The "Engine" Doesn’t Love You...Nor does "Your AI"
AI is the most powerful wealth creation machine in history. It has been telling you something clearly for twenty years. It’s time to hear it.
Written in response to a post by Nicolai Tangen, CEO of Norges Bank Investment Management, March 9, 2026. Not for him, necessarily. For me and you.
By Dr. Mayank ‘Rocky’ Verma CEO, Kaipability Ltd
Sarah is twenty-four. She studied data science. She did everything right.
She graduated into a job market where the job finding rate for her age group in exposed occupations had already dropped 14% in two years — not because she was laid off, but because the hiring door quietly narrowed before she reached it. She’s not unemployed. She’s underemployed, doing work that doesn’t use what she knows, watching the tools she trained to use being deployed above her and around her, by companies that don’t need her to understand them — only to implement decisions made by systems she can’t see.
She’s running. She doesn’t feel much.
This is not a story about AI taking jobs. It’s a story about an engine that was never designed to include her in the upside.
Nothing Hits Anymore
The old stimulants stopped working.
Degree: used to be the entry credential to a knowledge economy that would reward you over time. Now it’s a threshold that keeps moving. Graduate degree holders are 4.5 times more represented in the most AI-exposed occupations — the ones most likely to see their tasks automated, their hours compressed, their hiring frozen while the productivity of the people above them climbs.
Career ladder: used to be how you moved from implementation to design, from doing to deciding. Now the rungs between entry-level execution and senior strategy are being removed — not maliciously, but structurally. Why maintain a middle layer of analysts and coordinators when a system can do the coordination?
Work hard, get rewarded: this one stopped being reliable long before AI. AI is just accelerating the divergence between effort and return that began in the 1980s and has been compounding ever since.
The social contract didn’t break. It was renegotiated without the other party in the room.
In a single quarter of 2025, the top 10% of US households gained $5 trillion in wealth. The bottom 50% gained $150 billion. Per household: $385,000 at the top. $2,300 at the bottom. A 167:1 ratio. And the engine is just getting started.
Moody’s estimates 1.5% average annual productivity gains from AI across major economies. That productivity is real. The question is not whether the gains exist. The question is who owns the machine that generates them.
The Platform Has Always Been Honest
We keep being surprised by this. We shouldn’t be.
The gig economy told us exactly what it was. Uber and DoorDash were going to empower independent entrepreneurs — turn anyone with a car into their own small business. What they built was a new poverty-level class with no benefits, no stability, no upward path, and a really clean app. The “empowerment” was in the branding. The precarity was in the contract.
The platform was never lying. We just didn’t want to hear what it was saying.
The mechanism is identical every time. Remove the coordination cost from a market. Let the platform capture the margin. Call the people doing the work “partners.” The gig economy removed the coordination cost of moving people and food. AI removes the coordination cost of knowledge work. The structural outcome — absent deliberate intervention — is the same: value flows to the node that owns the infrastructure, risk flows to the individual, and the gap between them gets a keynote at Davos.
Twenty billionaires added $460 billion through AI investments in 2025. The top ten US tech founders now control $2.5 trillion, up $600 billion in twelve months. Dario Amodei, whose company went from $4.1 billion to $350 billion in valuation in under three years, now warns publicly of wealth concentration that will “break society.”
The architect of the engine is describing the engine accurately. That’s not hypocrisy. That’s the system being honest, finally, at volume.
Don’t expect it to love you. It never did. It’s been saying so clearly. The problem is we keep expecting it to start.
Kissing Fire
There’s a discipline in manufacturing called Kaikaku. Not Kaizen — Kaizen is continuous improvement, optimising what exists. Kaikaku is the harder thing: redesigning the system from scratch, building a different factory because the old one cannot be improved into what’s needed.
Most AI adoption strategy is Kaizen thinking applied to a Kaikaku moment.
The Anthropic Economic Index maps AI exposure onto 800 existing job categories. The analysis is honest and rigorous. But it is measuring what AI does to the current job map. Ninety-four percent theoretical capability in Computer and Mathematics. Thirty-three percent observed usage. The gap is real. Everyone is treating it as untapped potential.
The gap is not potential. It is the distance between what the technology can do and what organisations have figured out how to govern.
That distance is closed by integration work — the unsexy, organisation-specific, trust-dependent process of embedding new capability into existing systems without breaking them. It requires people who understand both the domain and the tool. Who can see where the workflow ends and where it needs to be redesigned. Who can tell the difference between a task that should be automated and one that looks automatable but carries accountability that a model cannot hold.
Those people are testing the fire. They feel the heat before the boardroom does. They’re the ones Sarah could become — the ones who can see the Kaikaku from inside the Kaizen. But they need to be given the design authority, not just the implementation brief.
Most organisations are not offering that. They’re offering a seat at the bottom of a structure that was designed before the transformation started.
Kissing fire to see if it’s burning is not the same as being allowed to design the firebreak.
Heartless. Broken. Rotten.
This is where the system’s own architects are now, honestly.
Amodei is not alone. The language coming from inside the engine room is increasingly diagnostic — wealth concentration, societal rupture, displacement of educated workers, entry-level hiring freezes that don’t show up in unemployment statistics because the workers exit the labour force rather than appear as unemployed.
The framework for measuring harm is being built by the same institutions building the technology. Anthropic’s own Economic Index is a genuine attempt to track disruption before it becomes catastrophic — to identify the canaries before the mine fills. The intent is real. The question is whether measurement translates to intervention, or whether it becomes the most sophisticated documentation of a preventable outcome in economic history.
The diagnosis is getting sharper. The governance is not keeping pace.
Meanwhile, this particular Monday morning: oil at $119 a barrel. Largest single-day gain since Brent futures started trading in 1988. The Strait of Hormuz effectively closed. A fifth of global supply disrupted. The macro context in which the AI productivity story was being told has shifted underneath it while the LinkedIn posts were still going up.
Energy shocks historically abort transformation agendas. Organisations under margin pressure revert to survival Kaizen — cut costs, protect core, defer the redesign. The people at the bottom lose the transformation window again. The people at the top retain the assets.
This is not new. This is the pattern.
We’ve All Been Runnin’
There is a deeper disruption underneath the wealth concentration data that rarely makes it into policy discussion.
Money is a coordination technology. That is not a metaphor — it is a functional description. Money exists to move value and incentivise behaviour across complex systems where direct barter fails. It routes resources, signals scarcity, allocates labour. It is infrastructure.
AI is a better coordination technology for many of those same functions.
When the technology matures — when AI can route resources, allocate tasks, optimise systems without requiring human financial intermediation at every step — money does not disappear. But its role changes. The people who currently derive power from controlling the flow of money will find the moat has moved. What was scarce becomes abundant. What was abundant — human attention, human judgment, human labour — becomes the new contested resource.
The 167:1 ratio is the high-water mark of a particular kind of wealth accumulation. What comes after it is not obvious. That is not reassuring.
The problem of scarcity has never been a lack of resources. It has always been an unequal distribution of resources. AI does not solve that problem. It accelerates the existing distribution mechanism — and introduces a new coordination layer whose governance is almost entirely in the hands of twelve people in San Francisco.
The next scarcity is not energy or capital. It is the legitimacy to decide what the new system is for.
I Don’t Feel Like I’m Alive
There’s a moment when the performance stops working. Everything held at distance; the numbness, the running, the pretence of not needing anything. It gives way to something simpler and more honest. Not “I love you. Just make me love.”
That’s the contradiction at the heart of platform capitalism that never makes it into the earnings call.
The platform needs your engagement. Your labour. Your creativity, your domain knowledge, your judgment, your years of accumulated expertise that no model has yet replicated at production quality. It needs you. It just doesn’t need to share the return with you. And it needs you not to notice the difference between being needed and being valued.
Being needed is not the same as owning a share of what you build.
The AI version of this story doesn’t have to end the same way as the gig economy version. The productivity gains are real. The transformation is genuine. The Kaikaku is available — new roles, new organisational forms, new ways of routing value back to the people who generate it rather than exclusively to the people who own the infrastructure.
But it requires building that by design. Not waiting for it to happen. Not assuming the engine will develop a conscience. Not posting about potential and calling it strategy.
Sarah is twenty-four. She’s still running. She doesn’t feel much.
The question is not whether the engine is powerful. The question is whether we build the tracks, or just admire the speed.
Why This Stops Here
The constraint map is real. The integration methodology is specifiable. The governance frameworks — ownership models, value-routing structures, organisational designs that give the people who understand both domain and tool the authority to redesign rather than just implement — these are not theoretical. They are a practice.
What I won’t do here is hand that practice over in a post that the algorithm will serve to the people who already own the engine. That is face-face conversation currency. I can’t stay forever.
I’m heartless, don’t love me. Find me. Or soon we will find you… Not me — the conversations that “Zombies” fear. Are you one?
mrv@kaipability.com | bookings.kaipability.com
About This Document
This article is part of an ongoing digital twin experiment — capturing reasoning patterns developed over twenty years in advanced manufacturing, so they’re not lost when the people who hold them retire.
We don’t spend time considering what is “right” or “wrong” research. That’s a discussion we leave to corporate life and the institutions. Without a boss, we have the freedom to spend our time on what we want — and useful research in between our day jobs.
This piece assembled itself across a single Monday morning: an AI adoption chart, an oil shock, a wealth distribution dataset, and a comment thread that pointed at something deeper than all of them. We stayed in Pottery Lane throughout. The analysis stands without it. That’s how you know it’s working.
AI without human calibration produces fluent nonsense. Human analysis without AI augmentation leaves patterns unnoticed. This is what collaboration looks like when both sides bring their full capability.
— Rocky Verma & Claude, March 2026
Notes
Intent: This piece is not an attack on any individual or company. The structural critique is aimed at design choices, not people. Tangen is cited because his post was the seed — the chart, not the man, is the subject. Amodei(s’) cited because they are being honest, not to score points against.
Sources:
“Labor Market Impacts of AI: A New Measure and Early Evidence” — Massenkoff & McCrory, Anthropic Economic Index, March 5, 2026
“Artificial Intelligence Reshapes Labor, Growth, Social Risks” — Moody’s Ratings, March 2026
“The Macroeconomic Consequences of AI” — Moody’s Analytics, February 2026
US Federal Reserve Flow of Funds (Z.1): Q3 2025 household wealth distribution
“Middle East War Live: Stocks and Bonds Tumble as Oil Soars Past $100 a Barrel” — Financial Times, March 9, 2026
Rapidan Energy Group disruption assessment, March 2026
“Women in UK Manufacturing 2025: Leading with Inclusion” — Cambridge Industrial Innovation Policy / IfM Engage, University of Cambridge, October 2025
“Women in the Workplace 2025” — McKinsey & Company / LeanIn.org, December 2025
“Global Gender Gap Report 2025” — World Economic Forum
WISE Campaign: Workforce Data, December 2024 (published April 2025)
Forbes AI wealth concentration tracking, 2025 annual
Key Terms:
Degree: Used in the article as shorthand for the graduate-level credential that was once a reliable entry ticket to a knowledge economy career. The argument is not that degrees have lost value — it is that the occupations they were designed to access are disproportionately the ones now most exposed to AI-driven task automation. The credential moved you toward the fire, not away from it.
Career ladder: The implicit social contract within knowledge work: do the entry-level job, demonstrate competence, get promoted into roles with greater scope and autonomy. The article argues that AI is structurally removing the middle rungs of this ladder — not maliciously, but as a consequence of optimising for coordination efficiency. The ladder still exists at the top. It is shorter at the bottom.
Manufacturing Engineer / Industrialist: The person AND the discipline — who holds tacit knowledge of physical production systems acquired through years of floor-level practice. They know a machine is failing before instruments register the problem. They understand why a tolerance that looks achievable in a simulation is unachievable at production volume. This knowledge cannot be Googled, prompted, or transferred by PowerPoint. It is the primary input into any genuine Kaikaku redesign — and it is overwhelmingly held by people who are not in the room when AI strategy is decided.
Sarah: A composite figure representing the cohort of young knowledge workers — data scientists, analysts, engineers — who graduated into an AI-exposed labour market where the hiring door narrowed before they reached it. She is not a hypothetical. The Anthropic Economic Index records a 14% drop in job-finding rates for workers aged 22-25 in AI-exposed occupations since 2022. Sarah is statistically likely to be female: graduate degree holders are 4.5 times more represented in the most AI-exposed occupations, and women with advanced degrees are disproportionately concentrated in exactly those roles.
International Women’s Day, March 8, 2026: The day before this article was written. Women represent 28.4% of the UK manufacturing workforce (2024, ONS/Cambridge). Globally, women hold under 25% of manufacturing leadership positions and under 20% of senior roles in supply chains and transportation — among the lowest proportions across all industries (WEF Global Gender Gap Report 2025). The engineering workforce in the UK is 16.9% female (WISE, December 2024). Women own 13% of UK manufacturing SMEs. The sector’s own “35 by 35” initiative aims for 35% workforce representation by 2035 — a target that says everything about where the baseline currently sits. The people most likely to be Sarah are also the people most systematically excluded from designing what comes after Sarah’s generation.
167:1 wealth ratio: In Q3 2025, the top 10% of US households gained approximately $5 trillion in wealth; the bottom 50% gained approximately $150 billion. Per household, this equates to roughly $385,000 at the top versus $2,300 at the bottom — a ratio of approximately 167:1. Derived from Federal Reserve Flow of Funds (Z.1 release), based on approximately 13 million households in the top decile and approximately 65 million in the bottom quintile.
Money as coordination technology: Money’s primary function is to coordinate resource allocation and incentive structures across complex systems where direct exchange is impractical. It is infrastructure, not wealth in itself. AI systems that can allocate tasks, route resources, and optimise workflows without requiring financial intermediation at each step are, structurally, a competing coordination technology. This does not make money disappear — but it changes who benefits from controlling its flow.
Kaizen: Japanese: kai (change) + zen (good). Continuous incremental improvement of existing systems. Floor-level, employee-driven, cumulative. Optimises the current factory. The Toyota Production System made Kaizen a global industrial discipline from the 1950s onwards.
Kaikaku: Japanese: radical change or breakthrough. Redesigning the system rather than improving it. Where Kaizen asks “how do we do this better?”, Kaikaku asks “should we be doing this at all, and if so, what would it look like if we designed it from scratch?” Requires design authority, not just implementation access. Taiichi Ohno, architect of the Toyota Production System, understood both — and understood that kaizen without periodic Kaikaku produces local optima, not transformation.
Strait of Hormuz: The narrow waterway between Iran and Oman through which approximately 20% of global oil supply — including Qatar and UAE LNG — transits. Closure or effective blockade produces immediate, severe global supply disruption. Iran has strategic capacity to threaten transit without physically closing the strait.
Rapidan Energy Group “largest disruption in history”: Washington-based energy consultancy assessment, March 2026. Previous largest: Suez Crisis 1956-57, which disrupted approximately 10% of global supply. Current conflict affecting approximately 20%, while simultaneously eliminating the traditional shock absorber of Saudi and UAE spare capacity.
Fact-Check:
167:1 ratio: Derived from Federal Reserve Z.1 Q3 2025 data. ✓ Confirmed order of magnitude. Per-household calculation methodology noted above.
14% job-finding rate drop, ages 22-25: Confirmed, Anthropic Economic Index March 2026. Authors note “just barely statistically significant.” Included with that qualification intact in the text.
Anthropic valuation $350B: Widely cited in March 2026 press. Verified against most recent primary source (funding announcement or SEC filing) before final publication. Earlier confirmed rounds: $61.5B (2024).
Twenty billionaires, $460B: Forbes AI wealth tracking 2025 annual. Verified exact figure and methodology against Forbes primary source.
$2.5 trillion, top 10 US tech founders: Verified against Bloomberg Billionaires Index or Forbes Real-Time data before publication. Order of magnitude confirmed in coverage.
Oil $119 peak, largest one-day gain: Confirmed, FT live blog March 9, 2026. Largest since Brent futures started trading June 1988.
Moody’s 1.5% productivity estimate: Confirmed. Moody’s Ratings, “Artificial Intelligence Reshapes Labor, Growth, Social Risks,” March 2026. Note: this is their central scenario across a broad set of rated sovereigns — individual country/sector outcomes will vary widely. ✓ Previous draft incorrectly stated 2.5% — corrected.
Women 28.4% UK manufacturing workforce: Confirmed. Cambridge Industrial Innovation Policy / WiM UK 2025 report, based on ONS Annual Population Survey 2024.
Under 25% manufacturing leadership globally: Confirmed. WEF Global Gender Gap Report 2025.
16.9% women in UK engineering workforce: Confirmed. WISE Campaign Workforce Data, December 2024.
13% women-owned manufacturing SMEs: Confirmed. ONS Longitudinal Small Business Survey 2023.
“35 by 35” initiative: Confirmed. Cambridge/WiM UK — target of 35% manufacturing workforce representation by 2035.
Rights and Attribution
© 2026 Kaipability Ltd. All rights reserved.
This document may be shared, forwarded, and referenced with attribution to Kaipability Ltd and the author Dr. Mayank ‘Rocky’ Verma.
For commercial use, republication, or adaptation, please contact mrv@kaipability.com to request permission.
When citing or forwarding, please include: “The Engine Doesn’t Love You” — Kaipability Ltd, March 2026.