Understanding the implications of the CitriniResearch 2028 Global Intelligence Crisis Macro Memo.

Hey everyone, for this post I’m talking about a Substack memo that lays out a theoretical future where agentic AI continues on its current trajectory and what that means for the economy, the job market, and specifically the white collar workforce.

What is Citrini Research?

Citrini Research is a boutique firm operating via Substack focused on equity investing ,and global macro trading. It has recently gained notoriety in the finance and tech world for it’s provocative,deep dive research papers that go viral. Founded by James Van Geelen, a former paramedic with a background in biology and psychology before pivoting to investments.

The core philosophy of Citrini Research is a “no-nonsense” approach to investing, focusing on:

• Thematic Baskets: Rather than just picking single stocks, they create baskets of equities to play on specific trends (AI, infrastructure, or defense) while attempting to hedge out broader market risks.

• Lateral Thinking: They also often connect disparate fields like how the power requirements of AI and how it impacts utility companies and how agentic AI might disrupt white collar middle man industries.

In early 2026, the Substack exploded in popularity following their viral report called “THE 2028 GLOBAL INTELLIGENCE CRISIS” co-authored with Alap Shah an AI entrepreneur. The paper presents a “dystopian” scenario where:

• AI agents become so efficient at cognitive labor that they rapidly displace human workers starting as early as 2026.

• This displacement triggers a massive financial crisis because the displaced workers can no longer pay mortgages or taxes, destabilizing the rent extraction layer of the US economy.

• The report was widely discussed on reddit (r/ValueInvesting, r/cscareerquestions) and featured in financial outlets like CNBC, and Seeking Alpha for its potential role in market volatility.

The post solicited an uncommon response from some of the largest financial institutions in the world shifting the conversation from “How much will AI improve productivity?” to “Will AI break the feedback loops that keep the economy stable?”

While some dismissed the report as “doomsday fan-fic”, the actual market movements in late February 2026 suggested institutional investors were at least partially spooked by its logic.

• The Stock Market’s Reaction: Specific companies highlighted in Citrini’s report such as Uber, Doordash, Mastercard, and American Express saw their shares drop between 4% and 6% in the days following the reports release. Traders attributed this to the papers argument that agentic AI would eventually bypass these middle men entirely.

• Citadel’s Rebuttal: The hedge fund Citadel Securities in an uncharacteristic move published a formal counter-paper titled “The 2026 Global Intelligence Crisis”. Where they argued that Citrini’s timeline was too aggressive, noting that technological diffusion usually follows an S curve rather than a vertical line. They also claimed Citrini ignored the “natural brakes” of the economy specifically that companies stop investing in tech if their customers can no longer afford the products.

• Institutional Skepticism: Other players, like Bianco Research, pushed back by arguing that humanity has an infinite number of problems to solve and that AI would simply shift labor rather than destroy it.

Beyond its obvious shock value, Citrini’s post is significant for three core reasons:

• It identifies the transmission channels of AI Risk: Most AI commentary focuses on “Who loses their job.” Citrini look at the underlying financials. They mapped out how a layoff in the tech sector leads to

  • Private Credit Defaults: Since many mid-market SaaS companies are funded by private debt, their failure could trigger a localized credit contraction.

  • Mortgage Stability: If high-earning white collar workers (the “top 10%, who drive 50% of spending”) lose their income, the prime mortgage market previously assumed to be safe could begin to show cracks.

• The Concept of “Ghost GDP”: The piece introduced an interesting economic concept Ghost GDP. This is a scenario where the economy’s output continues to rise, but demand collapses because humans no longer have wages. Decoupling is a nightmare for the traditional central bank model.

• Shifting the SaaS Narrative: For the last ~20 years was considered the ideal business model. Citrini argues that agentic coding (like Claude code or Cursor) makes software so cheap to produce that the “moat” has evaporated. This has forced venture capitalists and equity analysts to completely rethink how they value software companies.

The Thesis of “Ghost GDP”. Does it actually hold up?

Decoupling Output from Demand

In a traditional economy GDP grows because people work, get paid, and spend. In Citrini’s Ghost GDP scenario, this feedback loop breaks:

• Production without a Payroll: Ai agents will produce software, analysis, and logistics at far lower marginal costs. This keeps Output High.

• The Gap in Consumption: AI isn’t a person it doesn’t need to buy coffee, pay rent, take vacations, the “velocity of the money” slows down.

• The Anomaly: On paper, the country in wealthier than ever. In reality the median household is living through a depression as the value of their labor has crashed.

Does the Thesis Hold Up?

Personally I’m not sure I lean on the side of no but I can see the reasons and scenarios for when it does hold up I have a few reasons for each:

For the case of no

• Say’s Law: Simply put “supply creates its own demand”. If AI makes everything 90% cheaper people will simply spend their remaining on things that haven’t even been invented yet.

• Jevons Paradox: Historically, when a resource becomes more efficient, we don’t use it less; rather we use vastly more. This could lead to new industries that require new types of human oversight.

• Policy Intervention: The Ghost GDP thesis assumes a passive government. In reality, a collapse in demand would trigger large scale government intervention I think similar to The New Deal with infrastructure projects to employ people or UBI (Universal Basic Income) to breathe life back into the economy (though I doubt our politicians could any of this effectively).

For the case of yes

• Labor Share of Income: We are already seeing a decline in the labor share of GDP. If ai continues to automate high-value cognitive tasks (like software engineering, data science, law, supply chain management) the capital owners capture 100% of the gains, leaving the “Ghost” of the growth behind for everyone else.

• Asset Inflation vs. Real Economy: We see this in the stock market today. The S&P 500 can hit all-time highs while consumer sentiment and retail sales plummet. This would be the Ghost GDP in action.

Why this Matters for the Future of Tech.

The Ghost GDP thesis suggests that we are moving from the era of digital scarcity to well digital abundance. In our world being able to build is no longer as valuable of a commodity. The value moves to three critical areas:

• Ground Truth: When AI can generate 10,000 lines of code or a 50-page market analysis in seconds, the world will be flooded with content so high volume, low effort output (Dead Internet Theory).

  • Technical roles I think will become more of the auditors, making sure generated output isn’t hallucination or a loop that looks correct but fails when put into production, so most of will just become QA.

• Governance of Architecture: If software becomes purely a commodity, the individual components matter less than the “Blueprint”.

  • Purely developing features will be seen as low margin activity.

  • Seniority will also be defined by the ability to manage system complexity. People who understand disparate agents interact and how to prevent failure from cascading will be the new idealized version of the current “10x engineer”.

• The Human Premium: If the “Ghost” in Ghost GDP is referring to the lack of human demand and accountability. As automation scales, the market will place a premium on accountability.

  • An example would be say in medical diagnostics or trading if the tools, there must be human to explain why, fix the logic and take legal responsibility.

The Current Pivot

For the twenty or so years the tech world has been paid to solve the how (How do we build this gap? How do we scale this database or service?). In a world where Ghost GDP is a reality, AI will answer the “How” immediately. Instead our responsibility shifts towards:

• What problems are actually worth solving?

• Why should we trust one specific models results and findings over anothers?

Ultimately this thesis still remains a possibility, but whether or not our economic reality depends on where we value maximum efficiency or stability. Personally I think we will lean towards maximizing efficiency and pick up the pieces of stability after.

The Spiral of Intelligence Displacement. How Realistic is the Timeline?

This is in my opinion the most controversial part of Citrini’s thesis, because suggesting white collar job loss is a mathematical inevitability. They propose this to be a feedback loop.

The Logic of the Spiral.

The spiral operates on the economic premise: Profits from automation do not create new jobs; they find more automation.

• First Phase: Substitution (2025-2026): Companies have already begun to integrate Claude Code, Gemini, Cursor, or other specialized agents with the supervision of mid-level and senior developers to handle entry level tasks (basic data cleaning, Tier-1 support, legal drafting).

• Second Phase: Profit Recycling (2026-2027): Every dollar saved on a humans salary isn’t being reinvested into new human roles a recent example being oracles recent layoffs of ~30,000 engineers. Instead, flowing that capital towards compute, to ensure hyperscalers have continued funding to purchase more datacenters and compute.

• Third Phase: Displacement (2027-2028): As AI capability continues to increase at its current rate, it will begin to move up the ladder, automating management and coordination roles that were thought to be safe.

How realistic is the 2028 Timeline?

I believe that the timeline Citrini laid out is far too aggressive but the effects it mentions I think will happen. Data from 2026 shows that junior hiring has collapsed the number of entry level tech employees at large firms was cut in half compared to 2023. Companies don’t seem to be firing everyone just skipping over the most recent classes of new graduates.

Benchmarks like METR show that the duration of autonomous work that AI can handle has been doubling every few months. If this continue at this rate then yes AI will be able to handle full month continuous project management by mid-2028. But this is making a lot of assumptions I doubt AI growth will stay consistent even for the next year we are already approaching the limits of the existing methods and findings way to optimize token consumptions, compute and power availability are eventually not going to have as drastic of an impact. I think the more realistic timeline for this will be the mid 2030s.

The markets have also been pricing themselves accordingly in response to all of this AI hype and fear the “Black Friday” tech sell-off in February 2026 proved that wall street is starting to price in a “SaaS Graveyard” where agentic AI renders middleman software obsolete.

This what has happened and what I think will continue to happen based on Citrini’s timeline though there is some evidence to support it that comes off quite alarmist rather than just bearish.

The Wall Street giants like Citadel and Goldman Sachs argue that “The Spiral” ignores human friction. Integrating AI into legacy corporate systems is messy, slow, and expensive. They believe that the weeks to destroy, years to build gap is overestimated. Also as I have mentioned earlier with Jevons Paradox making a resource like intelligence cheaper has always lead to increased use of it not less. There is a world where we dont have fewer workers just everyone doing 100x more work (I highly doubt this reality will ever come). There also the role and angle governments play the EU AI Act and other similar US regulations are forcing a human requirement for high risk industries like finance, law, and healthcare, to act as a speed limit on this spiral.

My own Verdict.

The timeline for total intelligence displacement by 2028 is far too fast human systems and regulations are far too much of bureaucratic mess to change in the next 24 months. However, the junior displacement portion of the spiral is already here and will continue to stay for the foreseeable future. But the real danger to the tech world is the pipeline from junior to senior collapsing as typical junior work is being automated away.

What This Means for New Tech Grads Specifically .

This intelligence displacement creates a paradox: the barrier to entry for tech has never been lower, but the barrier to employment is at an all time high. If Citrini’s thesis holds then traditional junior developer roles are heavily endangered and we may not even exist in a few years. This means a few different things for those of us entering the 2026 job market.

• The Experience Gap Crisis: Historically companies would hire juniors as an investment, they performed lower-value tasks while learning the ropes the become senior engineers.

  • Displacement: AI agents now can perform the majority of learning tasks for juniors at faster speeds, at a fraction of the cost and time investment.

  • Consequences: Companies are now increasingly reluctant to pay for a juniors learning phase. New grads are now expected to enter the workforce with a mid-level engineer skill set and architectural mindset compared to even 5-6 years ago.

• Shifting from Semantic skills: Prior to the advent and large scale adoption of ai and llm’s just knowing the basic syntax of a language was enough to at least get you in the door. Now syntax is more of a commodity then a skill.

  • Systems Observability: Understanding why a AI generated system is failing.

    • Uncertainty Quantification: Utilizing statistical frameworks to measure how much we should trust AI output.

    • Context Engineering: Inputting the right business logic and constraints into a model to get viable results.

• Full Stack Orchestration Model: The days of specialized juniors is ending. To survive the displacement, new grads will have to pivot towards become Generalist Orchestrators a trend that had already been occurring before the popularity of AI just being sped up now. Instead of being a coder or engineer, we will become in my eyes a project manager who will manage legions of agents. But in order to do this you must understand the full lifecycle from data ingestion all the way to the end user interface, because AI in its current form can handle the individual steps but not the cohesion between them.

The Intermediation Collapse Argument Compelling but Incomplete.

The intermediation collapse is Citrini’s most aggressive claim. It suggests that AI won’t just replace workers; it will get rid of middlemen companies that currently sit a between customers and a service. If there are AI agents that can handle booking trips, managing your subscriptions and payments, and find the cheapest insurance, then what reason is there for companies like Visa, Expedia, or your local insurance agent to even exist? While it is a compelling argument I think Citrini overlooks real world friction.

Why the argument is compelling.

• The Toll Collection Problem: Many of the worlds most profitable companies are essentially toll collectors (credit card networks, food delivery apps). Their business model thrives because finding information and coordinating people is difficult.

• AI Solvent: If an AI agent can perform discovery and verification instantly than the value of middlemen drops to zero.

• Uber Example: If an autonomous vehicle fleet is managed by an open-source AI protocol, the 25%-30% take rate Uber charges to manage the marketplace becomes an unnecessary tax.

Why the argument is incomplete.

• Trust & Liability Barrier: Intermediaries don’t just provide information; they provide an alternative. If your AI agent books a flight that doesn’t exist or transfers money to a fraudulent account, who do you sue?

  • Organizations like Visa or Marriott provide someone to assume accountability.

  • Consumer are often willing to pay a trust premium to ensure that if something goes wrong, a human institution is legally and financially responsible.

• Regulatory Capture: The biggest middlemen are often protected by government regulation.

  • Banking, Healthcare, and real estate are not “pure” markets; they are heavily regulated.

  • Even if an AI can perform a title search or a medical diagnosis, it may not be legally allowed to do so without a licensed human intermediary for another decade.

• Curation Paradox: In a world of infinite AI generated options (Ghost GDP abundance), humans actually crave human curated signals.

  • We don’t just want the “most efficient” restaurant; we want the one that other humans are talking about.

  • Intermediaries like editors, influencers, and boutique agencies provide a status signal that AI can’t replicate because it simply doesn’t have social stakes.

Fragmented Intermediation: Instead of a total collapse I see it more as a hollowing out.

• Low End: Low trust intermediation (traveling books, basic tax prep) will likely collapse into AI protocols.

• High End: High trust, high-stakes intermediation (legal strategy, wealth management, specialized engineering subfields) will become even more expensive and human-centric.

The danger for the tech workforce isn’t that all intermediaries will disappear , but that the entry level versions of these roles will be the first to go and that there will no one to learn and take on the business.

Private Credit & the Mortgage Market. The Systemic Risk Case.

In my opinion the most terrifying aspect of Citrini’s thesis is the Systemic Credit Collapse. Where the 2028 Global Intelligence Crisis moves from a tech layoff story to a financial catastrophe of the same caliber or even beyond 2008. The argument centers on two highly leveraged markets: Private Credit, and Prime Mortages

The Private Credit (roach) Problem:

Private credit (non-bank lending to corporations) has ballooned into a $3.5 trillion industry in 2026. Because it is largely unregulated, and doesn’t trade on public exchanges, we can consider it essentially black box.

AI Vulnerability

Over the last few years, private credit funds have heavily targeted the enterprise software (SaaS) sector.

• The Thesis: If Citrini is right and Agentic AI makes building software 90% cheaper, the revenue of debt-ridden SaaS companies evaporates.

• Default Loop: As of early 2026, default rates in private credit are projected to climb. When these software firms can’t pay their debts, the private credit funds face massive redemption requests from investors..

• The Freeze: In Q1 of 2026, we’ve already seen funds like BlackRock and Blue Owl impose withdrawal limits. This liquidity mismatch where investors want their money out but the loans are stuck in failing software companies is eerily reminiscent of 2008.

Prime Mortgage Crack

Citrini argues that unlike 2008 (which was caused by subprime borrowers who couldn’t afford their homes), the 2028 crisis will be a Prime Mortgage Crisis.

The Displacement of the Top 10%

The Intelligence Displacement Spiral specifically targets high-income professionals the people who hold the largest mortgages in the US.

• Income Cliff: If a software engineer or corporate lawyer loses their 250k salary and are forced into the “gig” economy they can no longer sustain their $5000/month mortgage.

• Asset Devaluation: As these high-end homes hit the market simultaneously, real estate prices in tech hubs (San Francisco, Seattle) could crater.

• Systemic Link: These “Prime” mortgages are the bedrock of the global banking systems. If they fail Ghost GDP ends up becoming the total wipeout of household wealth.

Why this Matters

The reason that this matters is that the corporate sector and the household sector are linked by these two debt markets.

• AI displaces high earners (Household Risk).

• AI devalues software companies (Private Credit Risk).

• Both stop spending, causing Ghost GDP to manifest into a global depression.

Counter-Arguments

Many Wall Street analysts argue this is a worse case scenario that assumes a total lack of government intervention. They point out that:

• The Fed is already taking action: The Federal Reserve launched an inquiry into private credit exposure in April 2026.

• Jevons Rebuttal: If AI makes software cheaper, it might create more companies and more lending opportunities, offsetting the defaults of the “old” SaaS world.

The Takeaway for the new workforce of 2026

Whether Citrini is right or wrong, the financialization of AI risk is now a permanent part of not just the tech landscape but the wider economy. For those of us entering the market currently, it is not longer enough for us to understand the technology behind AI as it was when we began out schooling; we must understand the underlying economics connected to it. The people who do survive these next few years will eventually end up becoming the best general orchestrators of these ai agents and will be able to survive the volatility of this intelligence spiral.

What the Memo Gets Wrong About the Job Market.

Infinite Productivity Fallacy

Citrini assumes a loop where every dollar saved on human labor is immediately reinvested into AI, which then displaces more labor.

• Ignoring the Law of Diminishing Returns: For the vast majority of business models and technologies, the first 20% of automation provides massive gains, but the next 80% becomes exponentially more difficult and more expensive to integrate.

• My counter-argument: If every dollar spent on AI were infinitely productive, we would achieve a fully automated luxury communism rather than a crisis. Economic history suggests that as one resource (intelligence) becomes cheap, the value of other (physical infrastructure, land, human judgement) skyrockets, creating new categories of work.

Underestimating Organization Friction

The memo assumes that because AI agents exists, every major corporation will eventually fully adopt it.

• Most large enterprises (banks, governments, insurance firms) are notoriously slow. Many are still transitioning to the cloud twenty years after its inception.

• Legal departments, unions, and risk-averse executives act as a natural brake. Even if an AI can do the job of a junior analyst, many firms will keep the human role for accountability and liability they need someone to blame if a system fails.

The rise of Solopreneurs

The memo views displacement as a one-way street: “Corporate worker becomes unemployed.”

• The same tools that allow a company to fire 100 people allow 1 of those people to start a competing company with the overhead of zero. An example of this currently is Medvi a 1.8 billion dollar run by 2 brothers everything else is just fleets of agents they manage.

• We are seeing the slow rise of the company of one, where displaced tech workers use AI to launch micro-SaaS businesses or specialized consultancies, effectively decentralizing the economy rather than destroying it.

Ultimately, Citrini’s memo matters because it identifies the risks of a transition period, but it may fail as a long-term forecast because it treats the economy like a rigid machine rather than an adaptive ecosystem.

The Policy Response Section Honestly Weak for a Finance Memo.

While Citrini does a great job at identifying the technical and financial aspects , its policy section is lacking. It treat the government as a static bystander rather than a dynamic actor. In a true systemic crisis, the “invisible hand” of the market is usually replaced by the “heavy hand” of the state.

The Fiscal Dominance Blind Spot

The memo assumes that the government will simply watch as the mortgage and private credit markets collapse.

• We are currently in an era of Fiscal Dominance. Since 2020, the precedent has been set: if a systemic pillar (like the repo market or regional banks) wobbles, the Treasury and the Fed step in with unlimited liquidity.

• In a “Ghost GDP” scenario, the government doesn’t just let the economy hollow out; they move toward “Direct Monetization.” This could mean the Fed directly purchasing private credit’s off-chain debt assets or creating a “Mortgage Backstop” that prevents the prime market from ever hitting the “income cliff” Citrini fears.

The Rise of Luddite Legislation

The memo treats AI adoption as a purely economic decision. It ignores the Political Economy of Employment.

• Politicians do not win elections on efficiency; they win on jobs. We are already seeing the first wave of “Human Labor Tax Credits”. If the displacement spiral moves too fast, expect laws and regulations that mandate a certain ratio of human-to-AI workers for government contractors or federally insured banks. This turns the spiral into a slow crawl.

Sovereign AI & National Security

Citrini views AI as a corporate tool that destroys SaaS moats. The government views AI as a strategic national asset.

• If the intelligence displacement spiral threatens to bankrupt the tech sector, the U.S. government will likely treat major AI labs like defense utilities.

• Instead of a collapse, we may see a nationalization of compute. The government could subsidize the very SaaS companies Citrini thinks will die, repurposing them as infrastructure for a sovereign AI stack to ensure the U.S. maintains its lead over global rivals.

By underestimating the policy response, the memo creates a “False Certainty” regarding the 2028 crash. History shows that when the financial system faces an existential threat from technology, the state doesn’t just fold; it re-regulates. The 2028 crisis may not end in a depression, but in a heavily managed, highly regulated command economy where the ghost is put on a very short leash.

The Structural Optimism at the End is it Valid?

The optimism found at the end of Citrini’s memo is perhaps its most polarizing feature. After painting a 50-page picture of financial ruin, the authors pivot towards a vision of radical prosperity. Whether this optimism is valid depends on whether you believe the economy can survive a “Gap Year” (or a gap decade) between the collapse of old jobs and the arrival of the new world.

• The Argument for Deflationary Prosperity: The memo’s optimism rests on the idea of Cost Collapse. If intelligence is the primary cost of everything (legal, medical, software, logistics), and intelligence becomes free, then the “cost of living” eventually approaches zero.

  • You don’t need a high salary if your rent, food, and energy are managed by hyper-efficient AI agents and autonomous robotics.

  • We see early signs of this in digital goods today. The cost it takes to train and pay junior developers that cost hundreds of thousands of dollars in 2022 now costs the price of a senior developer and a claude code subscription. If that trend hits the physical world (via AI-managed energy and logistics), the optimism makes sense.

• Why this Optimism is Frequently Challenged: Critics most notably in the Citadel Securities rebuttal argues that Citrini’s optimism ignores the transition tax. Even if the destination is a utopia, the journey could be a wasteland. History shows that during the First Industrial Revolution, output per worker skyrocketed immediately, but real wages stagnated for 60 years(a period called the Engels’ Pause).

  • If we are entering a modern “Engels’ Pause” the structural optimism won’t matter for the people living through 2026–2040. They will experience the displacement without the benefits of the cost collapse.

• The Human Premium: The most valid part of the structural optimism is the shift in Human Value. Citrini argues that once “execution” is automated, the world will place an infinite premium on:

  • Taste & Curation: In a world of Infinite AI-Generated content, we will pay more for things vetted by humans we trust.

  • High-Stakes Accountability: We will always want a human to own decisions involving life, liberty, and capital.

Is this valid?

The optimism is theoretically valid but practically dangerous. It assumes a “frictionless” move to a new system. For the workforce of 2026, the memo is a reminder that the “Old World” (selling your time for cognitive labor) is dying, but the “New World” (the era of deflationary prosperity) isn’t fully built yet. The goal for everyone entering the market now regardless of where they are is whether or not they want to be the victim of this transition or the one who takes advantage of it.