Stay on the Right Side of AI

The rapid growth of AI across Web2 and Web3 brings genuine progress and plenty of hype. It also reveals a GenAI Divide: approaches that translate into real P&L impact vs. those that stall in pilots. While enterprises are still experimenting with how to deploy new tech, one pattern is already clear: individuals using flexible tools in the Shadow AI Economy are capturing the most consistent gains right now.

What are the GenAI Divide and the Shadow AI Economy?

GenAI Divide

MIT’s Project NANDA reports a stark split in outcomes: ~60% of firms evaluate enterprise systems, ~20% reach pilots, and only ~5% reach production. The blockers are less about AI model quality and more about brittle workflows, weak context/memory, and poor fit with day-to-day operations.

Shadow AI Economy

In parallel, individuals are quietly succeeding with personal tools: workers at ~90% of companies report regular use of ChatGPT/Claude-style tools for work, even though only ~40% of companies buy official LLM seats. Bottom-up usage shows what actually works and which use cases deserve investment.

The wrong side vs. the right side of the GenAI Divide

Wrong side (pilot-rich, production-poor).

On the wrong side are apps, systems, or static tools that don’t learn, don’t integrate, and don’t fit in any operational workflows. They fail quietly by offering demos in prototype and disappearing in production.

Right side (production and ROI).

The right side, in contrast, is where AI becomes operational. Success here begins with a very different approach to system design. Tools that perform well are adaptive and deeply embedded. They integrate with real-world data, persist contextual memory, and fit to customizable workflows. In other words, they offer exploring and maintaining a systematic dynamic through various individual demands.

Thesis.io: rethinking SaaS for the right side

What doesn’t work (the outdated playbook).

For the last decade, mainstream SaaS has centered on dashboards, static forms, and click-through interfaces. These tools assume the user does the heavy lifting while the software supplies a fixed structure. The result is rigid systems that forget context, struggle to adapt, and evolve slowly.

The Thesis.io playbook.

Thesis.io takes a different path. The platform assumes people work in varied ways and that software should supply the data, tools, and automation to help them discover, decide, and execute.

• Agents over apps. Thesis.io builds agents and Agent Spaces, not static apps. Agents can reach web data, social sentiment, on-chain transactions, and private research repositories, then combine these sources into structured insights, workflows, and alerts.
• Agent Spaces. Each Space is a customizable environment that bundles data, prompts, logic, and outcomes. Spaces are interactive and composable, and they are designed to compound value across users and over time.
• Contextual memory (under development). Agents retain relevant info chunks, user preferences, and intermediate reasoning so work does not restart from zero every session.

Bottom line for traders: make the Shadow AI Economy work for you

If you are a trader, researcher, or data-driven decision-maker, you are already in the Shadow AI Economy. The question is not whether you are using AI, but whether you are using it effectively.

With Thesis.io, traders can go far beyond what traditional search or chatbots offer. Instead of querying only what the indexed web provides, you can search and reason over a wider spectrum of inputs: on-chain data, social signals, Telegram alpha, and your own private knowledge. This means you do not just read headlines, but you form theses that are traceable, testable, and actionable.

Let’s stay with us on this side of AI!