Not Investing in AI: What AI Does to the Numbers

The best sentence in Marc Chaikin’s new 401(ai) presentation is a negation: “I’m not investing in AI. I’m investing in what AI does to the numbers.” It is the pitch’s whole thesis in eleven words, and it deserves a decoding, because it explains both what the five-stock portfolio is trying to own and what it is trying to avoid. We read the presentation end to end. Our full 401(ai) teardown covers it in detail.

Where the Line Comes From

The line arrives as an answer to the objection every AI pitch now faces: isn’t this just another AI stock list? The presentation handles it directly: “I didn’t create a list of A.I. stocks. That would have taken me an afternoon. I wanted a way to separate the companies benefiting from this transition from the company simply talking about it.”

The distinction is between two kinds of exposure. One is thematic: owning the AI builders because AI is the story. The other is economic: owning companies whose margins, demand, or cost structure change because prediction got cheap, whether or not AI is in their story at all. The 401(ai) claims to sell the second kind, filtered through the Power Gauge, Chaikin’s 20-factor rating system, whose output is a simple bullish, neutral, or bearish rating. Chaikin’s reframe of the factors: “I think of them more like 20 witnesses.”

What the Numbers Look Like

The pitch’s evidence for “what AI does to the numbers” runs from the spectacular to the surprisingly ordinary. On the spectacular side, the documented runs with rating dates attached: Vertiv (VRT), up more than 727% after an April 2023 bullish rating; Lumentum (LITE), up as much as 753% after August 2025, now anchored by NVIDIA’s real $2 billion investment; Occidental (OXY), rated bullish January 14, 2022, before Berkshire’s purchases; AppLovin (APP), rated bullish in March 2023 at around $13.

On the ordinary side sit the pitch’s favorite exhibits: Casey’s General Stores using AI to forecast pizza demand, and a senior-living operator using AI monitoring to cut resident falls by a claimed 46%. Neither company sells AI. Both are examples of the thesis: the numbers change first, and the numbers are what compound.

The Index-Fund Angle, Honestly Stated

The most persuasive argument in the whole 401(ai) presentation is also its most carefully hedged one. Chaikin’s case against waiting is the AppLovin example: the company was not added to the S&P 500 until two and a half years after its bullish rating, so index-fund holders structurally missed the early run. But when the host pushes the point, Chaikin concedes the other side himself: “That’s not a flaw in the fund, Tom. I want to be fair here. That’s the fund doing exactly what it was built to do… And for 40 years, waiting was the smartest advice in America. I actually gave it myself.” That is a rare admission in a promotional context, and it is the right frame for readers deciding how much of their retirement money belongs in a concentrated five-stock basket versus an index.

The honest reading is that the 401(ai) is not an argument against index funds. It is an argument that the AI transition is creating a dispersion wide enough that a small, actively managed satellite position can matter, especially for someone in their fifties or sixties with limited time to recover from a mistake. Five stocks is deliberately small: “I don’t want somebody who’s already nervous about retirement trying to choose among 40 different stocks across five different AI sectors,” Chaikin says. “That’s how people end up doing nothing.” The design philosophy, a few names, clear sell discipline, monthly review, is the most defensible part of the offer regardless of whether the specific picks win.

The Two-Arm Logic

The line also powers the pitch’s frame for what to avoid. If what matters is what AI does to the numbers, then the falling arm is defined the same way: companies whose numbers AI erodes. The pairs the presentation cites are real divergences: AppLovin versus WPP, Palantir (PLTR) versus Gartner, Abercrombie & Fitch versus Tillys. Same sectors, opposite arms, and the “Five Stocks to Sell Now” companion report applies the same screen to the downside. Our K-shaped economy explainer covers the full frame, and the chart-reading guide covers how the divergence is presented.

The Honest Caveats

The line is a thesis, not a proof, and the pitch’s own disclaimers mark the boundary. Every historical example is a winner shown after the fact; the one volunteered loser, Braze, is down about 25% from its cited rating. The hypothetical five-stock basket’s 137% figure carries an in-presentation disclaimer: “These results are not typical nor indicative of future performance.” And the claim that a 20-factor system catches the numbers changing early is a research claim, argued through backtests, not an audited record. Chaikin’s own position is modest: “I’m a quant, not a fortune teller.”

There is also a structural caution the pitch mostly avoids: “what AI does to the numbers” is observable in hindsight and genuinely hard to call in advance, and the fear of missing it is precisely the emotion that sells concentrated five-stock portfolios to near-retirees. The pitch’s design, five names, monthly updates, explicit exits, is at least built to manage that risk rather than only exploit it.

What You Get

The 401(ai) package: the five-stock model portfolio, the sell report, the waiting room, and updates when the evidence changes. Value anchored over $2,000, price behind the order-page click, 30-day full refund. Details in the presentation.

The Honest Read

“I’m not investing in AI. I’m investing in what AI does to the numbers” is the rare promo line that survives contact with scrutiny: it redirects attention from labels to economics, which is where AI’s effects actually show up. The 401(ai) is a bet that a 60-year Wall Street quant’s rating system can find where the numbers are about to change. The line is honest. The system is the question.

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