building a research vs. a product company

I didn’t intend to build a research company. I don’t have a PhD — no one on our team has one either. For the last decade I’ve been a product leader who wants to root a team in concrete, hair-on-fire, painkiller problems. But in the past 2 months of building we’ve pivoted further and further away from concrete problems (esp in software) toward more ambiguous ones that require a leap of faith.

Why the shift

concrete problems (esp in software) are saturated at significant levels both from buyer and supplier — there are ~30–40K SaaS companies globally (~17K in the US alone) and growth in SaaS revenue is slowing (12% in 2026 vs. 30% in 2021) as buyers are consolidating spend vs. adopting new software
you can still find niche spaces to start, but I found that the moment we extended beyond that initial audience we immediately hit against several companies doing the same thing
that’s not a bad thing if you’ve found a space where you can build a tailored solution at a fraction of what it used to cost — but the outcomes feel like they’re in the M not B range for many ideas in the software space

The questions we’re following instead

so in the last 2 months the team & I have followed our intuition on where the world will be 2–5 years from now, by asking questions like:
- what will be the dominant way people interface with AI — and how does that differ by situation?
  - devices → phone, desktop, smart speakers, AR glasses, VR
  - modality → text, voice, BCI
  - surfaces → chat apps, software, vehicles
- based on those interfaces, how do the behaviors and expectations people have with AI change?
- and based on those behaviors and expectations, how will software and hardware have to adapt?
we’re still early in navigating these questions (will share more of our opinions later), but some observations on what’s different operating like a ‘research’ company vs. a ‘product’ one:

Experiments vs. milestones

we spend more time thinking through what experiments to build and run to answer different questions — more of a ‘breadth’ search vs. the ‘depth’ search of milestones that sequentially build on each other

Custom instrumentation vs. out-of-the-box solutions

when I read Kuhn’s work on scientific revolutions, one thing that stood out was his argument about measurement: it’s the quantitative anomalies — the gaps that only become visible once your ability to measure a phenomenon improves — that crack the current belief system and usher in a new one. Qualitative anomalies just get patched over
not a perfect analogy, but I think some of that applies in ‘research’ mode — if you’re doing something at the frontier, your measurements need to be custom to your experiment, because out-of-the-box tools won’t give you the fidelity to generate new insights

Research papers vs. best practices

spend at least a day reading research papers to understand where the technology is going and what happens once it’s commercialized
for example: the initial exploration of full-duplex voice models started appearing in late 2024 with Kyutai’s Moshi paper, accelerated through 2025, and by mid-2026 showed up in products — Thinking Machines’ interaction models in May, and OpenAI’s GPT-Live replacing Advanced Voice Mode in ChatGPT in July. Reading the papers gave you an ~18-month preview

To be fair — at our core we’re still not a research company (at least not yet), and I think there’s real value in bringing product principles to this stage. Will share more thoughts in a future post.