For decades, artificial intelligence has been humanity's most ambitious moonshot – a quest to replicate human reasoning across every domain. Master abstract reasoning, ace academic benchmarks, and somehow—through mechanisms nobody quite articulates—you get a system that's good at everything.

Yet this pursuit has always been shackled by a fundamental misconception: that intelligence is a single, transferable capability. When Garry Kasparov defeated Deep Blue in 1997, he demonstrated that chess mastery doesn't translate to business strategy. Andrew Wiles proved Fermat's Last Theorem—one of the great intellectual achievements of our time. Does this qualify him to run a company, allocate capital, or evaluate market opportunities?

Different problems require different architectures.

While the world's greatest minds race to build models that solve the mysteries of the universe, we're building systems that master the practical—helping you decide when to raise prices, enter new markets, or retire failing products. The mundane decisions that silently drive the economy.

You can Vibe Code, but not Business Decisions

LLMs will confidently explain your market strategy. They cite frameworks, reference case studies, use all the right vocabulary. They sound authoritative.

When Cursor writes a Python function, you know instantly if it works. Run it. The feedback loop is seconds. The cost of failure is zero.

When an AI recommends a $10M market expansion, you won't know if it was right for eighteen months. The feedback loop is glacial. The cost of failure is the company.

Every LLM demo shows clever answers to business questions. What they don't show you is eighteen months later, when the $10M market expansion has failed and no one can explain why the AI was so confident. And there's no way to rewind the tape and understand what went wrong.

You can't patch this by scaling.

Why Intelligence Is Domain-Specific

The field has convinced itself that mathematical reasoning equals general intelligence. Yet intelligence can be narrow, deep, and extraordinarily valuable within its domain. A system superintelligent at theorem proving has no reason to be competent at business strategy—these are fundamentally different problems requiring fundamentally different architectures.

We're not building something intelligent at everything. We're building something intelligent at the decisions that create economic value.

Not AGI—Artificial Business Intelligence. While others pursue the science project, we're building the product.

Why Neurosymbolic AI Is the Only Architecture That Works

Neural networks trained on next-token prediction hallucinate by design. They don't know when they don't know. Ask ChatGPT why it recommended a strategy, and it invents a post-hoc rationalization. The "reasoning" isn't the cause of the answer—it's a story generated afterward to justify it.

When it's wrong, there's no way back to the source.

For corporate decisions that move millions, "usually right" isn't sufficient. Neurosymbolic AI provides correctness guarantees through a fundamental division of labor: neural networks recognize patterns—understanding language, identifying similar concepts, finding correlations in data. Symbolic systems verify logic—checking conclusions follow from premises, maintaining consistency with constraints, providing formal guarantees.

The neural system proposes. The symbolic system proves.

The neural component hallucinates hypotheses freely—creativity without consequence. But before any recommendation reaches a decision-maker, the symbolic component interrogates it: Does this conclusion actually follow from the premises? Is it consistent with the constraints? What assumptions does it depend on? What would change the conclusion?

When KnowIdea recommends a decision, you see the full provenance: which facts it used, what logic it applied, what assumptions it made. Pure LLMs cannot provide this—they lack the formal structure to separate facts from reasoning.

In high-stakes environments, no AI slop should be allowed.

The Only Benchmark That Matters

Most AI labs optimize for academic benchmarks—MATH, MMLU, standardized reasoning tests. These measure real capabilities. But there's no MMLU question that asks "Should we raise prices?" and grades the answer eighteen months later when revenue numbers come in.

We built a different benchmark: business reality.

Companies use KnowIdea to make decisions. Should we raise prices? Enter this market? Kill this product? Those decisions play out in the real economy. We track outcomes. Did the strategy work? Did revenue grow? Did the market expansion succeed?

This creates a continuously updating measure of what business reasoning actually works in practice, not what sounds clever to academics. The flywheel: more companies → more real-world outcomes → better signal on what reasoning transfers → more reliable insights → more companies willing to trust the system.

Every company using KnowIdea runs experiments in the real economy. That training signal—the difference between what sounds smart and what actually works—is irreplaceable.

What Actually Matters

When VCs evaluate YC companies, they're running pattern recognition across thousands of investments plus explicit reasoning about market timing and founder quality. When Buffett evaluates investments, he's combining decades of intuition with rigorous logic about what follows from which assumptions under what constraints.

Neural intuition verified by symbolic rigor. That's the architecture.

I say this as a mathematician: proving theorems is intellectually profound, but it's economically orthogonal to most value creation. The value is in helping founders decide what to build, executives allocate capital, and strategists spot opportunities before competitors do.

The Contrarian Bet

The most economically impactful AI won't be the most generally intelligent. A system superhuman at business strategy but incapable of proving the Pythagorean theorem is infinitely more useful than one that can prove Fermat's Last Theorem but can't tell you whether to raise Series B.

Everyone's racing toward AGI. We're building AI that's useful for the billions in value created and destroyed daily through business decisions.

Academic AI research pushes the frontier of what's possible. We're solving a different problem—one that requires deployment in production, measurement against real business outcomes, and immediate economic impact.

The business intelligence renaissance isn't coming. It's here.

KnowIdea is here.