Why Sell-Side Research is Expensive Fiction
Sell-side analysts might be the most insulated people in the stock market today.
On the last week of January, a former client forwarded me a report: hold your $1.5 Mn Infosys position for 35% upside. Why? Because the firm was adopting AI to “boost efficiency”.
I genuinely laughed.
The people paid thousands of dollars to analyze tech stocks are often the last to see structural shifts. While they were tweaking their DCF models, the friction to build software had collapsed to nearly zero. I’m not a traditional software engineer, yet I had just shipped two functional applications in a single month using Claude and Cursor.
I told my client the analyst’s thesis was broken. The very next week, the market agreed. Following the Friday launch of Anthropic’s new enterprise AI agents, Indian IT stocks dumped 7% on February 4th along with global tech selloff. The traditional IT services moat wasn’t expanding, it was evaporating.
here is my post on linkedin from morning of 👉 31st of Jan.
The $300 Billion Wake-Up Call
The carnage wasn’t limited to offshore IT. A sudden jolt rippled through the technology sector in early February 2026, when nearly $300 billion in market value vanished in about a single trading day, forcing investors to rethink where value truly sits in the age of artificial intelligence. High-margin data and legal service providers experienced even steeper drops. Stocks like Thomson Reuters and LegalZoom slid 15% to 20% as markets priced in the risk that AI agents could handle legal research, drafting, and triage.
But while Wall Street was panicking over the death of the “billable hour,” they completely missed the flip side of the equation. If AI agents can execute complex legal and coding workflows for a fraction of the cost, the friction to build a business drops to near zero. We are witnessing a massive supply shock. Because anyone can now “vibe-code” an app into existence, the market is being flooded with new software.
How a $319 App Changes the Global Economy
A client sent me a post from X this week that perfectly captures why the old IT model is collapsing.
A non-technical founder just launched a “Bhagavad-Gita daily” iOS app. He built the entire thing himself using a $200 Claude Code subscription, $20 for ChatGPT Pro, and a $99 Apple developer fee.
He shipped a complete, functional app for $319.
When the cost of building software drops this close to zero, supply explodes. We are looking at a vibe coding market projected to hit $325 billion by 2040. Hundreds of thousands of new applications are about to flood the market.
This creates a massive third-order impact. The hardest part of launching a startup is no longer writing the code. The new bottleneck is getting people to actually care. Every single app that gets vibe-coded into existence now has to fight for distribution. They all have to navigate the exact same post-ship lifecycle to survive.
The "Growth Tax" on the Infinite App Boom
Shipping an app for $319 is a miracle. But building an app and building a business are two entirely different games.
The moment that Bhagavad-Gita app goes live on the App Store, the “build” phase ends, and the “grow” phase begins. And the grow phase is an expensive gauntlet. To turn that app into a revenue-generating asset, the founder has to pay what I call the “Growth Tax.”
Every single vibe-coded app has to pay these four taxes to survive:
The Attention Tax (Acquisition): How do users find the app? The founder has to buy them. That means running a $50 ad test on Instagram or Google Search. (The Winners: Meta, Alphabet)
The Plumbing Tax (Operations): If the app goes viral, the servers will melt. Founders need edge caching, cloud hosting, and DDoS protection just to keep the lights on. (The Winners: Cloudflare, AWS, Datadog)
The Trust Tax (Security): Users won’t pay for a buggy, unsecured app. It requires enterprise-grade identity verification and bot protection. (The Winners: Okta, Auth0)
The Habit Tax (Retention): A daily quote app only works if people open it daily. Founders have to pay for push notifications and email flows to retain users. (The Winners: Twilio, HubSpot)
The legacy analysts missed this because they don’t build software. They don’t see the tollbooths that capture a fraction of a cent every time one of these new apps scales.
The "SuperAnalyst" Tollbooth Screener
I don’t want you to just take my word for this. I want you to spot these capital rotations before the sell-side analysts figure it out.
The old model of waiting for a 40-page PDF report is dead. The market moves too fast. To spot these “tollbooth” beneficiaries before they show up in an earnings report, you need to run the numbers yourself.
Open up ChatGPT, Claude, Perplexity, or My personal favourite (Gemini) & select deep research and run this exact research prompt. This isn't a basic question; it is an institutional-grade research protocol designed to force the AI to map both the public winners and the private market threats.