This as-told-to essay is based on a conversation with Sidhant Bendre, a 25-year-old cofounder of an AI-driven consumer software portfolio company based in New York. His company, Oleve, prioritizes using AI to stay lean while scaling with minimal staff and has also been recognized by OpenAI for processing an immense volume of text data through its models. His words have been edited for length and clarity.
I don’t know what it’s like to build a company without AI involved, and that impacts how my cofounders and I have always hired.
We started the first version of our company out of college in January 2023. The next year, we officially founded Oleve, an AI-driven consumer software portfolio across various app categories.
Right before then, there were only four of us, and we haven’t expanded much beyond that. We’ve been able to leverage AI tools, but staying tiny requires operating principles that actively resist the default path to add head count.
With AI, I can learn just enough operating knowledge about anything I need, which has allowed our small group to move faster. It’s also the reason we put extra scrutiny on hiring. Most companies reward people for becoming experts who are irreplaceable in their function. Tiny teams reward the opposite: people who master something fast enough to systematize it and move on.
We’ve only ever built with AI, so the ability to leverage it is necessary for a new hire
When we launched this company in college, we were already utilizing AI ourselves, so we had a clear understanding of its limits going into the business. As a result, we don’t have many issues with relying on it in our workflow.
If someone can’t leverage AI effectively, it could be a problem because we’ve built a lot of our systems around it. We have a template code base that we reuse for different products, and it’s built in a way that allows us to clone it for an individual product. We also use AI in our marketing, analytics, and hiring processes.
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Hiring comes down to whether the job candidate is leveraging AI in the right way and with the right mindset about its limits.
My biggest piece of advice for people who want to join a tiny team
I tell people to learn to think operationally, not just in terms of execution. There are a few mistakes with leveraging AI that can immediately disqualify someone from a tiny team. One is treating it like a replacement.
Treating AI as a replacement for thinking rather than a tool for leverage is a big mistake. We want candidates to use AI, but we’ve seen take-home tasks where someone clearly just fed a prompt into ChatGPT and submitted whatever came back without critical thought.
On tiny teams, carelessness doesn’t just mean one bad deliverable — it means building bad systems that compound. There’s no middle management layer to catch sloppiness, and there’s no room for people who aren’t thinking about how their work affects what comes next.
AI can generate the first draft, but it can’t tell you if it’s the right draft. It can explain how code works, but it can’t tell you if it’s well-architected for your specific needs. It can create options, but it can’t evaluate which option actually solves your problem.
The right way to leverage AI is to use it to accelerate learning and execution so you can focus on higher-level thinking and decision-making.
We are recruiting with way more scrutiny on engineering, and we only hire specialists
Any decent engineer can now do a lot more than before with AI.
Results don’t necessarily mean skill anymore. We’ve had to create more involved engineering recruitment processes because we need to vet potential employees more closely. It’s a skill to use AI to fully drive results, but that can be temporary when issues arise with pushing out a complex product and someone lacks the deeper knowledge needed.
This is why I prefer hiring specialists and utilizing AI to turn them into generalists. If there’s a system for a product that we need help with, we identify the critical pieces of the product. Then, I hire someone who’s a really good specialist in that area, and we know we can train them to expand their work to other platforms and products with AI.
For example, I have an engineer who was purely a backend engineer, but is now working on a front-end project as well. Much of this is possible because he can leverage AI to learn on the fly, even though I hired him for this very specific skill set that was needed for a high-priority backend project.
There are new lean startup principles with tiny teams
I presented at an AI engineering conference earlier this year, where they launched a tiny teams track. My talk was based on what we’ve learned and developed as the new principles of lean startups using AI at Oleve.
Startups can begin building toward a world where people can command clusters of agents to perform tasks on their behalf. At the most basic level, everyone is now their own chief of staff.
Doing this is something that’s very achievable.
Do you work on a tiny team and want to share your story? Email this reporter at [email protected].
