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When Data Is Cheap, Insight Is Everything

AI agents will not eliminate analysts. They will move the bottleneck from access to judgment.

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In 1865, an English economist named William Stanley Jevons noticed something strange. James Watt’s improved steam engine was designed to use less coal. It did — per engine. But total British coal consumption had risen tenfold. Efficiency hadn’t reduced demand. It had created it.

Cheaper steam power made railroads viable. Iron smelting became affordable enough to industrialize. Ocean shipping reorganized around coal-fired engines. The savings per engine were real, and they were completely swamped by the proliferation of engines.

Jevons had stumbled on one of the most durable findings in economics: when the cost of an input collapses, demand for that input explodes. Not shrinks. Explodes. The pattern repeats everywhere. Cheaper lighting lengthened the working day. Cheaper computation built an information economy that now consumes more electricity than most countries.

We are about to learn this lesson again. The input this time is data access. And the place we’re about to learn it is every company that runs on dashboards.

The Age of Cheap Queries

For most of the history of business analytics, access to data was rationed by labor. You needed someone who could write SQL. Someone who knew which table was the right table. Someone who could clean the data, join it, validate it, and render it into a chart a human could actually read. The cost of asking a question was high, so questions were rationed.

AI agents are collapsing that cost. Natural language to SQL. Automated cleaning. Instant summaries. Charts generated on demand. A product manager can now ask, “What’s our churn rate by cohort for the last six months?” and get an answer in seconds — not days.

A reasonable observer might predict that this makes analysts obsolete. If anyone can query the data, who needs the person who used to write the queries?

This is the wrong prediction. And Jevons explains why.

What Jevons Did to Analytics

When the cost of querying data falls, two things happen at once. More people ask more questions, more often, about more things. That part is obvious. The less obvious part is that the bottleneck migrates.

When data was expensive to access, the binding constraint was access. You needed SQL skill, dashboard permissions, ETL pipelines, and time. When AI removes that friction, the constraint doesn’t disappear — it moves to the next layer.

It moves to: which insights are real, which are artifacts of bad data, which are actionable, and which are worth acting on.

In other words, the bottleneck moves from access to judgment.

This is the Jevons paradox applied to analytics. Cheaper queries don’t reduce the need for analytical thinking. They increase it — because now the organization is flooded with answers, and the scarce resource is knowing which answers matter.

Wheat and Bread

There’s a concept from the rabbinic tradition that captures this perfectly. The Talmud distinguishes between two kinds of expertise: the master of wheat — someone who has read everything, who knows every source — and the baker — someone who takes raw material and transforms it into something that sustains people.

The tradition’s verdict is unambiguous: wheat alone is not bread. Information was never the goal. The tradition lives in what gets made from it.

AI is the new master of wheat. It can retrieve, summarize, translate, and connect information at a speed no human can match. But retrieval is not insight. Summary is not judgment. A chart is not a decision.

The products that win — in analytics, in operations, in any domain where data meets action — are not the ones that give you more wheat. They are the ones that have a baking layer: validation, judgment, workflow, audit trails, human approval gates, and learning loops that get smarter over time.

What “Baking” Looks Like in Practice

Consider two contexts: one vertical and highly regulated, one horizontal and cross-functional.

ClaimMind: A Vertical System of Intelligence (Healthcare RCM)

When a hospital in Indonesia processes a BPJS insurance claim, the raw material is already there: diagnosis codes, procedure codes, patient history, coverage rules, and reimbursement policies.

An AI can retrieve all of this in milliseconds. It can cross-reference thousands of regulations. It can even generate a suggested claim submission.

But what makes the claim trusted — what makes the hospital confident it will be approved and paid — is not retrieval. It is the layer on top:

  • Validation: Do diagnosis and procedure codes actually match?
  • Cross-reference: Are there contradictions or duplicates?
  • Judgment: Is this claim borderline and in need of human review?
  • Audit trail: Can we prove every decision path to auditors?
  • Learning loop: When a claim is rejected, does the system learn why?

The wheat is data. The bread is a trusted reimbursement decision.

That is what ClaimMind is built for: not faster querying, but decision-grade claim intelligence.

Seeknal: A Horizontal System of Intelligence (Data/ML/Analytics Ops)

Now zoom out to organizational operations.

Most companies run on fragmented systems: CRM, ERP, finance, inventory, support, internal tools. AI can query each one quickly. But speed across fragmented systems often scales inconsistency, not clarity.

Seeknal exists to be the baking layer across that environment:

  • Pipeline validation: Is data fresh, complete, and structurally sound?
  • Cross-system reconciliation: Do operational metrics reconcile with financial reality?
  • Policy-aware workflows: What can be auto-executed, and what must be escalated?
  • Decision traceability: Can every recommendation be explained and audited?
  • Outcome learning: Do failed actions improve future decisions?

Again, the point is not access. The point is trustworthy action.

Seeknal and ClaimMind are different products in different domains, but they share the same architecture: a harness that turns cheap data access into reliable decisions.

The Shift: System of Record to System of Intelligence

This is where the “system of intelligence” perspective becomes critical.

A system of record stores facts. A system of intelligence decides what to do with those facts.

This distinction is now strategic.

As Steph Zhang has argued, value is moving from storage layers to reasoning-and-action layers. The moat is no longer who stores the most information. The moat is who can repeatedly convert information into high-quality decisions under real constraints.

Ben Lang’s lens complements this: the winners won’t be thin AI wrappers that generate output. They will be products that own workflow, context, and execution from end to end.

Cheap retrieval gives everyone wheat. Systems of intelligence are what bake bread.

Why This Matters Now

We are in the middle of a great flattening. AI is making data access, query generation, report creation, and summarization incredibly cheap. This is real progress. Millions of people who were previously excluded from data-driven decision-making are now included.

But Jevons tells us what happens next. Cheap access creates appetite for more. More queries, more reports, more dashboards, more “insights.” Organizations don’t become smarter automatically. They become noisier. The signal-to-noise ratio drops.

And the thing everyone actually wants — a clear, trustworthy answer they can act on — becomes harder to find, not easier.

That is why the next generation of data products won’t be better dashboards. They won’t be faster chatbots. They will be agentic workflows that don’t just fetch data, but bake it: validate it, contextualize it, flag what needs human judgment, execute what doesn’t, and leave an audit trail behind.

The workflow looks like this:

ask → inspect data → generate query → validate result → explain meaning → approve/escalate → act → learn

Everything after “generate query” is the real product. Everything after “generate query” is where value compounds.

The Bottleneck Has Moved

Every era of technology has a bottleneck. For a long time, analytics bottlenecks were access: SQL expertise, ETL pipelines, dashboard licenses, data engineering headcount.

AI is removing that bottleneck. And in doing so, it is revealing the next one:

The capacity to turn cheap data into trusted decisions.

This is not solved by “more model.” It is solved by systems:

  • systems with validation layers
  • systems with approval gates
  • systems with audit trails
  • systems that learn from outcomes

In that sense, Seeknal and ClaimMind are not two unrelated products. They are two expressions of the same thesis:

  • Seeknal is a system of intelligence for organizational data operations.
  • ClaimMind is a system of intelligence for healthcare reimbursement operations.

Different domains. Same architecture of trust.

When data is cheap, insight is everything. And insight doesn’t come from the model alone. It comes from the harness around it.

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