Everyone is talking about AI apps.
Everyone is building AI tools.
Everyone is launching yet another “AI-powered productivity app.”
But here’s the ...
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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.”