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                    "This one is short, but it\u2019s been bothering me for a while.A few months ago I stepped up as Head of Engineering, and with the role came the part of the job I\u2019ve always found the hardest: hiring. I\u2019m lucky to already work with great people. But in a startup there\u2019s a constant, quiet pressure to keep looking for talent, because the moment you actually need someone, you can\u2019t afford to wait three months for the perfect candidate.Skills aren\u2019t the whole storyI used to have grand ideas about meritocracy. People should be hired on their skills and nothing else. I couldn\u2019t have been more wrong. How you behave, how you relate to others, how you approach problems and the energy you bring to a team matter as much as your technical ability, and often more.From my admittedly anecdotal and narrow point of view, once you clear a baseline of competence, your willingness to do the work takes you much further than knowing a bit more. In a startup this makes sense. It\u2019s how small teams with limited capacity manage to take on giants with thousands of employees, companies that can outbid you for every engineer on the market.The blockers that have nothing to do with youThat\u2019s why it hurts when good engineers, the kind that are genuinely rare, can\u2019t join us for reasons that have nothing to do with them as engineers. They can\u2019t get a visa. They live in another city. The contract terms don\u2019t work for them.Lately, though, something else has been on my mind. I believe using LLMs is now an essential part of software engineering. So in our interviews and technical challenges, we expect candidates to use them.Now put yourself on the other sideImagine you\u2019re the one looking for a job. You have no income. Job hunting today is largely a numbers game, so you might be interviewing with five companies in a single week. And every one of them hands you a challenge where you\u2019re expected to use AI.AI isn\u2019t free. The tools that are actually good at coding need a monthly subscription or API credits. So you\u2019re paying for them at what might be one of the worst financial moments of your life.The usual response is: \u201cIt\u2019s always been like this. You always needed money to get a job.\u201d And that\u2019s true. You need housing, clothes and food just to be in a position to apply. But none of those exist only to get you hired. LLM costs are different. They\u2019re ephemeral, and their only purpose is getting the job.There\u2019s no clean answerThis puts me in an uncomfortable spot. I can accept the status quo, knowing we\u2019ll lose a non-zero number of good engineers simply because they couldn\u2019t afford a decent model for their challenge. Or I can try something different.I\u2019ve thought about this a lot, and there\u2019s no satisfying solution. Giving candidates a company account for the challenge is off the table: it\u2019s a liability, and I can\u2019t guarantee how it will be used. Reimbursing what they spend doesn\u2019t work either. It\u2019s hard to justify as a business expense, and it quickly drifts into a financial gray area.What I can actually changeIf I can\u2019t remove the cost, the one thing I do control is the process. So the goal becomes making candidates pay as little as possible, and only when it really matters.Move the challenge later. Nobody should burn tokens for a company that hasn\u2019t had a proper conversation with them yet. If you reach our challenge stage, it\u2019s because we\u2019re already seriously interested, and the money you spend is far more likely to turn into an offer.Make it smaller. A challenge that needs a top-tier model running for hours isn\u2019t testing the candidate, it\u2019s testing their wallet. I\u2019d rather give a tight, well-scoped problem that works fine on a free tier or a basic plan, then spend the rest of the time talking about how you approached it. That conversation is where I learn the most anyway.Do it together. This is the one I like most. Instead of a take-home, a live session where the candidate drives and the AI runs on our side, on our machine and our account. No liability, nothing to reimburse, no gray areas. And I get to see what I actually care about: how you think, how you communicate, and how you react when the model confidently hands you something wrong.Weigh the code less. Honestly, the biggest change is how much the challenge counts at all. These days I give far more weight to a proper conversation about architecture and how you like to build software: how you\u2019d structure a system, where you\u2019d draw its boundaries, how you think about testing, reviews and shipping. When an LLM can write most of the code, the code tells me less about you. How you reason about trade-offs tells me a lot more, and that conversation costs you nothing.None of these are perfect. Live sessions are stressful for some people, and a smaller challenge shows less. But I\u2019d rather choose a trade-off on purpose than pretend the problem isn\u2019t there.If you\u2019re hiring too, ask yourself how much your process costs someone who has no income right now. And how many good people you might be losing without ever knowing it.Small footnote. If you live in Berlin and you are comfortably coming to our office in Rosenthaler Platz and you love startup environments, shoot me an email to gerard (at) konvoai (dot) com. As a conversation starter, tell me what you think about RAG, how you've used it in the past and if there might be something better for agentic AI assistants. :)"
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