Non-AI Startups in an AI-Dominated Funding World - How to Compete for Capital

General
1

When 50% of all VC funding goes to AI, what happens to everyone else?

I’ve been having a lot of conversations with founders building “non-AI” companies who feel like they’re suddenly second-class citizens in the funding landscape. Here’s my attempt to synthesize what’s working and what isn’t.

The Uncomfortable Reality

The numbers don’t lie:

  • AI startups get 42% higher valuations at seed stage
  • 65% of US VC deal value goes to AI
  • “AI” in your deck = immediate investor attention
  • “Not an AI company” = harder conversations

What I’m Seeing Work

1. The “AI-Enhanced” Narrative

Smart founders aren’t claiming to be AI companies. They’re showing how AI makes their core value proposition better:

  • “We’re a logistics company that uses AI for route optimization”
  • “We’re a healthcare company that uses AI for diagnosis support”
  • “We’re a fintech that uses AI for fraud detection”

This is authentic—and it opens doors without the burden of competing with foundation model companies.

2. Capital Efficiency as a Feature

Ironically, the AI funding frenzy creates an opportunity for capital-efficient companies:

  • Some investors are explicitly looking for “non-AI” bets as portfolio diversification
  • Profitability and efficiency become differentiators when everyone else is burning cash
  • Lower burn = more runway = more optionality

3. Different Investor Profiles

Not all VCs are chasing AI. Finding investors whose thesis isn’t “AI or bust”:

  • Vertical-focused funds (healthcare, fintech, climate)
  • Geographic-focused funds
  • Stage-specialized funds that care more about fundamentals

What’s Not Working

  • Pretending AI isn’t happening (denial)
  • Slapping “AI” on a non-AI company (inauthentic)
  • Competing for the same investors as OpenAI (wrong arena)

Questions for Discussion

  1. Has anyone successfully raised recently without an AI angle? What was your pitch?
  2. Are there sectors where AI dominance is actually creating opportunities?
  3. How are you thinking about integrating AI without becoming an “AI company”?
2

Keisha, the funding environment shift you’re describing is real, and I think it’s forcing a healthy diversification of funding strategies.

Alternative Funding Paths I’m Seeing Work:

1. Revenue-Based Financing

For companies with predictable revenue, RBF has become increasingly attractive:

  • No equity dilution
  • Faster decisions (weeks not months)
  • Scales with your revenue
  • Providers like Pipe, Clearco, Capchase have matured significantly

2. Strategic Corporate Investment

Corporate VCs in non-AI sectors are actively looking for portfolio companies:

  • Healthcare strategics want healthtech
  • Retail corporates want commerce tech
  • Industrial players want IoT/manufacturing tech

They often care less about “AI” and more about industry fit.

3. Venture Debt + Smaller Equity Rounds

Combining venture debt with smaller equity raises:

  • Less dilution than pure equity
  • Demonstrates capital discipline
  • Banks are actually more comfortable with predictable businesses

4. Non-US Investors

European and Asian investors often have different thesis priorities:

  • Less AI-centric worldview
  • Value sustainability and profitability more
  • Longer time horizons

The Counter-Cyclical Opportunity:

When everyone’s chasing AI, the best deals in non-AI sectors might actually be underpriced. Some investors are explicitly building “non-AI” portfolios as a hedge.

I’ve been modeling scenarios where capital efficiency isn’t just a feature—it’s the entire competitive advantage. The 18-month runway that AI companies burn through in 6 months? That’s strategic optionality.

3

The “adding AI authentically” question is one I think about a lot, especially since I see both sides—the companies building genuine AI capabilities and the ones just adding buzzwords.

The Authenticity Test:

Here’s how I evaluate whether an “AI integration” is real or theater:

  1. Does it require AI? Could you solve this with simple rules/heuristics? If yes, it’s probably theater.
  2. Is there a feedback loop? Real AI applications improve with data. Is yours?
  3. Can you measure impact? If you can’t show AI-specific value, you probably don’t have it.
  4. Would removing AI break the product? Or would it just be slightly less convenient?

Legitimate AI Integrations for Non-AI Companies:

Use Case Why It’s Legitimate
Customer support automation Requires NLU, improves with data
Demand forecasting Pattern recognition beyond human capacity
Personalization Scale of 1:1 recommendations
Fraud detection Real-time pattern matching
Document processing Unstructured data at scale

Red Flags Investors See:

  • “AI-powered” but you’re just calling an API once
  • No proprietary data or training pipeline
  • AI cost is 90%+ of COGS with no path to improvement
  • Can’t explain what the model actually does

My Recommendation:

Be honest about what AI is in your stack:

  • If it’s a commodity capability you’re buying → say that
  • If it’s a core competency you’re building → show the investment
  • If it’s aspirational → have a credible roadmap

Investors can tell the difference. The ones worth having as partners will respect authenticity over hype.

4

Let me offer a CTO perspective that goes beyond the funding narrative: what technology strategy actually makes sense when AI is everywhere?

The Strategic Question:

Instead of “how do we compete for AI funding,” I think the better question is “how do we build sustainable competitive advantage in an AI-abundant world?”

My Framework:

1. AI is Infrastructure, Not Differentiation

For most companies, AI should be treated like cloud infrastructure—essential but not differentiating. You don’t brag about using AWS. Similarly, using GPT-4 isn’t a moat.

2. Invest in What AI Can’t Replace

  • Proprietary data: AI needs training data. Do you have unique data assets?
  • Trust relationships: Regulated industries need trusted partners
  • Physical presence: Hardware, logistics, real-world networks
  • Domain expertise: Deep understanding of specific problem spaces

3. Build AI-Ready Architecture

Even if you’re not an AI company, your systems should be ready to leverage AI:

  • Clean, well-structured data
  • APIs that can integrate with AI services
  • Feedback loops to capture learning
  • Flexibility to swap AI providers

The Sectors Where Non-AI Companies Win:

Sector Why AI Companies Struggle
Healthcare Regulatory, trust, liability
Construction Physical operations, relationships
Manufacturing Equipment, supply chains
Local services Geographic density, reputation
B2B enterprise Sales cycles, implementations

My Prediction:

In 5 years, we’ll look back at this period and realize the best investments weren’t in AI companies—they were in companies that used AI thoughtfully to extend existing moats.

The “non-AI” framing is actually backwards. Every company will be an “AI company” in the same way every company is now an “internet company.” The question is what else you bring to the table.