Everyone is talking about AI apps.
Everyone is building AI tools.
Everyone is launching yet another “AI-powered productivity app.”
But here’s the truth that most founders and investors will realise too late:
The real opportunity in AI isn’t in the apps.
It’s in the infrastructure.
Not the shiny front-end tools.
Not the wrappers.
Not the quick SaaS clones.
The deepest, most durable value is in the:
- orchestration layers
- data pipelines
- agent frameworks
- evaluation systems
- vector infrastructure
- monitoring tools
- automation backbones
- memory systems
- inference optimisation layers
This isn’t the glamorous part of AI.
But it is the gold rush.
Let me break down why.
1. AI Apps Are Easy to Build, and Easy to Kill
Most AI apps today can be built in:
- a weekend
- with a small team
- with a simple UI
- on top of existing APIs
The barrier to entry is low. And because they're all built on the same models, the differences between them are:
- invisible
- small
- temporary
If your product depends entirely on an API call, you don’t have a moat.
If someone else can recreate your app in 7 hours, you don’t have a company.
This is why app-level AI startups will continue to rise and fall like waves.
2. Infrastructure Is Hard, Necessary, and Non-Optional
While apps come and go, every serious AI company needs:
- fast routing
- scalable inference
- context management
- reliable RAG systems
- monitoring and observability
- model-version management
- safety & alignment frameworks
- data transformation flows
- evaluation loops
- real-time memory systems
These problems repeat across every vertical.
And yet most companies aren’t equipped to solve them.
Whoever solves these pain points becomes mission-critical.
And mission-critical means:
- recurring revenue
- deep integration
- sticky adoption
- defensibility
- long-term dominance
Apps chase users.
Infrastructure anchors industries.
3. Infra Startups Become the Picks & Shovels of the AI Industry
In every gold rush, the people who made the most money were not the miners.
It was the companies that sold:
- the tools
- the equipment
- the maps
- the transport
- the infrastructure
Today’s AI infrastructure companies are the modern “picks and shovels.”
They are building:
- the rails
- the highways
- the pipelines
- the foundations
- the operating system of the AI age
When everyone is mining, it pays to sell the shovels.
4. Infrastructure Has Higher Switching Costs Than Apps
A user can switch AI apps in:
- seconds
- or minutes
But a company cannot switch infrastructure providers without:
- rearchitecting workflows
- reindexing data
- rebuilding pipelines
- retraining agents
- reconfiguring memory
- updating integrations
This creates:
- strong lock-in
- high lifetime value
- lower churn
- network effects
- deeper defensibility
Infrastructure becomes the backbone of everything built on top of it.
5. Infra Solves Problems That Every AI Company Has, But Few Understand
Every AI-first team faces:
- hallucinations
- latency spikes
- inconsistent output
- scaling costs
- context window limits
- multi-agent breakdowns
- version drift
- data freshness issues
- token inefficiency
- brittle pipelines
Apps ignore these issues.
Infrastructure solves them.
The teams solving these hard problems are building the foundation for the next decade of AI innovation.
6. Infra Startups Don’t Compete With the Models, They Complement Them
Models evolve rapidly. New versions drop every few months.
But the need for:
- orchestration
- evaluation
- routing
- memory
- monitoring
- optimisation
… does not change.
Infrastructure abstracts away model chaos and provides stability.
When the model ecosystem becomes more chaotic, the value of infrastructure increases.
Here’s My Take
The future of AI won’t be decided by the thousands of tools built on top of models.
It will be decided by:
- the memory layers
- the orchestration frameworks
- the evaluation engines
- the retrieval infrastructure
- the agent networks
- the optimisation layers
- the systems that make AI reliable, predictable, and scalable
Apps are the surface.
Infrastructure is the substrate.
And in every technology wave, the substrate always wins.
If I had to bet on the next trillion-dollar companies, I wouldn’t bet on the tools. I’d bet on the systems that power them.
Because those systems will define how the entire AI industry grows, builds, and scales.
That’s the real gold rush.
Next Article
“Data Is the New Oil, But Prompting Is the New Pipeline.”
Top comments (11)
Every AI startup needs reliable data flowing into their models, but nobody wants to build plumbing. They want to build the shiny AI feature. So they hack together scripts, hit scale problems at 10k records, and suddenly data infrastructure becomes urgent.
The "picks and shovels" thesis is real. While everyone's racing to build the next AI wrapper, there's a massive gap in the infrastructure layer that actually makes those apps work.
Curious - what infrastructure categories do you see as most underserved right now?
You’re absolutely right, the infrastructure layer is where many AI products quietly succeed or fail. Right now, some of the most underserved areas I see are data ingestion and normalisation, long-term context and memory management, evaluation and quality gating, and governance/auditability layers. These aren’t flashy, but they’re critical once systems move beyond prototypes. The teams that invest early in these “unseen parameters” tend to scale with far less friction than those retrofitting them later.
That’s a very pragmatic way to approach it, and honestly, it’s often the only way these gaps get taken seriously. For a long time, ingestion and normalisation felt like “nice-to-have” plumbing, until AI started stressing systems in ways dashboards and prototypes never did.
Yes, I am seeing noticeably more awareness now, largely because AI makes data quality failures visible very quickly. When outputs degrade, hallucinate, or drift, teams are forced to trace the problem backwards, and they often land exactly where you’re building: inconsistent inputs, brittle pipelines, and unowned data flows. AI is effectively turning hidden infrastructure debt into an immediate product problem.
Products like Flywheel make sense in this moment because they let teams experience the value of good foundations instead of debating them. Letting the product make the argument is usually far more convincing than any whitepaper.
Thank you! What are you building?
Thank you for asking. I’m currently building systems and frameworks that help individuals and small teams use AI as an operating layer rather than a standalone tool, focusing on context engineering, decision workflows, quality gates, and governance so AI use is reliable, ethical, and scalable. The goal is practical adoption that creates real leverage, not just faster output.
Sounds awesome, good luck!
Thank you.
“Spot on. Everyone is busy shipping another UI wrapper, but the real bottlenecks right now are in routing, memory, evals, and reliability. The teams solving those ‘boring’ infra problems are the ones shaping how AI actually scales. Apps come and go, but infra becomes the backbone the entire ecosystem relies on. Totally agree—this is where the long-term winners will emerge.”
AI should elevate people, not turn them into button-pressing robots.
The shift you mentioned, from craft to quantity, is exactly where companies risk losing long-term quality, creativity, and trust. Tools can accelerate work, but they cannot replace judgment, context, or lived experience. When organisations treat AI as a shortcut instead of a multiplier, both employees and products suffer.
Now, maximum employees who use ChatGPT they spend most of their time in refining the content and checking the reference.
Today’s AI infrastructure companies are the modern “picks and shovels.”
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