The past years have brought rapid technological advancements – changes that forces every tech company to adapt. I have experienced this first hand working in different environments as Head of Product. Despite these companies being on the smaller side, with not a single one surpassing a head count of 50, I have seen how company structures struggle with this new speed: Things that were relevant one day, cease to be so the next. Things that were impossible yesterday, are possible today.
Long before this acceleration, I observed how quickly a company’s ability to adapt dwindles with growing head count. It’s not mystical: So much more needs to be discussed, aligned, reported – and then those things may change much quicker than they did before.
Don’t get me wrong: Scaling in size brings many advantages: Power, total skillset and skill specialisation, reach. But I’m making the case that there’s never been a better time to run a super small startup, and this is my pitch:
Hypothesis 1: It is a better time than ever to build a startup with <4 members, as agility is more important than ever and you can upskill everyone by using AI tools
Sub-hypothesis: Technological advancements are happening faster and faster
If there’s one constant in tech, it’s that change is accelerating. Every year, innovations that once felt futuristic—like AI Language Models (e.g., GPT-3 to GPT-4) or Generative AI Tools (e.g., MidJourney, DALL-E, and Stability AI)—are no longer confined to labs but hit the mainstream.
Sub-hypothesis: Larger companies are slowed down because of communication and alignment needs
Even at companies with fewer than 50 people, decision-making can feel like steering a massive ship. One department is focused on customer feedback, another is rolling out a new marketing campaign, and suddenly, you’re spending half your time just getting everyone aligned. The larger the organization, the longer it takes to shift direction. But whenever I’d complain about a lack of speed at one of my smaller companies, my friends at FAANG or other tech giants would only throw me a bemused smile.
Sub-hypothesis: Tools are not good enough to replace professionals, but good enough to cover a skill in MVP mode
Over the past months, my partner and i have been building an iOS app for strength training. The market is overcrowded, but we didn’t find the tool that gave us exactly what we wanted (despite testing, in good old product fashion, at least 40 different apps). Both of us had some development experience, but not in Swift, and not to the extent needed to build an app from scratch. It wasn’t a piece of cake, but with the help of Chat GPT we got a good MVP going. We used other AI tools to create UI designs, copy, brand images, and so on. Their quality was nowhere near that of a professional, but sufficient – and we knew how to tweak and iterate to get the result we wanted. AI tools are like having a junior assistant who can get you 80% of the way there. The remaining 20% still requires expertise, but for an MVP, that 80% is often good enough to get the ball rolling.
Hypothesis 2: You need a moat so you won’t be eradicated by model advances made by big players such as OpenAI, Meta, etc.
Don’t build something where a big player can quickly shoot you out of the market. Many “Text to video” generator startups were made obsolete by OpenAI’s Sora or Meta’s Movie Gen. Building something with a small team is like making a venture investment with portfolio n=1. There should be a credible moat that protects the startup from such a demise, ideally even riding on the back of model advancements rather than being crushed by them.
The nature of such moats is possibly the most interesting question. They’re not inherently different from those that startups traditionally need to guard themselves against the incumbents. While there are many more, some moats immediately come to mind:
- Business relationships: In many industries, particularly in B2B or niche markets, relationships are everything. You can dedicate one person entirely to sales and relationship-building while the rest focus on product and operations. This approach can be particularly effective when you’re targeting a market or region that’s too small to get on the radar of big companies.
- Niche – customer specific tools: This could be a specific demographic (e.g. strength training app for seniors) or a solution unique to a culture/ region, focusing on ease of use and delightful interactions exactly for this audience.
- Personality-driven: Come for the influencer, stay for the (excellent) product. AI tools have lowered the cost of production, meaning a small team can focus on getting the fundamentals right and tap into the influencer’s reach to build an acquisition funnel that actually pays off. It’s the combination of personality-driven marketing and a solid product that creates the moat.
- Customer tailored: The beauty of being a small startup is the ability to create hyper-personalized solutions for your customers. It creates a connection with your users that a generic, mass-market solution simply can’t. Larger players often can’t afford the time or attention to go this deep for smaller segments.
- Access to proprietary data: If your startup has access to unique data (e.g. from a partnership, a specific dataset, or something unique to your product’s usage), you can create solutions that competitors can’t easily duplicate.
- Regulatory expertise: In industries with heavy regulations — like health, finance, or legal — navigating the complex legal landscape can create a barrier to entry for bigger players that focus on more generalised solutions. A small startup can dive deep into the specific regulatory needs of its customer base and tailor the product to meet these demands.
A small, hyper-agile startup comes with its own set of challenges. The team has to be resilient, constantly learning, and ready to jump into unfamiliar areas. They need to switch focus quickly, handle multiple roles, and be comfortable working without many formal structures.
Success will eventually mean growing beyond the small team—unless AI tools advance dramatically. Even if the goal isn’t to build a unicorn but a solid business, more people will be needed over time. If unicorn potential is there, scaling comes with the familiar challenges of adding headcount and putting systems in place.
Big players will always pose a threat because they have more resources to throw at problems. They can outspend, out-market, and out-hire a small startup. Competing head-on with them isn’t realistic, so you need to find your niche and build something they won’t prioritize.
The market itself is just as much of a risk. Speed is a double-edged sword—while you can build quickly, the pace of change means your product can become obsolete just as fast. What’s relevant today could be outdated tomorrow.
At the end of the day, small teams have an edge in today’s fast-moving landscape. With the right moat and a smart use of AI tools, they can punch above their weight and carve out lasting value. You don’t need to beat the big players at their own game—just find the space they’re not paying attention to and make it yours. If you can stay agile and adaptable, there’s no limit to what a small startup can achieve.