A sobering piece from Citrini Analysis jars traders… the dystopian situation based mostly on AI working as hoped… how and why the dominoes fall… the place to speculate to maintain your cash protected
We most likely may have figured this out sooner if we simply requested how a lot cash machines spend on discretionary items. (Trace: it’s zero.)
That line is the emotional gut-punch on the coronary heart of a viral essay that hit the markets like a flashbang over the weekend.
It comes from Citrini Analysis’s THE 2028 GLOBAL INTELLIGENCE CRISIS – a chunk written as a fictional “macro memo” from June 2028. It’s not a forecast, not a prediction, and never “bear porn,” because the authors explicitly put it.
Nevertheless it’s a situation that merchants, traders, and a rising variety of strategists are taking significantly as a result of it explores a danger that just about everybody prefers to not mannequin:
What if our AI bullishness continues to be proper… so proper that it’s really bearish?
As we speak, I need to stroll you thru the essay’s argument in a grounded approach – to not scare you, however that can assist you suppose clearly about the place AI may stress enterprise fashions, the place it’d stress client demand, and the place it’d focus earnings.
Another factor value noting that’s associated to this…
Nvidia Corp. (NVDA) delivered robust earnings final evening, beating income and earnings estimates and delivering better-than-expected steering – and but the inventory is down 5% as I write Thursday midmorning.
This isn’t a referendum on AI demand. It’s a reminder of how concentrated this cycle has grow to be. When management narrows to a handful of names, even glorious outcomes can set off profit-taking quite than enlargement.
Markets finally begin asking whether or not management can broaden. That’s one thing to remember as we take into consideration the place earnings focus – and the place they don’t.
Citrini’s setup… and why it hit a nerve
Citrini frames the essay as a thought train and even makes use of the phrase “left tail dangers” – principally, low-probability outcomes with excessive penalties.
With that caveat apart, the fictional “macro memo” opens with a world that feels believable sufficient to be unsettling:
The unemployment fee printed 10.2% this morning, a 0.3% upside shock.
The market offered off 2% on the quantity, bringing the cumulative drawdown within the S&P to 38% from its October 2026 highs.
The essay’s level will not be “markets will crash precisely like this.”
It’s that AI can succeed and nonetheless destabilize the economic system – as a result of the economic system is not only manufacturing and productiveness. It’s additionally wages, demand, consumption, and the speed of cash.
Citrini describes a interval the place productiveness seems to increase, nominal GDP nonetheless prints advantageous, and but the buyer economic system begins to wither.
One of many essay’s most essential ideas is what it calls “Ghost GDP”:
When cracks started showing within the client economic system, financial pundits popularized the phrase “Ghost GDP”: output that reveals up within the nationwide accounts however by no means circulates by means of the actual economic system.
If that sounds acquainted, it ought to – I’ve highlighted an identical paradox within the Digest for months.
From our 11/19/25 Digest:
AI could also be improbable at changing employees… nevertheless it’s horrible at changing customers…
From a company price construction standpoint, AI is an effectivity dream.
However there’s a problematic flip aspect…
AI doesn’t purchase something.
In different phrases, AI doesn’t drive consumption – and consumption remains to be almost 70% of U.S. GDP.
It’s virtually as if Citrini learn that Digest:
The human-centric client economic system, 70% of GDP on the time, withered.
We most likely may have figured this out sooner if we simply requested how a lot cash machines spend on discretionary items. (Trace: it’s zero.)
The disruptive suggestions loop behind this situation
In response to Citrini, the disaster doesn’t start with a monetary accident. It begins with layoffs. “Human obsolescence” layoffs.
At first, these layoffs are bullish. Margins increase. Earnings beat. Shares rally. And – critically – earnings get funneled again into extra AI funding.
However then the loop tightens:
AI capabilities improved, corporations wanted fewer employees, white collar layoffs elevated, displaced employees spent much less, margin stress pushed corporations to speculate extra in AI, AI capabilities improved…
It was a adverse suggestions loop with no pure brake. The human intelligence displacement spiral.
That phrase – “no pure brake” – is why this essay resonated…
In a standard recession, the system self-corrects. Extra cools. The Fed responds. Hiring finally resumes.
However what if cost-cutting know-how turns into the self-reinforcing mechanism?
In a typical downturn, the remedy for weak point finally turns into “stimulus.”
However in an AI-driven downturn, the “remedy” could also be extra automation… which solely worsens the issue.
Now, let’s be clear.
This isn’t nearly software program. Sure, Citrini references stress in SaaS. Nevertheless it makes a much bigger level…
Software program was solely the opening act.
Be careful for “friction”
If software program is simply the opening act, what comes subsequent?
Effectively, simply search for “friction.”
Consider friction because the intermediary layer within the economic system – the middleman that exists as a result of human beings are gradual, inconsistent, overwhelmed, distracted, or restricted.
That middleman performs a service and expenses for it.
AI destroys friction.
If an organization’s moat relies upon totally on charging for coordination, paperwork, compliance, or workflow administration, then AI is coming instantly for that layer.
However when friction disappears, it doesn’t simply imply “a instrument will get cheaper.” It means corporations can’t depend on clients mechanically renewing contracts… it turns into tougher to quietly cost totally different clients wildly totally different costs… and the a great deal of charges embedded in all types of industries begin getting squeezed.
And over time, a few of these companies merely cease working.
The essay doesn’t preserve this summary. It name-checks insurance coverage, monetary recommendation, tax prep, routine authorized work – even actual property brokers as soon as AI bots can faucet into nationwide property itemizing information and many years of transaction information.
So, this isn’t only a software program drawback. It’s a structural intermediation drawback.
After which Citrini widens the lens additional.
This isn’t solely about industries shedding pricing energy. It’s about what occurs when paychecks weaken:
White-collar employees noticed their earnings energy (and, rationally, their spending) structurally impaired.
Their incomes had been the bedrock of the $13 trillion mortgage market – forcing underwriters to reassess whether or not prime mortgages are nonetheless cash good.
That’s the macro dynamic.
The query for us, as traders, is what to do about it.
So, what’s the motion step?
To assist us navigate this, let’s usher in our know-how professional, Luke Lango.
Starting with “protection,” his prescription is straightforward:
Essentially the most weak zones are friction-based digital companies… particularly commoditized SaaS platforms that cost subscription charges for workflows AI can more and more automate.
We’re not saying “promote all software program.” We’re saying the SaaSmageddon danger is actual.
Selective software program will survive. Some will thrive.
However broad publicity to generic workflow automation platforms? That danger profile has modified dramatically.
It’s nicely value your time to investigate every one in every of your holdings by means of this “friction” lens.
Is that this firm promoting true differentiation? Or is it promoting course of automation that AI may commoditize?
Even if you wish to preserve a weak place in your portfolio, does it deserve the identical weighting?
Shifting to “offense,” right here’s Luke with the gameplan:
The important thing funding perception is that, within the AI Suggestions Loop, capital retains flowing into the bodily layer of AI.
Each iteration requires extra compute. Extra chips. Extra reminiscence. Extra networking. Extra energy. Extra cooling.
That’s the layer that will get paid… each single cycle.
So… if we’re proper about this danger… and if we’re proper that AI will work brilliantly at scale, the cash concentrates within the bodily provide chain of AI. Chipmakers. Reminiscence suppliers. Networking corporations. Energy suppliers. Cooling corporations. Foundational mannequin builders.
These shares usually are not disrupted by the AI Suggestions Loop – they gas it.
That’s a important distinction.
Broad “tech publicity” will not be the identical factor as proudly owning the spine of AI.
Which bucket is represented in your portfolio?
Luke additionally highlights HALO shares as an essential step
To verify we’re all on the identical web page, HALO stands for “Heavy Property, Low Obsolescence.” It was coined and popularized by Josh Brown, CEO of Ritholtz Wealth Administration.
The thought is straightforward – personal companies with real-world belongings that AI can improve however not get rid of.
Again to Luke:
These are corporations with bodily operations AI can not replicate, actual belongings, pricing energy, and AI augmentation potential.
Suppose massive retailers with logistics moats, industrial tools producers, protection contractors, mining corporations, power producers, and so on.
AI can improve their effectivity… however not get rid of their core worth proposition. These companies sit exterior the direct blast radius of AI-driven digital disintermediation.
This aligns with what we’ve already seen out there. Anxiousness hasn’t simply hit software program – it’s unfold throughout office-heavy and intermediary-heavy industries.
In the meantime, hard-asset themes have attracted consideration as perceived shelters.
Even earlier than Citrini’s essay, Luke had been urging traders to search for “choke-point” suppliers within the bodily AI buildout – the supplies and parts that should exist for the AI economic system to perform (metals, chips, energy, specialised supplies, infrastructure).
As I identified in yesterday’s Digest, Luke has lately highlighted MP Supplies (MP), Lithium Americas (LAC), and Trilogy Metals (TMQ) as examples of corporations tied to important inputs.
However this theme extends nicely past metals – into semiconductors, energy infrastructure, networking, cooling techniques – all the AI spine.
If you wish to go deeper, Luke has already begun repositioning round this thesis. If you wish to see how he’s translating it into particular inventory concepts, you can access his research report here.
One closing suggestion…
This Digest will not be a name to flip your entire portfolio as a result of a viral essay spooked social media.
It’s a name to stress-test your assumptions.
If AI retains getting higher – and all proof suggests it is going to – the place do earnings focus? The place do moats skinny? The place does pricing energy migrate?
And does your portfolio mirror these shifts?
Make any portfolio adjustments intentionally and thoughtfully – not based mostly on concern or kneejerk response.
On the identical time, let’s not ignore the structural shift unfolding in entrance of us.
On that word, I’ll give Luke the ultimate phrase:
The actual danger isn’t that AI fails. It’s that it really works.
Have a great night,
Jeff Remsburg
