The Taxos Thesis

As any industry, market, or system evolves, one thing is guaranteed: increasing complexity. And as complexity rises, so does the demand for more human resources and higher operational efficiency—usually unlocked through innovation and technology.

The U.S. tax industry today is flashing a unique set of signals that scream billion-dollar opportunity. Globally, taxation has been on a steady path of becoming more complex, with major regulatory bills emerging every 3–4 years and expanding the workload across the entire ecosystem.

Signal 1 — Taxation only gets more complex

It has never simplified—not historically, not recently, not ever. Every cycle adds more rules, more nuance, more exception cases.

Signal 2 — The supply of CPAs and accountants is shrinking

Just when the complexity demands more talent, the industry is losing the very people required to handle the volume.

Signal 3 — More businesses are born every year

More entities, more filings, more compliance, more work. The volume is compounding on top of the complexity.

Now take those three forces and layer them together:

Rising complexity + declining talent supply + rising workload = structural pressure

In any rational world, increasing complexity demands an increasing amount of human expertise to maintain the same output. But what happens when the required labor pool is not only insufficient, but actively shrinking? Add to that the exploding number of businesses entering the economy, and you get a widening gap that traditional systems simply cannot fill.

This is the foundational premise of the Taxos thesis.

And if you zoom out, it aligns with a universal truth: Industries scale through efficiency. Efficiency comes partly from labor, but mostly from technological leap.

For the coming decade, that leap is AI. Not "AI" as in the buzzword plastered across startup pitches—but the core logic, the infrastructure, the systems-level intelligence that will reshape entire industries.

Back to the U.S. tax ecosystem: the labor gap is real, the demand is rising, and technology—not people—is going to fill it. In this era, that technology is AI.

But here's where the narrative usually gets delusional. Even with the advances we've seen, no one actually knows whether we can trust highly sophisticated AI systems with real-world tax practice—not just exam-level accuracy, but practicality, ethics, reliability, and the most important piece: production readiness at scale.

The Big Four already recognize this. They've said it explicitly: Static evaluation is dead. We need dynamic, real-world validation frameworks. We need Assurance for Agents—and not generic agents, but deeply specialized, domain-anchored, high-stakes, production-grade tax agents.

The uncomfortable truth is: Right now, the industry does not have the data, the methodology, or the infrastructure required to build a truly production-grade agentic LLM for tax.

Meanwhile, some companies are branding themselves as the "AI tax infrastructure" or the "Cursor for tax," but under the hood it's mostly GPT + RAG, held tightly by guardrails to avoid hallucination, and ultimately dependent on human approval to avoid disaster.

This is not intelligent infrastructure. It's duct tape.

Taxos exists at the intersection of what's needed and what's missing.

We are building the intelligent tax infrastructure for the coming decade—research, systems, tools, and services that will power the next generation of domain-grounded, production-ready tax LLMs.

This is the Taxos thesis. Bold by design, because the industry demands nothing less.