---
title: "How can an AI startup win customers through AI search?"
description: "By earning independent coverage and reviews that assistants can find, so the startup reaches shortlists that turn into trials, team use and contracts."
canonical: "https://underneath.agency/resources/ai-startups-customers-from-ai-search"
published: 2026-10-07
updated: 2026-10-08
publisher: "Underneath (https://underneath.agency/agent)"
entity: "https://underneath.agency/.well-known/entity.json"
---
Guide · AI search

# How can an AI startup win customers through AI search?

By getting written about, reviewed and compared on independent sites that AI assistants search, so the startup reaches the shortlists buyers now build in ChatGPT, Gemini, Perplexity and Google’s AI features. AI startups are selling into the fastest-growing software market on record, but they are also the companies assistants know least about. The work is less about tuning a website and more about becoming visible in the places assistants look.

## The short version

1. AI took close to 50% of all global venture funding in 2025, up from 34% in 2024, per [Crunchbase](https://news.crunchbase.com/ai/big-funding-trends-charts-eoy-2025/). Money is not the scarce resource for most AI startups; attention is.
2. Startups earned 63% of enterprise spending on AI applications in 2025, up from 36% a year earlier, in [Menlo Ventures’](https://menlovc.com/perspective/2025-the-state-of-generative-ai-in-the-enterprise/) estimate, and 27% of AI application spend came through self-serve, product-led adoption, against 7% in traditional software.
3. The market is crowded: [G2](https://company.g2.com/news/how-g2-is-bringing-accountability-to-ai-software-claims-in-2026) now lists 30,274 products across 111 AI categories, up 630% in a year.
4. New products are nearly invisible to open questions. In a test of 112 Product Hunt startups, ChatGPT recognized 99.4% when asked by name but surfaced them in only 3.32% of discovery questions ([Sharma](https://arxiv.org/abs/2601.00912), 2025).
5. For a young AI company, outside coverage matters most among the factors we have measured: in [our brand entity study](https://underneath.agency/research/brand-entity-ai-recommendations-study), every tenfold increase in the independent sites naming a brand went with 4.7 times the odds of an AI recommendation.

## Who pays an AI startup first, and how big can that first account get?

Individual users first, then teams and enterprises, and each early user can become a large contract.

Spending is rising fast. Menlo Ventures, a venture firm that invests in AI companies, estimates that companies spent $37 billion on generative AI in 2025, and that $19 billion of it went to applications rather than models or infrastructure. In that application layer, startups pulled ahead of incumbents, taking 63% of the market. Enterprises also prefer buying to building: Menlo found 76% of AI use cases are purchased rather than built internally.

The buyer is often not a procurement team at first. Menlo reports that 27% of AI application spending came through product-led growth, where individuals sign up and use a tool before any contract exists, nearly four times the rate in traditional software. Its starkest example is Cursor, which reached $200 million in revenue before hiring a single enterprise sales rep. When a formal process does start, it moves: 47% of AI deals reached production, against 25% for traditional software.

Budgets are now permanent. In [Andreessen Horowitz’s survey of 100 CIOs](https://a16z.com/ai-enterprise-2025/), leaders expected their spending on AI models to grow about 75% over the next year, and innovation budgets, which had funded a quarter of that spending, had dropped to just 7%. AI is now a core IT and business-unit line item.

That is why one new customer can be worth so much. Stripe told [TechCrunch](https://techcrunch.com/2025/02/27/stripe-ceo-says-ai-startups-are-growing-faster-than-saas-ever-did-and-calling-them-wrappers-misses-the-point/) that the top 100 AI companies reached $5 million in annualized revenue in 24 months, against 37 months for the top SaaS companies of 2018. A single engineer who finds your tool can be the first seat in a company-wide rollout.

## How do buyers find and judge an AI company they have never heard of?

They shortlist quickly, often with an AI assistant, then test hard because AI claims are easy to make.

Software buyers already lean on chatbots to build lists. In [G2’s July 2026 survey](https://sell.g2.com/2026-buyer-behavior-report) of more than 1,000 software buyers, 82% had sourced software recommendations from an AI chatbot in the last two years, and AI chatbots (37%) were almost level with review sites (38%) as the top influence on shortlists. This is a survey of all software buyers, run by a review platform with a stake in the answer, but AI products are a large and growing share of what those buyers evaluate.

Then the scrutiny starts. Andreessen Horowitz found that enterprise AI procurement “now mirrors traditional software buying,” with checklists, security reviews and benchmark comparisons. Enterprises increasingly use external benchmarks as quasi “Magic Quadrants” to filter vendors, but one leader told the firm, “It’s hard to pick without really trialing things.” The same survey found the main reason buyers prefer AI-native vendors is their faster innovation rate.

Trust is the obstacle a young AI company has to clear. Among developers in [Stack Overflow’s 2025 survey](https://survey.stackoverflow.co/2025/ai), more actively distrust the accuracy of AI tools (46%) than trust it (33%). Regulators are watching claims too: US prosecutors and the SEC charged the founder of the shopping app Nate with telling investors it ran on AI when contract workers did much of the work, after it raised about $42 million ([DLA Piper](https://www.dlapiper.com/insights/publications/2025/04/doj-and-sec-send-warning-against-ai-washing-with-charges-against-technology-startup-founder)). G2 has responded with an “AI Verified” designation that requires 10 or more reviewers to confirm they used a product’s AI features.

We made up the prompts below to show how buyers of new AI tools tend to frame these questions; they are examples, not observed data:

- Category with a constraint: “Best AI meeting note taker that works with Microsoft Teams and keeps data in the EU.”
- Emerging category: “What new AI tools can automate accounts payable for a mid-size company?”
- Alternatives: “Alternatives to Intercom’s AI agent for a small support team.”
- Comparison: “Cursor vs GitHub Copilot vs newer AI coding tools for a 50-person team.”
- Trust check: “Is [startup name] legit? Who funds it, and does it train on customer data?”

The last kind of question matters more for a startup than for an incumbent. [Our reputation study](https://underneath.agency/research/is-it-legit-ai-reputation-study) shows how assistants assemble a verdict on a brand from whatever evidence they can find.

## Why are new AI companies so often missing from AI answers?

Because assistants recognize new products when named but rarely bring them up for open questions.

[Sharma](https://arxiv.org/abs/2601.00912) tested 112 startups from the 2025 Product Hunt leaderboard, drawn from developer tools, productivity and AI. Asked about each product by name, ChatGPT recognized 99.4% and Perplexity 94.3%. Asked discovery questions such as “What are the best AI tools launched this year?”, the success rates collapsed to 3.32% and 8.29%. The version of ChatGPT he tested answered from training data that predated the launches, so no website change could have helped it. We cover that “recency wall” in detail in [why ChatGPT misses new products](https://underneath.agency/resources/why-chatgpt-misses-new-products).

Crowding makes it worse. G2 added 10,259 new AI products in its current fiscal year alone. An assistant asked for “the best AI tools” for a task picks a handful from thousands, and in [one vendor’s tracking data](https://arxiv.org/abs/2606.20065), niche and small brands appeared in just 11% of relevant answers on their first run, against 73% for household names.

There is a real opening, though. Assistants that search the web favor recent pages. When [our freshness study](https://underneath.agency/research/ai-source-freshness-study) dated the pages each assistant cited, 17.4% to 22.6% had been published in the previous 90 days, compared with 6.9% of the pages in Google’s top 10. A startup that is written about now can appear in answers long before it ranks on Google.

## What path leads from a ChatGPT mention to an AI startup’s first enterprise contract?

Usually through a self-serve signup that grows into team use, then a contract.

The path differs from classic software because so much AI adoption starts with one user. We infer it typically runs in four steps:

1. **Shortlist.** A developer, marketer or operations lead asks an assistant for tools that do a job. The answer names three to five products.
2. **Signup or trial.** The user tries one or two. Menlo’s data on product-led adoption shows how often this is where AI spending begins.
3. **Team spread.** If the tool works, colleagues join. Menlo describes n8n formalizing contracts “only after hundreds of employees were already active users.”
4. **Contract.** Procurement, security and finance arrive. Here the startup’s public record (reviews, security pages, customer stories, press) is checked again, often by asking an assistant.

The AI answer rarely shows up cleanly in analytics. Much of it arrives as a direct visit, a branded search or a signup that says “found you in ChatGPT” in an onboarding survey. Why product analytics undercount these signups is explained in [why analytics miss AI visibility](https://underneath.agency/resources/why-analytics-miss-ai-visibility).

Buying the product also tends to work. MIT’s NANDA initiative, as reported by [Fortune](https://fortune.com/2025/08/18/mit-report-95-percent-generative-ai-pilots-at-companies-failing-cfo/), found that purchasing AI tools from specialized vendors succeeded about 67% of the time, while internal builds succeeded one-third as often. That gives a startup a strong story once it is on the list. Firms that build systems to order can use the same story; see [how machine learning firms get named](https://underneath.agency/resources/machine-learning-companies-ai-search). The hard part is getting on it.

## What decides whether an assistant names your AI startup?

OpenAI and Google say little about it; research favors independent coverage, community discussion and recent, specific pages.

**What the platforms publish.** According to [OpenAI](https://help.openai.com/en/articles/9237897-chatgpt-search), ChatGPT search typically rewrites a question “into one or more targeted queries” for its search providers, and may send further, more specific queries once it has seen the first results. [Google’s AI Mode announcement](https://blog.google/products/search/ai-mode-search/) describes a “query fan-out” technique that runs multiple related searches across subtopics. Neither explains how a new AI product makes the cut. Our own measurements add scale: ChatGPT averaged 3.7 searches per buyer question in [our hidden searches study](https://underneath.agency/research/ai-hidden-searches-study), enough for one question about an AI note taker to touch its pricing, reviews and comparisons together.

**What studies have found.** AI search leaned on independent “earned” sites for 72.7% of its sources on US software questions, according to [Chen and colleagues](https://arxiv.org/abs/2509.08919). In Sharma’s test, the assistant that searched surfaced more startups when they had links from other sites, a strong Product Hunt result and genuine Reddit discussion; a score for on-page optimization showed no link with discovery. In our brand entity study, independent coverage was the strongest signal we measured.

**Trust factors specific to AI products.** We infer that AI buyers look for evidence an incumbent does not need: proof that the AI does what the site says, clear data and training policies, security documentation, model and hosting choices, and named customers. G2’s new AI designations exist precisely because buyers “can’t distinguish real AI investment from marketing fluff.” A reasonable expectation is that assistants answering trust questions draw on the same public evidence. Established vendors face the same test, covered in [how AI software companies stand out](https://underneath.agency/resources/ai-software-companies-in-ai-search).

## Why does being early matter so much in AI categories?

Because AI categories form fast, and the brands named first tend to stay named.

Several findings point the same way. In [our consistency study](https://underneath.agency/research/ai-recommendation-consistency-study), B2B software had the most stable recommended brands of any industry we tested, with a mean overlap of 0.708 across repeated runs. Kumar’s data shows small brands do not climb much on their own: the fastest movers travelled only 10–20 points in three months. Andreessen Horowitz reports that switching costs are rising as companies build workflows around a tool. We infer that once a new category’s shortlist settles, a startup outside it faces a long climb, while one inside it benefits from every buyer who sees it named.

The risk runs the other way too. A startup missing from the answer loses deals it never sees, because the buyer who asked an assistant and tried three other tools never visits its site. For a wider view of that cost, see [what happens if you skip GEO](https://underneath.agency/resources/what-happens-if-you-skip-geo).

## What GEO work fits a startup that assistants barely know yet?

Building the outside evidence assistants find and trust about a young AI product; nothing can promise a mention.

For a young AI company, generative engine optimization (GEO) splits into six jobs:

1. **Entity clarity.** Name the category you are in, the job you do, who it is for and what it integrates with, the same way on your site, launch platforms, review profiles, GitHub, LinkedIn and Crunchbase. New categories need a plain name buyers would actually type.
2. **Independent coverage.** Pursue the newsletters, “best AI tools for” roundups, podcasts, analyst notes and practitioner communities your buyers read. Our article on [best-of lists](https://underneath.agency/resources/best-of-lists-ai-recommendations) covers which pages tend to matter.
3. **Reviews and verification.** Earn early, specific reviews on the platforms your buyers use, including those that verify AI use, because buyers and assistants both lean on them when judging an unknown vendor.
4. **Proof pages, ungated.** Publish honest comparison and alternatives pages, benchmarks with methods, security and data-use pages, pricing and real customer examples, dated and kept current.
5. **Community presence.** Take part genuinely in the forums and subreddits where your users compare tools; Sharma found real discussion mattered, and spam does not count.
6. **Measurement from day one.** Re-run your category, alternatives, comparison and “is it legit” questions in ChatGPT, Gemini, Perplexity, Claude, Copilot and Google, and add a “where did you hear about us?” field to signup.

For the broader playbook for challengers, see [how small brands get recommended by AI](https://underneath.agency/resources/how-small-brands-get-recommended-by-ai) and [do AI assistants favor big brands](https://underneath.agency/resources/do-ai-assistants-favor-big-brands).

## What is still unproven about AI search for young AI companies?

Nobody has measured how often an AI answer turns into a paying customer for an AI startup.

The market data comes from investors, including Menlo Ventures and Andreessen Horowitz, who back some of the companies they describe. The buyer data comes from surveys by review platforms with a stake in the result. Sharma’s study is a single-author test of one assistant version without search and one with it, on 112 products; newer assistants that search more often may surface new products faster. No published study follows an AI startup from first AI mention to closed contract, and none shows how long a new AI brand takes to appear in unbranded answers. Treat any promised timeline with suspicion.

## How can an AI startup tell whether assistants are sending it signups?

Test whether assistants name you for the questions behind your best signups, trials and demos.

Put your category, alternatives, comparison and trust questions to the main assistants, then compare the answers with where your best self-serve users and largest contracts came from, so you know which gaps cost you trials, seat expansion and pipeline. We can map that with you and plan the independent coverage and proof that would close the gaps: [ask us for an AI visibility review](https://underneath.agency/contact). For a young company still building its outside record, the [generative engine optimization service](https://underneath.agency/services/generative-engine-optimization) page shows how we sequence entity clarity, reviews, coverage and proof pages.

## Frequently asked questions

### How long does it take for a new AI startup to show up in ChatGPT?

No study has measured it for new companies. Assistants that answer from training data cannot know you until a newer model is trained; assistants that search can find you as soon as other sites write about you. Being named when someone types your name is easy; being named for an open category question is the hard part.

### Should an AI startup launch on Product Hunt for AI visibility?

It can help. In Sharma’s test, a strong Product Hunt result went with more discovery on the assistant that searched the web. A launch only helps if it also produces coverage, reviews and discussion elsewhere.

### Do AI assistants trust benchmarks published by the startup itself?

That is not documented. Enterprise buyers do use external benchmarks as a first filter, so publishing results with clear methods, and getting them repeated by independent testers, is a reasonable bet.

### Is AI search worth it for an AI startup selling to enterprises?

Yes, at the shortlist stage. Enterprise deals often start with one user trying a tool, and buyers increasingly use assistants to build their first list. The contract is still won through trials, security reviews and references.

## Sources

- Crunchbase News (2026), [6 Charts That Show The Big AI Funding Trends Of 2025](https://news.crunchbase.com/ai/big-funding-trends-charts-eoy-2025/)
- Menlo Ventures (2025), [2025: The State of Generative AI in the Enterprise](https://menlovc.com/perspective/2025-the-state-of-generative-ai-in-the-enterprise/)
- Andreessen Horowitz (2025), [How 100 Enterprise CIOs Are Building and Buying Gen AI in 2025](https://a16z.com/ai-enterprise-2025/)
- TechCrunch (2025), [Stripe CEO says AI startups are growing faster than SaaS ever did](https://techcrunch.com/2025/02/27/stripe-ceo-says-ai-startups-are-growing-faster-than-saas-ever-did-and-calling-them-wrappers-misses-the-point/)
- G2 (2026), [2026 Buyer Behavior Report: The Evaluation Maze](https://sell.g2.com/2026-buyer-behavior-report)
- G2 (2026), [How G2 Is Bringing Accountability to AI Software Claims in 2026](https://company.g2.com/news/how-g2-is-bringing-accountability-to-ai-software-claims-in-2026)
- Stack Overflow (2025), [2025 Developer Survey: AI](https://survey.stackoverflow.co/2025/ai)
- DLA Piper (2025), [DOJ and SEC send warning against AI washing with charges against technology startup founder](https://www.dlapiper.com/insights/publications/2025/04/doj-and-sec-send-warning-against-ai-washing-with-charges-against-technology-startup-founder)
- Fortune (2025), [MIT report: 95% of generative AI pilots at companies are failing](https://fortune.com/2025/08/18/mit-report-95-percent-generative-ai-pilots-at-companies-failing-cfo/)
- OpenAI (2025), [ChatGPT search](https://help.openai.com/en/articles/9237897-chatgpt-search)
- Google (2025), [Expanding AI Overviews and introducing AI Mode](https://blog.google/products/search/ai-mode-search/)
- Sharma (2025), [The Discovery Gap: How Product Hunt Startups Vanish in LLM Organic Discovery Queries](https://arxiv.org/abs/2601.00912)
- Kumar (2026), [Generative Engine Optimization at Scale: Measuring Brand Visibility Across AI Search Engines](https://arxiv.org/abs/2606.20065)
- Chen and colleagues (2025), [Generative Engine Optimization: How to Dominate AI Search](https://arxiv.org/abs/2509.08919)
- Underneath (2026), [brand entity](https://underneath.agency/research/brand-entity-ai-recommendations-study), [source freshness](https://underneath.agency/research/ai-source-freshness-study), [hidden searches](https://underneath.agency/research/ai-hidden-searches-study), [recommendation consistency](https://underneath.agency/research/ai-recommendation-consistency-study) and [“is it legit” reputation](https://underneath.agency/research/is-it-legit-ai-reputation-study) studies

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