This content originally appeared on DEV Community and was authored by Jaideep Parashar
VCs are throwing money at AI startups faster than any funding cycle I’ve seen in years.
Model companies, infra companies, tool companies, and even weekend hackathon prototypes are getting valuations that make no mathematical sense.
But here’s the part people don’t talk about:
VCs are over-indexing on the wrong things and underestimating the one factor that will actually decide the next wave of winners.
After watching hundreds of pitches, products, and founders trying to ride the AI wave, here’s the truth I’ve learned:
VCs are betting heavily on capability, but the real leverage in AI comes from distribution + behaviour change.
Let me explain what they’re missing.
1. The Market Is Overcrowded With LLM Wrappers
This is the biggest blind spot.
More than 70% of AI startups today are:
- thin wrappers on top of OpenAI
- UI skins on top of the same APIs
- task-specific tools without real defensibility
- “AI-powered X for Y” apps with zero switching cost
VCs love these because they look like SaaS.
Founders love them because they’re easy to launch.
But here’s the uncomfortable truth:
If you can build it in a weekend, someone can kill it in a weekend.
This is not a SaaS market.
This is an ecosystem consolidation market.
And VCs treating wrappers as defensible products are missing the bigger picture.
2. Real Value Will Come From Distribution, Not Models
Everyone is obsessed with model benchmarks:
- accuracy
- latency
- context length
- multimodality
- pricing
- token efficiency
But none of these create long-term value without distribution.
The next billion-dollar AI company won’t win because they have a slightly better model.
They will win because they understand how humans adopt AI, not how models perform.
The hard part of AI is not the model.
The hard part is changing behaviour at scale.
VCs underestimate this every single day.
3. They Are Overrating AI “Capability” and Underrating “Workflow Depth”
Most VCs ask:
- “What can your model do?”
- “What’s the accuracy?”
- “What’s the latency?”
What they should ask is:
- “How deeply does this tool integrate into someone’s daily workflow?”
- “Is the user replacing a habit or just adding a new chore?”
- “Does the product collapse the steps of a workflow or add more steps?”
- “Does it compound value each time it's used?”
The best AI tools don’t add new behaviours.
They merge, compress, or eliminate existing workflows.
That’s where all the magic is.
4. They’re Backing Tech, Not Founders Who Understand Applied AI
AI founders fall into two categories:
Category 1: Builders
People who know how to build things but don’t understand the real-world context.
Category 2: Operators
People who understand systems, constraints, domain knowledge, and incentives.
Almost every real innovation in AI comes from operators, not builders.
But VCs overwhelmingly back builders because:
- builders speak the language of models
- builders impress on technical depth
- builders know how to pitch capability
- builders make the deck look good
But operators know how to:
- solve a real problem
- understand business economics
- find product-market fit
- design end-to-end workflows
- sell to actual customers
VCs repeatedly miss this distinction.
5. They Forget That AI Tools Have the Worst Unit Economics in SaaS
Inference is expensive.
Context windows are expensive.
Latency reduction is expensive.
Retrieval infra is expensive.
Multimodal processing is expensive.
And every feature users want increases cost.
VCs keep treating AI companies like SaaS businesses.
But most AI companies today have:
- higher variable costs
- unpredictable scaling expenses
- weaker margins
- less defensibility
- shorter user attention cycles
You can’t apply the economics of Slack or Notion to companies running multimodal inference on partner models.
Yet VCs keep doing exactly that.
6. The Next AI Winners Will Look Different From Traditional SaaS
The next trillion-dollar opportunities will come from:
- AI-native workflows
- Infrastructure orchestration layers
- Multi-agent systems
- AI-operated businesses
- AI-powered micro-companies
- Deep domain verticals
- Hybrid human + AI service layers
- AI-first marketplaces
- Intelligent automation pipelines
Not from generations of “AI-powered SaaS.”
But VCs are still thinking in old frameworks.
7. The Blind Spot: Understanding the User’s Real Job-to-Be-Done
AI doesn't win by adding features.
AI wins when it changes how people work.
Founders who understand:
- the psychology of adoption
- downtime reduction
- task-level friction
- system constraints
- context switching
- cognitive load
- trust thresholds
- user anxieties
- operational edge cases
… will build the generation of products that VCs should be betting on.
AI doesn’t disrupt industries.
AI disrupts behaviour.
That’s the real game.
Here’s My Take
The AI funding wave is historic.
But most bets today are being placed on:
- capability → instead of workflow
- models → instead of distribution
- founders with demos → instead of founders with depth
- technical novelty → instead of behavioural adoption
- “AI + X” → instead of true AI-native systems
VCs aren't wrong.
They're just early and looking in the wrong direction.
The founders who understand psychology, systems, incentives, and real-world constraints will define the next decade.
AI is not a technology revolution.
It’s an operations revolution.
An economic revolution.
A behaviour revolution.
And the winners will be the ones who build for that reality, not the hype.
Resource:
If you enjoy these articles, then I have compiled some of the best articles in one book. You can check them here
Next Article
“The Coming Divide: AI Builders vs AI Operators.”
This content originally appeared on DEV Community and was authored by Jaideep Parashar
Jaideep Parashar | Sciencx (2025-11-23T02:18:07+00:00) VCs Are Betting on AI Startups, But They’re Missing This. Retrieved from https://www.scien.cx/2025/11/23/vcs-are-betting-on-ai-startups-but-theyre-missing-this/
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