Why AI Startups Fail at Scaling Technical Teams (And How to Avoid It)
- Publish Date: Posted 1 day ago
- Author: Ben Greensmith, Senior Recruitment Consultant
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 problems? Culture is key! Don’t dilute your culture. It can be the difference.
3. The Specialized vs. Versatile Dilemma: Hiring hyper-specialists too early versus adaptable technologists who can evolve with your product. A lot of founders get this balance wrong at crucial growth stages. Do you want a jack of all trades or a master in one specific area? You’re going to need the specialists but bringing onboard the engineers that can turn their hands to anything and quicky pivot can really help a scaling business. The key is timing!
So how do you avoid these pitfalls?
Here are three simple approaches, I’ve advised on that consistently work:
1. Map out the talent and skill sets you need before scaling: Identify the specific technical capabilities you’re going to need at each growth stage rather than generic "ML Engineer" roles and work with your friendly neighbourhood AI specialist recruiter to really nail this down. You’ll hit the ground running and dodge the credential trap easily.
2. Try to structure your hiring so new technical talent overlaps with existing team members for better knowledge transfer and versatility. Getting new hires up to speed quickly is essential and blending skill sets can help the cohesion of the team.
3. Create a technical assessment framework that evaluates both specialized knowledge and adaptability. You want to be clear and concise with your interviewing and technical assessments. It’s great to be a specialist but we’re fighting fires and bulging at the seams here and we need people who can just run with it. Also, think deeply about the culture you want to keep/build in your expanding team and be strict with it. Don’t bring in the all-star if it’s going to tank your culture or you’ll be left high and dry right when you’re ready to push.
If you’ve run the gauntlet of early stages and stealth mode then there’s no reason why scaling your team should be any harder. Getting the right team in place as you scale is often considered a small consideration and when compared to things like cash flow it objectively is. However, getting this part right and having a real strategy to deliver it is massively advantageous.
If you’re sat there with a chunky Seed or Series A round and really don’t know where to go with it then get a talent strategy in place. It’s often why at this stage in their growth, founders speak to people like me or even get a dedicated talent function on board.
It’s a fundamental competitive advantage if done correctly and helps you avoid the AI scrapyard .
What scaling challenges is your AI startup facing right now? I'd be interested to hear in the comments.