Editor’s Note: Every Sunday, we’ll publish a quick Weekly Roundup to connect the dots from the past five issues of Hypergrowth Investing and highlight the biggest themes shaping the market right now. Markets move fast. Narratives shift. Volatility can blur the bigger picture. This recap is designed to zoom back out… to help you see the throughline, understand what’s really changing beneath the surface, and stay focused on the long-term opportunity instead of the short-term noise.
If you felt like the market’s whole AI story flipped on its head this week… trust me, you’re not crazy.
One minute, everyone’s celebrating the unstoppable AI boom. The next, software stocks are wobbling, infrastructure names are pulling back, and the narrative suddenly feels a lot more complicated.
That wasn’t random.
Across our five Hypergrowth Investing issues this week — plus our weekend guest essay — one big theme kept surfacing:
We’re moving out of the headline phase of AI and into the structure phase.
The easy “AI is the future, buy everything” trade is behind us. Now comes the part where business models are stress-tested, capital rotates, and where some former leaders stall out… and entirely new ones start to emerge.
This is the phase where investors have to be sharper… more selective about where they put money to work.
And historically? This is also the phase where the biggest long-term opportunities are born.
Below is a day-by-day recap with links to each issue:
Quick links
Monday: The easy AI money is over… and that’s the signal the real opportunity is forming
Link: The Easy AI Money Is Over, but the Bigger Gains Come Next
Monday’s issue framed the current volatility for what it likely is: a leadership rotation inside the AI boom — not the end of it. The core argument is that AI is transitioning from “novelty” to “infrastructure,” and markets are starting to ask tougher questions about returns, durability, and who actually benefits next — which is often when a big “dislocation” appears and new winners emerge.
Key takeaways:
- The selloff in software/data-services was positioned as AI getting “good enough” to threaten legacy models, which forces repricing.
- The piece uses past tech cycles to make the point that Stage 2 often creates fresh leaders (while many early leaders stall).
- The opportunity set shifts toward the builders and enablers (power, connectivity, compute infrastructure) and toward profitable, practical AI adopters.
Tuesday: “SaaSmageddon” — why agentic AI attacks the seat-based SaaS model
Link: SaaSmageddon Is Here – and Not All Software Stocks Will Survive
Tuesday took Monday’s “AI is changing the rules” theme and applied it directly to software. The thesis: agentic AI reduces the need for human “seats,” and that undercuts the core pricing engine of a huge swath of SaaS. If AI systems can complete workflows autonomously, companies don’t just need fewer people — they often need fewer licenses.
Featured in this issue was our three-zone map of software’s future:
- Red Zone: categories facing structural obsolescence as “middle-layer” workflow tools and generic creation moats get flattened.
- Yellow Zone: large, sticky platforms that likely survive, but with margin/pricing pressure and “perfection-priced” expectations.
- Green Zone: “AI-resistant fortresses” — businesses rooted in regulated/proprietary data, cybersecurity, or physical-world integration (where AI needs the platform rather than replaces it).
Key takeaways:
- Don’t “fund AI indirectly” by owning the businesses getting their budgets harvested; focus on the beneficiaries of the spend.
- Look for “agent-proof” moats and for an outcome-based pricing pivot (pricing tied to value delivered vs. number of seats).
Wednesday: AI supply-chain stocks are dropping on “peak capex” fear — and the essay argues that’s backwards
Link: The Best AI Stocks Are Falling for the Wrong Reason
Wednesday’s issue tackled a very specific market worry: “Are we at peak AI capex?” The essay’s answer: no — because spending is shifting from one-time training builds into ongoing inference demand (and “test-time scaling”), which behaves more like a recurring utility than a cyclical binge.
It also argued that the market is missing visible “demand locked in” signals — particularly large cloud backlogs and signed contracts — which would imply capex isn’t a hope trade; it’s fulfillment capacity.
From there, the piece framed the dip as a potential opportunity in “picks-and-shovels” AI exposure, naming examples across the stack.
Key takeaways:
- Compute/chips: Nvidia positioned as the “cadence controller” of the upgrade cycle.
- Memory bottlenecks: Micron and high-bandwidth memory as a choke point.
- Physical infrastructure: Wesco as a beneficiary of power/cooling/network build-outs that sit downstream of every new rack.
Thursday (Guest Post – Marc Chaikin): The danger isn’t being wrong — it’s staying wrong too long
Link: The Dangerous Side of Being Right
Thursday’s guest post from Marc Chaikin zoomed out from “what to buy” and focused on how investors blow up. Using the story of natural gas trader Brian Hunter and the Amaranth collapse, the essay’s point was blunt: intelligence and being “right” doesn’t save you if risk controls fail, positioning gets crowded, and leverage turns a thesis into a cliff.
Key takeaways:
- The catastrophic damage often comes from overconfidence + size + inability to exit — not from a single bad call.
- In volatile regime shifts, having a plan and guardrails matters as much as upside.
(Thursday’s post also included an invitation to a free live briefing on Tuesday, Feb. 17 at 10 a.m. Eastern.)
Friday: AI job loss isn’t theoretical anymore — and policy won’t slow it down
Link: AI Job Loss Is Accelerating – and Washington Won’t Stop It
Friday’s essay moved from markets to the real economy — and argued we’re entering a structurally different environment: “CHAOS Economics” (a collision of deflation from automation and an inflationary policy response).
The piece framed AI as the ultimate cost-cutter — including for white-collar roles — and argued that once replacement is economically obvious, it becomes a fiduciary decision for companies.
It also leaned on the “Engels’ Pause” analogy: a period where GDP and profits rise while wages stagnate — compressed into a much shorter timeframe than historical industrial shifts.
Key takeaways:
- If labor and currency are both depreciating, ownership beats saving — i.e., positioning in the companies and infrastructure driving automation.
- The piece argues that government priorities (geopolitics and industrial strategy) will keep the AI push accelerating, not pausing.
Saturday (Guest Post – Marc Chaikin): Barneys New York and the cost of ignoring paradigm shifts
Link: Barneys New York and the Danger of Ignoring a Market Paradigm Shift
Saturday’s upcoming Chaikin guest essay uses the collapse of Barneys New York as a case study in how structural change doesn’t destroy industries — it destroys outdated business models.
Barneys didn’t fail because luxury retail disappeared. It failed because the paradigm shifted: e-commerce + direct-to-consumer reduced foot traffic and weakened the economics of expensive flagship real estate and inventory-heavy middlemen.
Key takeaways:
- When the market’s “rules” change, the biggest risk is not volatility — it’s complacency.
- Investors who recognize structural shifts early can reposition before capital rotates into the next set of leaders.
(The Saturday essay also includes a reminder about Marc’s free live briefing on Tuesday, Feb. 17 at 10 a.m. Eastern, where he plans to share a new tool and two stock picks.)
The Next Phase of AI Will Be More Selective (and More Rewarding)
If there’s one lesson from this week, it’s this:
Paradigm shifts don’t reward passive investors. They reward prepared ones.
The easy money in AI — the broad “buy anything with exposure” phase — is fading. In its place, we’re entering a far more selective market. Some business models will crack under pressure. Some will stagnate. And a small group of companies will quietly become the backbone of the next economic expansion.
That’s where the real upside lives.
But identifying those names requires more than headlines and hype. It requires understanding what’s changing beneath the surface — capital flows, infrastructure buildouts, labor displacement, policy tailwinds, and shifting profit pools.
If you want to see where this transformation is heading next — and how to position ahead of it — I strongly encourage you to take the next step here:
Click here to access the full briefing and see how to prepare your portfolio for the next wave of AI-driven gains.
Inside, you’ll discover what’s driving this rotation, why volatility may increase before the next leg higher, and how to focus your capital on the companies most likely to emerge stronger on the other side.
The AI revolution is evolving.
The question is simple: Will you be holding yesterday’s winners… or tomorrow’s?
