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According to the Founders Factory (AI4SP)​ 30% to 40% of VC-backed startups fail, so they cease operations and provide no value to investors. However, AI startups have a much higher failure rate, with 90% failing to survive within their first year.​ Now, there are obviously many factors that contribute to this statistic, running out of cash being the main one… However, another glaring point of failure that I’ve seen time and time again are excellent founding teams hitting that critical growth wall, poorly scaling their team and ending up on the AI scrap yard. ​ Failure to get this fundamental issue right frequently leads to: Delayed product launches, not being able to get that next crucial round of funding, technical debt, cultural breakdown and in the worst cases, complete product failure (i.e. off to the scrapyard.ai) I’ve found that AI founders excel at building impressive MVPs, but the skills needed to scale technical teams are and will always be fundamentally different. ​ Here are Three Key Failure Points I’ve seen when building out teams that you might want to avoid: 1. The Credential Trap: Over-indexing on academic credentials rather than practical implementation skills. In a cutting edge industry like this, academic qualifications definitely have their place, but scaling requires builders who can ship production-ready AI products. Don’t get too hung up on the Masters and PhDs each candidate has, or time served, you want to set up a system that attracts and recruits the builders and doers. 2. The Culture Dilution: Growing too quickly without embedding technical culture leads to disconnected teams. When your ML engineers and deployment teams aren’t on the same page, your product quality suffers. Slow it down and get the culture right first. This goes beyond just technical culture and into forming cohesive teams that embody your philosophy. Whether you’re distributed around the world or in a We work in central London 5 days a week smashing out AI prob

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