The AI boom of 2025 created one of the most competitive hiring markets the tech world had ever seen. Funding accelerated, product roadmaps expanded, and founders had one shared priority: building world-class AI capability as fast as possible.

But despite the opportunity, one challenge slowed progress more than anything else: AI founders struggled to hire the talent they needed, at the speed their roadmap demanded.

Throughout 2025, at Tides Digital, we worked with AI start-ups and scale-ups across the UK and Europe, and everywhere, the pattern was the same. Roadmaps were ready. Investment was available. Demand was strong.

The true bottleneck was talent.

Below is what actually caused those hiring struggles and the lessons founders are carrying into 2026.

1. There were more AI products than AI specialists

The explosion of AI companies in 2025 created a huge imbalance. Roles like Machine Learning Engineer, LLM Engineer, AI Researcher, Python Developer, Data Scientist and Voice AI Product Manager became some of the most in-demand positions in Europe.

AI founders were all competing for the same limited pool of talent who had:

• hands-on production experience

• experience shipping models end-to-end

• deep Python expertise

• experience with NLP, NLU and LLM pipelines

• the ability to scale models in real environments

Traditional recruitment didn’t work. Companies needed specialists who understood the domain and could engage deeply technical candidates with credibility.

2. AI engineers moved only for impact, ownership and clarity

In 2025, top-tier AI talent cared less about brand prestige and far more about meaningful ownership.
The hires who moved did so because founders offered:

• a clear product problem

• real autonomy

• access to strong datasets

• freedom to experiment

• a technical mission they believed in

When a company couldn’t articulate these clearly, conversion dropped dramatically — no matter the salary.

3. Slow hiring cycles killed momentum

One of the biggest reasons AI founders struggled to hire in 2025 was simply speed. The best engineers were in multiple processes within days.

We repeatedly saw:

• 3–4 week processes losing candidates

• founders stuck in long interview loops

• teams missing out to competitors who finished in 7–10 days

The companies that hired well were the ones that treated hiring like a sprint, not a sequence.

4. Internal parity blocked mission-critical hires

Even well-funded AI businesses hit the same issue:
market salaries for AI specialists had outpaced internal pay bands.

Founders had to choose between:

• paying above internal salaries (risking fairness challenges), or

• sticking to outdated bands and losing top candidates

Teams who benchmarked salaries every 6–9 months and flexed intelligently hired better, and kept internal trust intact.

5. The winners weren’t the biggest companies, they were the clearest

By the end of 2025, it was obvious that the companies hiring best weren’t the ones with the most funding, but the ones with:

• a sharp technical narrative

• a compelling mission

• a fast, respectful process

• strong partnership between founders and TA

• recruiters who genuinely understood AI and engineering

This blend of clarity, pace and credibility consistently outperformed brand recognition or compensation alone.

AI in 2025 didn’t suffer from a lack of ideas, funding or ambition.
It suffered from a shortage of the right people to build the future.

And the companies that recognised this early, and adapted, were the ones who made the biggest leaps.

If you’re scaling AI capability in 2026 and want insight into where the best talent actually was (and still is), we’d be happy to share what we’re seeing on the ground.