State of Startups 2026 | Supabase

14 min read Original article ↗

Supabase Presents

The State
of Startups 2026

Two thousand startup builders told us what they picked up and what they put down between 2025 and 2026. Anthropic rewrote the tooling layer, AI-written code became the median experience, and great companies are now starting up from anywhere.

Startups are the leading indicator. The tools founders pick up, the habits they drop, and the channels they stop trusting shift the status quo. If startups succeed, the effects ripple outward. We track them so we can see what is coming.

  1. 1 Founder and Company
  2. 2 The Anthropic generation
  3. 3 AI-written code
  4. 4 Supabase deepens
  5. 5 Agents mainstream
  6. 6 What happens to SaaS?
  7. 7 Getting customers
  8. 8 Community
  9. 9 Where they show up
  10. 10 Outlook

Founder and Company

Who’s Building Startups

The respondent base got older, more European, more solo, and less self-described-technical. Experienced operators with AI in their pocket are starting companies again.

One person, one platform

Solo founders continue to thrive.

Solo founders were already the largest group in 2025 at 53%. In 2026 they are 61% of respondents, up 8%. Cofounder pairs slipped.

Q&A

How many founders at your current startup?

Not what an engineer looks like

22% of startups are founded by non-technical founders.

Technical-founder share dropped from 82% to 78%. The same cohort leans harder on AI code generation than anyone else.

Q&A

Is the founder(s) at your current startup technical?

Older is the new younger

Founders 40 and older grew from 18% to 25%.

Every age band above 40 grew by a statistically significant margin. The 22 to 29 cohort shrank by 4%. Seasoned operators are filling the gap, often with AI doing the typing.

From anywhere

You can build a great company from anywhere now.

The top metros are still the top metros. But AI has flattened the development gap everywhere else. Europe and Africa grew 3%. Startups are setting up across Toronto, Chicago, Denver, and Austin, and throughout Europe, Asia, and Africa.

Q&A

Where is your startup headquartered?

Q&A

Which North American metro are you based in?

The Anthropic generation

One company swept the tooling layer.

Claude Code became the most-named must-have dev tool. Claude paid subscriptions nearly doubled. Anthropic overtook OpenAI on the model-provider question. The Anthropic Agent SDK leads SDK adoption.

Must-have dev tools

Claude Code eclipsed everything else.

Cursor dropped 19%. v0, Bolt, and Windsurf each lost 6–9%. VS Code held flat. Antigravity appeared for the first time and took the 4th spot in its debut year.

Q&A

What are your must-have dev tools?(Unaided, free-form text)

Q&A

Which AI coding tools do you use?(Aided, multiple options selectable)

Model providers

Anthropic is the default model provider.

Anthropic/Claude climbed from 38% to 64%; OpenAI fell from 69% to 52%. Gemini entered at 44%. Hugging Face and custom models lost material share, a sign fewer teams run their own inference.

Q&A

Which AI models are you using?

AI-written code

AI-generated code is the median experience.

61% of startups have more than half their codebase written by AI. 40% are at 76 to 100%. Only 2% are at zero. Older founders use it more heavily than younger ones, and non-technical founders more than technical ones.

Share of codebase

40% of respondents have more than three-quarters of their code AI-generated.

Among non-technical founders, the 76-to-100% share rises to 54%. Among 50-to-59-year-olds it rises to 60%. Flip the age cohort toggle to see it climb with age.

Q&A

What percentage of your codebase was generated by AI?

Menial coding tasks handed off entirely

How AI is changing the way startups build

The cost of vibes

Building got easier. Selling did not.

The more AI-generated a startup’s codebase, the less likely it is monetizing yet, and the more likely it names customer acquisition as its biggest challenge. One way to view this, early-stage startups are the ones more likely to be heavy users of AI, and they’re also the ones most likely to be early to monetization.

Cost of vibes

More AI-generated code, less likely to be monetizing yet

0%25%50%75%100%Share of respondents who are monetizing0%1–10%11–25%26–50%51–75%76–100%Share of codebase that is AI-generated

Supabase deepens

The stack consolidated.

Supabase gained ground as a primary database, and combined with Postgres, it’s clear what platform startups are betting on. Hyperscalers lost share. And the frontend layer is diversifying fast.

Primary database

80% choose some form of Postgres.

Postgres went from 76% to 80%. Every legacy NoSQL option lost share: MongoDB dropped 5%, MySQL 3%, Firebase 2%. Neon, DynamoDB, and Convex appeared as options for the first time and took small but measurable shares.

Q&A

Which databases are your startup using?

Auth (new this year)

Supabase Auth landed at 72%.

This question was new in 2026, so there is no prior baseline. Three in four respondents who answered the auth question picked Supabase Auth. The firm migration floor is the ~25% who picked no Supabase option at all.

Q&A

What authentication provider do you use?

Hosting and cloud

Supabase and Vercel are running away with startup hosting.

Supabase held its lead. Vercel was already ahead of AWS in 2025, and in 2026 it extended that lead by 9%. Cloudflare grew fastest of all, crossing 27% and passing AWS on the way up. Every hyperscaler lost share.

Q&A

Which cloud providers is your startup using?

Frontend diversifies

Expo, TanStack, HTMX, and Astro all arrived this year.

React and Next.js both grew. But four tools went from effectively zero to real share in 12 months: Expo 10%, TanStack 8%, HTMX 4%, Astro 3%. Native mobile also picked up 3%.

Q&A

What frontend technologies are you using?

Native mobile (iOS / Android)18%

Agents mainstream

Agents shipped. The operator tools did not.

Half of respondents are building agents. Multi-agent systems are in production at a quarter of them. MCP adoption crossed 57% in its first year. But the operational layer beneath all of this is missing: most teams do not monitor AI workloads, most have no formal prompt management, and one in three has no eval process.

Who is building agents

52% of respondents are building agents.

Agent-building share is statistically flat year over year. The "not sure" cohort shrank, which means undecided builders are making up their minds and shipping. Agents are also getting more sophisticated and handling workflow and data analysis, not just customer support.

Q&A

Are you building or planning to build AI agents?

Q&A

What problems are your AI agents solving?

Personalization and recommendations40%

Automating customer support38%

Sales and lead generation29%

Education and training27%

Internal tools such as Slackbots and workflows25%

Financial modeling or forecasting20%

Workflow and process automation

Most common AI agent use cases

Multi-agent (new this year)

Three in four agent-builders are doing multi-agent work.

24% of people building agents are orchestrating multiple agents. The advanced builders are racing ahead of the pack.

Q&A

Are you building multi-agent systems?

Not sure what this means4%

The operational gap (new this year)

Agents ship without the operations stack.

Agent usage and construction is growing. But the operational layer (monitoring, versioning, evaluation, etc.) is still missing. If the agentic trend continues, this represents a real opportunity for the next wave of builders.

Q&A

How do you manage prompts in production?

Hardcoded in source code26%

Version-controlled prompt files25%

Prompt management platform (PromptLayer, Humanloop, etc.)11%

Feature flags for prompt variants10%

"Template-based prompt architecture with dynamic data injection via Gemini for context-aware Google Ads auditing."<1%

(dynamic prompts via orchestrator + RAG)<1%

Q&A

How do you handle AI model evaluation and testing?

Manual testing / prompt iteration52%

We don't have a formal eval process yet36%

A/B testing in production20%

Automated eval suites (custom-built)17%

Third-party eval tools (Braintrust, Humanloop, etc.)7%

Q&A

What observability tools do you use for AI workloads?

We don't monitor AI workloads specifically59%

Custom-built dashboards20%

MCP (new this year)

MCP went mainstream fast.

A year after the Model Context Protocol launched, more than half of respondents are already active users. Even as the debate over MCP and APIs continues, people still find MCP valuable in at least some contexts.

Q&A

Have you adopted any MCP servers or tools?

Yes, actively using in production29%

Aware of it but not using yet29%

Supabase AI Tools

Learn more about Supabase MCP servers and Supabase Agent Skills and level up your AI-driven development.

Read the docs

What happens to SaaS?

What is going to happen to SaaS?

The same pattern shows up across categories this year. In CRMs, analytics, and observability, named SaaS vendors lost share while "we don’t have one yet" or "custom-built" grew. Startups are not buying operator SaaS. They are building their own or doing without.

Sales tools

CRM absence jumped 11%.

53% of startups with a GTM motion have no formal CRM, up from 43%. Every named CRM lost share: HubSpot, Salesforce, Notion/Airtable, Google Sheets. Teams build their own or do without.

Q&A

Which sales tools are you using?

We don’t have a formal CRM or sales tool yet53%

Observability

56% still don’t use observability tools. Those who do are building their own.

"Custom solution" is the fastest-growing answer, up 2.5%. Datadog and Prometheus both lost share. Sentry kept its lead and grew slightly. The observability market is bifurcating between Sentry for errors and custom dashboards for everything else.

Q&A

Which observability tools are you using?

We don’t use observability tools yet56%

Analytics and growth tools

Custom-built dashboards grew 6%.

"I don’t track this yet" grew 5%. HubSpot, Salesforce, Mixpanel, Segment all lost share. The pattern repeats: skip the vendor, ship something internal, or don’t track at all.

Getting customers

Founder-led. No CRM. Mostly bootstrapped.

Founders still do sales themselves. Two in three have never tried paid acquisition. Pricing is settling on tiered feature plans for the first time.

Initial customers

Personal networks still work best.

Personal networks remain the top source of initial paying customers. 67% of respondents still haven’t tried paid acquisition at all, up from 62% last year.

Q&A

Where did your startup's initial paying customers come from?

Personal/professional network56%

Cold outreach or sales35%

Inbound from social media (Twitter, LinkedIn, etc.)29%

Content (blog, newsletter, SEO)21%

Developer communities (Discord, Slack, Reddit, etc.)14%

Open source users who converted8%

Accelerators/incubators7%

Hacker News or Product Hunt6%

No paying customers yet<1%

Sales motion

Founder-led sales is still the norm.

Dedicated full-time sales hires usually do not arrive until after the tenth employee. Product-led growth as a motion climbed 4% to half of respondents. "Not sure yet" is shrinking.

Pricing is settling

Tiered feature plans jumped 12%.

For the first time in the survey, startups are picking a pricing shape earlier in their lifecycle, and they are picking the same one. Tiered feature plans went from 23% to 36% of respondents who picked any pricing model. The "still experimenting" cohort shrank.

Community

Community is the moat.

Few startups build a developer community. The ones that do grow from a completely different mix of channels, and convert at higher rates.

Developer communities

Most startups skip community.

Most startups skip community entirely. 48% have not built one, up 4%, and the "planning to" middle is shrinking. Only 11% have built one. That small group is the one to watch: the chart below shows they grow from a completely different mix of channels.

Q&A

Have you built a developer community around your product?

In progress / planning to24%

Not relevant to our audience19%

The growth split

Investing in community pays off.

When anyone can ship anything, distribution is the bottleneck. Personal networks run dry. Cold outreach gets ignored. The startups that built a developer community ship into a different funnel: open-source users and developer communities convert into paying customers at multiples of the rate non-community teams see. The chart below splits the customer-source mix by community status.

The moat

Where first customers come from, split by community status

Built a developer community

No community

Open-source users who converted

Content (blog, newsletter, SEO)

Personal / professional network

Where they show up

Founders are broadcasting less.

Conferences emptied out. Social media lost users across every major platform except TikTok. 1 in 10 respondents now says they have given up on social media entirely. 1 in 3 says they have no online persona at all.

The quiet exit

10% of founders have given up on social media.

X lost 6%. LinkedIn lost 3%. Reddit and Discord lost 3-4%. TikTok was the only platform that grew. The "I have no online persona" share grew 5% to 33%. One in three respondents is fully offline.

Q&A

Which social media platforms do you use at least 3x per week?

I’ve given up social media11%

Conferences fell

2 in 3 respondents are not attending any industry conference.

The "none of the above" cohort jumped 10%. Google Cloud Next, AWS re:Invent, Microsoft Build, and Y Combinator Demo Day all lost share. Conference-led developer marketing is working for a smaller slice of the market every year.

Q&A

Which events have you attended or plan to attend?

AI Engineer World’s Fair6%

Outlook

Technical complexity is handled. New fears took its place.

"Technical complexity" as the largest business challenge fell from 24% to 11%, the biggest single movement in the survey. AI ate the hard parts of shipping. What replaced it: burn out, AI-competition fear, runway anxiety. Optimism is mostly flat. Engineers less so.

Biggest challenge

Technical complexity fell 12%.

The largest year-over-year shift in any single category. Three new challenge options came online: burn out, AI competition, runway anxiety. Together they absorb roughly the same share that used to pick technical complexity. Among 1-10 person teams, burn out has already overtaken technical complexity as the second-biggest challenge.

Q&A

What is your startup's biggest business challenge today?

AI competition / disruption7%

World outlook

People are mostly optimistic, but not equally.

57% say they are optimistic, down 1% from last year, not statistically significant. Founders are 58% optimistic; non-founders are 49%.

Q&A

Given the state of the world, are you...

Neither optimistic nor pessimistic27%

The optimism gap

The most optimistic founders are also the farthest from revenue.

Optimism rises with AI codebase share: 49% of zero-AI founders feel optimistic about the world, 61% of heavy AI users do. Revenue runs the other way. 56% of zero-AI users are currently monetizing. 31% of heavy AI users are. One reading is that hands-on AI experience demystifies the technology and turns it into something founders feel they can wield. The heaviest AI users are also the most likely to be bootstrapped, the earliest in their lifecycle, and the least exposed to whether the market actually wants what they have built.

Optimism gap

Optimism climbs as AI writes more of the code

0%25%50%75%100%Share of respondents who are optimistic0%1–10%11–25%26–50%51–75%76–100%Share of codebase that is AI-generated

Builders choose Supabase

Supabase is the Postgres development platform. Build your startup with a Postgres database, Authentication, instant APIs, Edge Functions, Realtime subscriptions, Storage, and Vector embeddings.

Thank you

A special thanks to the following companies for participating in this year's survey.