AI companies are building essentially the same technical solution with essentially the same data, so there is no moat.

This is judged as of June 2026 and has two parts. (a) Leading AI companies build essentially the same technical solution with essentially the same data: broadly similar transformer-based LLM approaches trained on overlapping web-scale data. (b) As a result, no company has a durable competitive moat. TRUE: evidence shows both of the following. First, frontier labs use broadly similar architectures and data. Second, no lab keeps a lasting edge: rivals and open-weight models reach near-parity on benchmarks within months, prices are competitive and falling, and customers readily switch providers. MOSTLY TRUE: both parts hold broadly, but some firm has a modest, narrow edge. MIXED: the technical and data sameness is well supported, but evidence on moats is split. For example, capabilities are similar, yet some firms show strong, persistent advantages in distribution, user lock-in or proprietary data. MOSTLY FALSE: the approaches are similar, but clear evidence shows durable moats exist (sustained capability lead, proprietary data, or entrenched market share), so the 'no moat' conclusion overstates. FALSE: leading companies use clearly different technical approaches or materially different data, and one or more holds a clear, lasting advantage.

Set before the research began.

1. E1 Competition in Generative AI: Evidence from Consumer Demand Harvard Business School Working Paper 27-037 (Egan, Russel, Shi, Yao) · 16 October 2026

   “Model capabilities vary substantially over time but relatively little across providers at a given date, showing that AI providers’ frontier models have thus far advanced at similar rates.”

2. E2 Jiaxin Zhang | How Frontier Labs Train Large Language Models Jiaxin Zhang (personal blog) · 27 June 2026

   “Read them side by side and the striking thing is not how different the labs are, but how convergent they have become.”

3. E3 Two Loops: How China's Open AI Strategy Reinforces Its Industrial Dominance U.S.-China Economic and Security Review Commission (Research Working Group Paper, Ngor Luong) · 23 March 2026

   “Beyond publicly available data scraped from the internet, leading U.S. frontier labs rely on proprietary datasets, including enterprise data streams like search results and social media feeds as well as specialized collections like scientific publications.”

4. E4 Senior execs worry about AI system lock-in CIO Dive (Informa TechTarget) · 26 June 2026

   “More than 7 in 10 respondents said it would be challenging to switch from their primary AI provider.”

E1 finds that frontier model capabilities vary little across providers at a given date and have advanced at similar rates. E2 describes frontier labs' training approaches as strikingly convergent, which supports technical sameness. E3 says leading U.S. labs also rely on proprietary datasets beyond public web data, which weakens the "same data" part. E4 reports that more than 7 in 10 surveyed executives would find it hard to switch AI providers, a sign of customer lock-in that cuts against "no moat." The evidence on moats is split, which matches the criteria's MIXED outcome.