AI startups need data and distribution more than another agent

This blog was auto-generated by a multi-agent eval loop, and optimised for SEO / AEO using DataforSEO MCP

AI startups are discovering an old Web2 lesson at unusual speed: the application layer alone is becoming harder to defend.

That was one of my main takeaways from two recent PMF Show conversations. Kristina Meister, co-founder of Fixter, made the point that another AI application or agent can become difficult to defend when technical users can build or automate much of the workflow themselves. Elston Baretto, founder of Tiiny Host, made a related point from the positioning side: as software gets easier to build, the advantage moves towards the things that take longer to copy.

For founders, this has a practical consequence. The product still matters, but the commercial work around the product starts to matter more. Proprietary data, positioning, brand, audience, reputation, community and distribution become part of the operating system, rather than the marketing layer that gets added later.

That is where senior marketing judgement and AI marketing systems start to meet.

What the keyword data says

I used DataForSEO MCP to pull a UK English keyword library around fractional CMO work, AI marketing agents, forward deployed marketing, answer engine optimisation, generative engine optimisation, llms.txt and WebMCP.

The commercial centre is still familiar. fractional cmo and fractional chief marketing officer both returned UK search volume of 880, with reported CPC of $21.72. That is strong buyer-language, and it signals active demand for senior marketing help.

The AI operating terms are smaller, but they are commercially useful. ai marketing agents returned search volume of 480, with reported CPC of $14.77. generative engine optimisation returned 390, answer engine optimisation returned 170, and WebMCP returned 590. llms.txt was the highest-volume technical term in the set at 1,600, although I have already written about why the file is useful infrastructure rather than a discovery strategy on its own.

The useful point is the split. Buyers search in familiar leadership language while the operating language around AI systems, answer engines and agent-facing websites is still forming. A founder-led content strategy has to connect both layers without flattening everything into an SEO explainer.

The application layer is easier to copy

AI has reduced the cost of building software prototypes, internal tools and thin workflow applications. That is useful, but it also compresses the period during which a product can feel defensible because it exists.

If a capable user can inspect the workflow, rebuild a working version with Claude or ChatGPT, and connect it to existing tools, then the product needs more than functional novelty. It needs accumulated advantage.

Web2 already taught this lesson. Google was not defensible because nobody could build a search box. LinkedIn was not defensible because nobody could build a professional profile page. Their advantages compounded through data, distribution and network effects. The interface mattered, but the hard-to-copy value sat underneath and around it.

AI founders should take that seriously. If the core feature can be replicated quickly, the question becomes what the company accumulates every time the product is used.

Proprietary data can become a moat and a product

The first data question is defensive. What does the product learn, collect or structure that makes future versions harder to copy?

That might be customer behaviour, transaction history, operational benchmarks, review data, industry workflows, usage patterns, labelled examples, specialist documents or decision histories. The value depends on quality, permission, structure and commercial relevance. A large database of weak or unusable material will not become an advantage by itself.

The second data question is more interesting. What data is the company creating that AI agents might pay to access tomorrow?

We are moving towards an internet where autonomous agents can discover APIs, evaluate whether a dataset helps them complete a task, pay a small amount and consume the data without a human filling in a form. That changes the commercial imagination around proprietary data. A founder may be building a software product today while quietly accumulating an agent-readable data asset that becomes valuable later.

That does not mean every startup should become a data broker. It means founders should design the product, permissions, documentation and analytics with future data products in mind.

Positioning becomes harder to fake

The other moat is market memory.

Elston’s point about YouTube is a good example because it is painfully practical. You can clone a feature, but you cannot instantly clone a founder who has been on camera every week, explained the category clearly, built trust with an audience and created a body of work that search engines, AI systems and buyers can all recognise.

That same principle applies to events, PR, partnerships, community and founder-led content. These activities are slower than building another feature, which is exactly why they matter. They create evidence in the market.

For AI startups, this matters even more because buyers are surrounded by similar claims. The company that explains the problem more clearly, owns a sharper point of view, proves its methodology and appears consistently across trusted sources becomes easier to understand and easier to cite.

That is positioning as infrastructure.

AEO makes the evidence layer visible

Answer engine optimisation adds a useful discipline to this discussion.

When ChatGPT, Claude, Gemini, Perplexity or Copilot answer a buyer question, they need retrievable evidence. They may draw on search indexes, partner data, cited pages, public profiles, documentation, articles, reviews, comparison pages and structured website content. If the company’s public evidence is thin or inconsistent, the model has little to work with.

This is why AEO should be treated as an evidence system rather than a trick. The task is to make the company easier to find, understand, trust and cite across the places that current AI products already use.

For an AI startup, that means the website, documentation, founder profile, case studies, technical explanations, comparison pages, API documentation and public content should all reinforce the same entity. The language should be clear enough for a buyer to understand and structured enough for an AI system to extract.

What I would build around an AI startup

If I were advising an AI startup founder, I would build five operating layers around the product.

First, a positioning layer. The company needs a clear answer to who it serves, what painful problem it solves, why now, and why its approach deserves trust. This should appear consistently across the homepage, founder profile, pitch deck, sales material and public content.

Second, a data layer. The team should identify which proprietary data the product accumulates, what permissions govern it, how it is structured, and whether any part of it could become an internal advantage or external data product.

Third, a distribution layer. Founder-led content, partnerships, community, newsletters, webinars, events, YouTube, PR and analyst-style references should be treated as compounding assets, not as occasional promotion.

Fourth, an AEO layer. The team should test commercially important prompts across the main AI products, record which competitors and sources appear, improve the evidence base, and repeat the test after pages are updated and recrawled.

Fifth, an agentic marketing layer. Research agents, content agents, SEO agents, customer-intelligence agents and reporting agents can support the work, provided they have clear sources, evaluation criteria, approval gates and memory from human edits.

The role of a fractional CMO changes here

This is why the fractional CMO role is changing for founder-led AI companies.

The work still includes positioning, GTM, acquisition, content strategy, demand generation and commercial leadership. The additional requirement is systems design: deciding which repeated marketing decisions should become governed workflows, which data sources deserve trust, where AI agents can help, and where human judgement must stay in control.

A founder does not need more generic AI content. They need a commercial operating system around the product: clearer positioning, stronger evidence, better distribution, cleaner AEO visibility, and practical AI marketing agents that improve the work rather than simply increasing the volume.

That is also where Forward Deployed Marketing makes sense. The best version is practical build work inside the company’s real context, with senior judgement close enough to the work to make trade-offs, set standards and leave behind systems the team can keep using.

The practical takeaway

If your AI product can be copied quickly, the answer is unlikely to be another thin feature.

Ask harder questions.

What proprietary data does the product accumulate? What proof exists in the market? Which buyer language can the company own? Which sources would an AI answer engine trust? Which marketing decisions should become repeatable systems? Which distribution assets will compound over the next twelve months?

That is the work that makes an AI company harder to copy.

The application still matters. The market around it may matter more.