skepticism, quantified: Salesforce AI research puts out-of-box agents at 58% accuracy single-turn and 33% multi-turn; AWS reported 70% of “agents” are traditional scripting. His contrarian allocation for a $400M fund: look beyond out-of-box agents and back AI-native businesses that serve the customer need directly — YC’s recent class, he thinks, did 2x the revenue of any prior class doing exactly that.

🔗 Original source & video: Matt Fitzpatrick: Who Wins the Data Labelling Race & Why Al Needs Forward-Deployed Engineers