DEV Community

AURA GEO
AURA GEO

Posted on

Where the AI Money Actually Goes: What Builders Can Learn From 2025–2026 Spending Data

#marketing #saas #startup #ai

Every week a new AI wrapper launches, and every week someone on a forum asks: "Will anyone actually pay for this?" Instead of guessing, I went through the public spending reports from Menlo Ventures, Sensor Tower and Appfigures and pulled out the numbers that matter if you build software. This post is a builder's reading of that data: what people pay for, what problem they are buying a fix for, and what that means for the thing you are shipping.

TL;DR

  • Consumer AI spend went from an estimated $12B (2025) to ~$40B (2026), while US adoption only moved from 61% to 64% (Menlo, 2026). Growth came from existing users paying more, not new users.
  • Enterprise gen-AI spend hit $37B in 2025, and coding alone was $4.0B, the largest application category (Menlo, Enterprise 2025).
  • 27% of enterprise AI app spend arrives through product-led growth, vs 7% in traditional software. Individual developers swiping a card is a real go-to-market.
  • Users now rank accuracy (45%) and trustworthiness (40%) above ease of use (32%). That changes what "good UX" means for an AI product.

1. People pay to save time on work they already do

The 2026 Menlo consumer survey (5,067 US adults) lists six activities where more than half of the people doing them now use AI:

Activity Share using AI
Writing support 61%
Coding projects 58%
School / work assignments 56%
Image creation 53%
Presentations 52%
Game creation 52%

Look at the list: none of it is a new behaviour. People already wrote, coded, and made slides. AI money flows to existing, frequent jobs where the output is easy to check. If your product idea needs users to adopt a brand-new habit and trust AI, you are fighting two battles.

2. Coding is the first "killer use case", and devs are price-insensitive

On the enterprise side, Menlo estimates departmental AI at $7.3B in 2025, with coding at $4.0B, 55% of it. Next come IT ops ($700M), marketing ($660M) and customer success ($630M).

The report also notes that "for frontier use cases like coding, users are actually quite price-insensitive and will pay more for performance." That matches what many of us feel personally: if a tool reliably saves an hour a day, $20 vs $40 a month barely registers.

Implication for builders: developer tools can price on value and still convert, if the quality gap is obvious within the first session.

3. A small group of power users funds the whole market

This is the most useful number in the 2026 consumer report for anyone designing pricing:

  • 55% of AI users pay for at least one AI product.
  • The typical payer spends $20–49/month.
  • But the 14% who spend $100+/month generate 60% of consumer AI spending.
  • Payers are 5x more likely to use agents (65% vs 13%).

Pricing models reflect this. Half of payers are on flat monthly plans, 26% annual, 15% mix a subscription with usage charges, and 17% are on usage-based pricing.

Implication: a single $9 tier leaves most of the money on the table. A common pattern that fits the data: free tier for discovery → predictable subscription → metered overage for heavy users. Menlo's own advice to founders is blunt: "Keep the free tier." It's where habits form before people pay.

4. Thin wrappers make money, but the bulldozer is coming

Appfigures found that third-party "General Assistant" apps (thin wrappers on top of model APIs) earned $726M in gross revenue since 2023 among the top 1,000 AI apps, more than ChatGPT's own app category in their dataset at the time (Appfigures).

But Menlo's 2025 consumer report warns that each new model release "absorbs more intelligence and specialized features, making entire categories obsolete overnight," which they compare to "building in front of a moving bulldozer."

The 2026 data adds nuance: specialized-tool usage rose from 69% to 75% of AI users, and 72% now use both general and specialized products. Users don't replace ChatGPT, they add tools that do one job materially better. Menlo calls this the difference between being in the "rotation" (competing for queries) and being in the "stack" (owning a workflow).

Implication: a wrapper can earn, but durable revenue comes from owning a workflow: data the general assistant doesn't have, integrations, state, and memory.

5. Trust is now a feature you have to ship

In 2025, ease of use ranked ahead of trust. In 2026 the order flipped: accuracy 45%, trustworthiness 40%, security and privacy 36%, ease of use 32%. Among people who only use general assistants, 27% cite privacy concerns with niche tools and 21% say they don't know which tool to trust. Only 11% say they're unaware of a specialized product.

So the problem isn't discovery. It's credibility. Practical things builders can do:

  • Show sources and confidence for generated output.
  • Ask for the minimum permissions your core job needs, then expand.
  • Make output easy to verify (diffs, previews, citations).
  • Publish clear data-handling docs.

6. Distribution is shifting into AI answers themselves

One more data point that affects how dev tools get discovered. In the 2026 survey, 50% of shoppers who use AI used it to compare prices, 43% to narrow a shortlist, and 37% made a final purchase decision based on its recommendation. Menlo also reports that ChatGPT launched ads in February 2026 and, according to OpenAI, passed $1B in run-rate ad revenue about 200 days later.

For builders, "ask ChatGPT which tool to use" is now a real acquisition channel, next to search and word of mouth. It's worth occasionally asking the main assistants about your own category and seeing whether your product shows up, and what they say about it. We build AURA GEO around exactly this kind of check, but you can start manually: ask five assistants the question your user would ask and write down the answers.

A builder's checklist

  1. Pick a job people already do weekly and where output is checkable.
  2. Be 10x better at one thing than the general assistant your user already has open.
  3. Price for the power-user tail: subscription + metered overage, plus a free tier.
  4. Ship trust: citations, permissions hygiene, verifiable output.
  5. Plan for PLG: individuals adopt first, teams contract later. Menlo notes Cursor reached $200M in revenue before hiring an enterprise sales rep.
  6. Check how AI assistants describe your product, because a growing share of buying decisions start there.

Closing thought

The data says people aren't paying for "AI". They're paying to get back time on work they already do, and they keep paying when they trust the result. That's a much more boring, and much more buildable, target than most launch posts suggest.

Sources

Top comments (0)