Nursing was supposed to be the safe profession in an AI world. 

Hands-on, empathetic, credentialed, performed in person at the bedside of human beings in their most vulnerable moments. If you wanted to design a job no algorithm could touch, you’d design this one. 

A Yahoo! News piece this week made clear we should retire that assumption1. Apps like ShiftMed, CareRev, and Clipboard Health are matching nurses to open shifts the way Uber matches drivers to riders. Hospitals get a flexible labor pool. Nurses get dynamic pricing on their own time. AI runs the matching, the surge, the optimization. 

Read it carefully. Then ask the obvious follow-up. 

If nursing isn’t safe, what is? 

The HALO Assumption 

Nursing is a textbook HALO profession — Heavy Asset, Low Obsolescence. Years of clinical training, state licensure, hospital credentialing, accumulated bedside judgment. The work itself can’t be commoditized away, the assumption goes, so the economics around it are safe. 

Investors apply the same shorthand to entire sectors. Miners are HALO. Refiners are HALO. Utilities, railroads, defense primes, pipelines, industrial commodity producers — anywhere the capital is heavy and the function is essential, the consensus instinct is to call it safe. 

The country still needs nurses. The world still needs copper, maybe more so in an AI world — though there are substitutes. Maybe a good momentum-induced trade. The question consensus refuses to ask is whether it’s an investable inflection. That’s a meaningfully higher bar. 

The SaaS Lesson, Generalized 

Every sophisticated investor has internalized that SaaS economics can deteriorate even when SaaS companies don’t go away. I’ve written about it last November, in my blog Can’t Win for Trying: The SaaS Dilemma2. Margins compress. Renewal rates erode. AI-native challengers run lower-cost stacks and pull pricing leverage out of the incumbents. The companies survive. The unit economics don’t, and consensus has no trouble pricing that risk. 

It is right to worry about SaaS players that can’t adapt. The same lens applied to HALO produces the symmetric conclusion. A business doesn’t have to disappear for the economics to break. Consensus just refuses to look — it has trained itself to read heavy capital as a structural moat, even when the business model has no leverage. 

Two mechanisms shift HALO economics in an AI world without the underlying business going away. Both are happening now. 

Mechanism One: Layer Eats the Relationship 

A digital intermediary — let’s call it the orchestration layer — sits between buyer and seller and runs the matching, pricing, and data flow between them. The layer doesn’t own the asset. It owns the relationship, and increasingly the data exhaust that lets it price the asset better than the asset’s owner can. 

ShiftMed sits between hospitals and nurses. Uber sits between drivers and riders. Booking sits between travelers and hotels. The asset survives. The economics migrate one layer up. 

Wherever AI-powered matching can disaggregate a capacity pool that used to be managed inside one company, the same outcome holds. 

Mechanism Two: Reset, Not Inflection 

The second mechanism is more subtle, and more aggressively misread. 

AI delivers genuine productivity gains in old-economy businesses. We have private-market visibility into miners using AI to identify ore bodies more accurately, run autonomous haul trucks, optimize drill-and-blast patterns, and predict equipment failure before it idles a pit. The same is true across upstream oil and gas, agriculture, chemicals, and refining. Ultimately, the gains will be real and quantifiable. 

The mistake isn’t doubting the gains. The mistake is reading them as inflection and assuming the path is smooth. 

What’s actually changed about the business model? Efficiency. Nothing else. And efficiency doesn’t propagate evenly. Adoption is staggered; early movers capture outsized gains while laggards subsidize the transition. In commodity markets, every gain a producer books gets handed back to the customer once competitors catch up. Winners and losers within a sector diverge faster than the sector average suggests. Then the underlying business reasserts itself. Slow-growth. Capital-intensive. Cyclically priced. Structurally low return on capital. AI doesn’t fix any of that. 

A more efficient miner is still a miner. The cycle still cycles. AI changes the marginal cost; it doesn’t change the structural framework that determines long-run returns on capital. 

Even the valuation rotation — money moving out of expensive software and into cheap industrials, energy, and materials — is itself a reset. HALO might have been too cheap. SaaS might have been too expensive. Once that re-equilibrates, you’re back to the same underlying question: which businesses compound, and which don’t. 

Trade the Reset, Don’t Marry It 

The opportunity is real — but only if you’re early, and only if you exit before the peak of normalization. The reset isn’t a glide path. Operational enhancements and financial impact take much longer than people think, especially in old-economy businesses. The trade window is narrower than it looks, and the cost of missing the exit is higher than the cost of missing the entrance. This requires an active approach, not a set-it-and-forget-it mentality. 

I’d rather own a growing business with a great business model that’s inflecting and argue about whether it’s too expensive, as opposed to a no-growth, lousy business model temporarily enhanced by AI and argue about whether it’s too cheap. 

The Portfolio Implication 

SaaS companies with a real moat aren’t HALO. They are orchestration layers and proprietary data engines for industries that haven’t been fully reorganized yet — businesses whose customers can’t function without them and whose data only gets harder to leave behind. Process flow ownership. Compounding data. Regulated complexity. Real growth driven by real business-model advantage, not a one-time productivity reset. 

That isn’t an accident. I argued in The Layoff Lie that AI is providing the organizational courage for restructuring executives already wanted to make3. The corollary is that the value released migrates to the layer that captures the relationship. The producers get a moment. The layer gets a decade. 

The Edge in Plain Sight 

This is one of the best times in years to be looking for real inflection. The masquerades are loud. The analytical work is rewarded. The gap between what compounds and what merely resets has rarely been wider. 

While efficiency masquerades, inflection compounds. 

Footnotes 
1 Alex Bitter, “AI is disrupting nursing as Uber-like apps grow and set earnings and work schedules”, Yahoo! News, May 3, 2026.
2 Mark Scalzo, “Can’t Win for Trying: The SaaS Dilemma”, Validex Global Investing (validex.co), November 21, 2025.
3 Mark Scalzo, “The Layoff Lie”, Validex Global Investing (validex.co), March 11, 2026.

Important Disclosures

The Destra Multi-Alternative Fund, which is sub-advised by Validex, holds options positions in UBER.   

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