? @SaintMecatti - Twitter Profile

For everyone asking if I’m beating the S&P 500 this year with high-growth names like $IREN: that was never the metric. If you expected S&P-style drawdowns while owning high-beta exposure, that expectation was never realistic. Large, sharp drawdowns are not a glitch — they are a structural part of the journey. High-volatility names routinely give back 40-60%+ even when the operating thesis is intact. That is the price of admission for the asymmetric upside. In periods of macro uncertainty, the market is not evaluating $IREN as a business. It is evaluating its debt, Iran headlines, yields, oil, and the broader derisking of the entire market. Contracted ARR, GPU deployment progress, and the AI pivot become secondary. Price action is driven by the macro filter: how much leverage and volatility capital is willing to own while risk is being systematically reduced. This is why these stocks can sell off hard while nothing material has changed in the company. The market is not asking “is the business on track?” It is asking “how much high-beta debt exposure do I want right now?” That is also why position management is non-negotiable. Size the position so a deep drawdown is painful but survivable. Anything larger and macro noise turns into forced selling at the worst time. The asymmetry only works if you are still in the name when the market eventually shifts back to pricing the underlying business instead of the risk regime. High-growth concentration produces periods of material underperformance and violent drawdowns. Measuring it by whether it is ahead of the S&P in any given year, or expecting index-like behavior, misses the entire point. The only relevant questions are whether the business is still progressing and whether your sizing lets you stay through the volatility.

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@SaintMecatti

13 days ago

@altcap @TrumpAccounts @InvestAmerica24 Happy Halloween Brad👻

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SaintMecatti retweeted

Alex Lieberman

@businessbarista

15 days ago

Spoke to someone leading AI enablement at a massive retailer. The #1 problem they’re running into: AI transformation is owned by IT and all initiatives must run through their AI Center of Excellence. This is causing two parallel issues: 1) Employees outside of IT aren’t given the tooling/infra needed to automate anything beyond simple workflows/use cases (read: citizen developer SDLC) 2) Rolling out AI is bottlenecked by ITs ability to own and approve every initiative This challenge is incredibly common across enterprises & you can quickly appreciate why… On one hand, you can empathize with IT. They’re not incentivized to take risks & embrace disruption, so the risk-return of decentralizing AI implementation cross-org is not obvious. On the other hand, an enterprise’s ability to metabolize raw intelligence is metered by its corporate SDLC. IT is often the bottleneck in that SDLC and it’s not hyperbolic to say that poses existential risk for the enterprise.

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