I’ve been fortunate to work alongside @MatthewEdanWoo for more than four years now. Here’s a first glimpse into what we’re exploring now: https://matthewedanwoo.substack.com/p/building-a-research-vs-a-product…

building a research vs. a product company

Source: https://matthewedanwoo.substack.com/p/building-a-research-vs-a-product 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.

• 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 areconsolidating spendvs. 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 againstseveral 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 theM not B rangefor many ideas in the software space

• 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:

• 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

• when I read Kuhn’s work on scientific revolutions, one thing that stood out was hisargument about measurement: it’s thequantitativeanomalies — 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

• 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’sMoshi paper,accelerated through 2025, and by mid-2026 showed up in products — Thinking Machines’interaction models in May, and OpenAI’sGPT-Livereplacing 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.

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