Key takeaways
- A model upgrade can improve your product without strengthening your competitive position. Competitors with access to the same model can make the same improvement.
- Distribution deserves a place on the product roadmap. Reaching buyers, earning trust and reducing adoption friction require deliberate investment.
- Allocate runway against the actual bottleneck. Prioritise model quality when reliability blocks adoption; prioritise distribution when satisfied customers retain but too few prospects arrive.
- Measure value across the entire workflow. Include human review, correction, onboarding and support when assessing customer ROI and your own economics.
Choosing the right AI model matters. But when competitors can access the same technology, a repeatable way to reach customers and earn their trust can become the more durable advantage.
AT&T processes 45 billion AI tokens a day. About 40% of its AI workloads already run on open models, and it wants that figure to reach 70% within a year. Its calculation is straightforward: maintain accuracy while reducing the cost of delivering it.
At Tinder, the pressure is equally tangible. Its chief technology officer told the Financial Times that AI spending had climbed from an annualised rate of $1 million in January to $10 million by July. The company has started routing some queries to open-weight models to help control those costs.
Customers care about model choice when it affects their results, their data or their bill. But those requirements do not explain why they will choose your product over another built on the same technology. If your competitors can make the same upgrade, everyone’s product may improve without your competitive position changing.
This is where I think founders should spend more time on distribution: a repeatable way to reach the right customers and convert their interest into adoption at workable economics. Product value determines whether they stay; strong retention and referrals can then make distribution more effective.
For many AI application companies, the more durable advantage will sit in the customer relationships, workflow expertise and routes to market they build around the technology. Founders should give those questions the same rigour they bring to model selection.
What are you actually underwriting?
When I assess an early-stage investment, I want to understand what becomes more valuable as the company grows. That might be a workflow that becomes harder to replace, customer relationships that generate referrals, proprietary data that improves the product, or a route to market that competitors would struggle to reproduce. Access to a commercially available model can help create those assets, but is rarely an exclusive advantage itself.
A better product and a stronger competitive position are related, but they are not interchangeable. A company can keep improving its product while finding it harder to acquire customers or defend its pricing. The technical roadmap should therefore connect to commercial outcomes. Making an unreliable workflow usable is significant. Improving a benchmark without changing customer behaviour may deserve less urgency than fixing onboarding or establishing a working sales channel.
Distribution changes the economics of a good product
Microsoft can introduce Copilot inside applications its customers already use. Meta’s Muse brings the same distribution question into consumer AI, with the additional pressure of a free base tier.
According to PitchBook, citing Apptopia data, Muse reached number one on the iOS App Store in less than two weeks. The personal AI assistant space is heating up right now, it’s a great example of how distribution is going to determine the winner.
Instinct is a serious competitor to Muse with deep pockets and distribution via Whatsapp. But a large round does not give a startup the same economics as a platform that can promote an assistant through WhatsApp and Instagram while subsidising the compute required to operate it. Instinct and all the other competitors in this category must finance development, acquire users and establish a business model while Meta’s Muse offers an overlapping service for free and literally owns the platforms.
The download figures establish reach, not retention or a settled winner. Trust also matters when an assistant asks for access to messages, payments and sensitive information. I would be careful about concluding that consumer AI is therefore closed to startups.
The requirement is more specific: a horizontal assistant needs a compelling explanation of why customers will seek it out, keep using it and pay for it when a free alternative is being put in front of them. A particular audience, a substantially better workflow or a trusted community can provide an opening. The founder then has to demonstrate that this advantage translates into adoption and workable acquisition economics.
Start with a market you can reach
A large addressable market tells me something about the potential ceiling. It tells me much less about how a company will get started.
“Every small business could use this” leaves almost every important distribution question unanswered. Which businesses have the most urgent problem? Who controls the budget? Where do those buyers look for advice? What would make them take a meeting with an unknown startup?
A more specific starting point creates something founders can test. An operations product for a particular type of wholesaler might reach buyers through industry groups, software implementers or trusted advisers. That does not establish demand, but it creates a plausible route to discovering it. A precise explanation of how to reach an initial market, with a credible path to expand, is more useful than a broad market definition with no acquisition insight.
Early founder-led sales are valuable here. Doing the work manually reveals objections, budget processes and the language customers use to describe the problem. The aim is to turn those lessons into a process someone else can eventually repeat. If every sale depends on a unique personal favour, there is still work to do.
It’s difficult to get the balance right between “we’re targeting a huge market” to “we have a very specific ICP”, but I’d much rather have a clear path to the first million in revenue by being very specific about who’s problem the product is solving, than be comforted by the notion that the market is big.
Build distribution into the product
Some distribution mechanisms emerge from how a product is used. A collaborative workflow brings in colleagues. An output shared with a client introduces another potential customer. A partner embeds the product in a service it already sells. Dropbox’s referral programme, which rewards both parties with additional storage, connects the incentive directly to the product’s value.
For an AI company, the useful question is whether delivering value can naturally create the next introduction. A research product whose findings circulate within a company may have that potential. A private, single-user utility may need an entirely different acquisition strategy.
The mechanism needs evidence. A share button is a feature; the proportion of recipients who become active users tells you whether it contributes to distribution. A partner agreement creates an opportunity; activated, paying customers show whether the channel works.
Founders also need to understand how much control they have. Dependence on one marketplace, advertising platform or partner can leave acquisition exposed to someone else’s pricing and priorities. A successful channel becomes more valuable when it helps build direct customer relationships.
Measure the constraint before allocating more runway
At pre-seed and seed, these choices are capital allocation decisions. Being data-driven means using the evidence available to identify what is holding the business back, while recognising the limits of a small sample.
| Question | Evidence to examine |
|---|---|
| Can we reach the right buyers? | Qualified opportunities by source and the effort required to generate them. |
| Can we turn interest into use? | Trial or pilot activation, time to first value and where prospects drop out. |
| Does the product earn a place in the workflow? | Retention by cohort, repeated completion of the core task and reasons for leaving. |
| Can we acquire customers economically? | Acquisition cost and gross-profit payback, with inference, onboarding and support costs explicitly accounted for. |
| Is acquisition becoming repeatable? | Referral conversion and sales that can close without the founder’s personal involvement. |
If prospects abandon the product because results are unreliable, model performance may be the priority. If customers retain and expand but few qualified buyers enter the funnel, distribution deserves more attention. If deals stall during implementation, another marketing campaign may simply create a larger backlog. The next week of engineering or sales effort should address the constraint the evidence reveals.
What I want founders to bring into the room
I do not expect a pre-seed company to have a proven acquisition engine. I do expect a thoughtful view of how it could build one, what the founders have learned and which evidence would cause them to change direction. Ultimately, it’s our joint goal to understand how can the startup scale.
The founders I want to work with can explain why their architecture matters and what they learned from a customer who declined to buy. They bring curiosity and discipline to both. Before the next model switch, ask: if we already had the best model for this task, what would still stop this company from growing? The answer deserves a place on the roadmap.
He is a former General Partner at Google Ventures (GV) in Europe, former head of Google for Entrepreneurs in Europe, and founding head of Campus London, Google's first startup hub. Eze writes on Israeli tech, venture capital, artificial intelligence, and founder strategy.
He is also the founder of Techbikers, a nonprofit that brings together the startup ecosystem on cycling challenges in support of Room to Read.
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