Contra Yang, et al

This morning I woke up to a text from my dad, who was asking for my opinion on this piece from Andrew Yang. I wrote him a shorter response that contained a decent chunk of what I’m about to say, but it turns out I had a lot more to say about the topic, and when I finally got done writing it all down, I had what almost looked like a blog post. Figured I might as well flesh it out, and here we are.

I try to read these viral AI-displacement pieces with an open but critical eye; looking for what’s genuinely new versus what’s just repackaged anxiety, and trying to separate the claims that hold up under scrutiny from the ones that fall apart one you bring the temperature down a few degrees. I read Yang’s piece with that spirit in mind.

I think the basic point of Yang’s piece is right: white collar work is information processing, AI is good at information processing, and the stock market will reward companies that figure out how to do more with fewer people. I don’t think that’s a particularly original take, and people in my industry have been frothing about this (on Twitter, on LinkedIn) for literally years. Maybe it’s hitting mainstream politics finally.

But his speculations on timelines are pretty insane. “20-50% of 70 million white-collar jobs” gone in “the next several years,” millions displaced in 12-18 months – based on what? A conversation with one CEO? Talk about anecdotes laundered into predictions. And while influencers in more tech-native sphere aren’t innocent of making these types of claims too (e.g. AI 2027¹), the general vibe of this discourse feels like shock and awe over substance. Straight-line extrapolation dressed up as forecasting.

That was my biggest complaint the whole thing, really: the emotional engineering. The timbre. Yang frames someone in his family building a website in minutes as evidence that designers are obsolete, but anyone who ships software knows the demo is maybe 20% of the actual work. He cites mortgage delinquency charts as though AI is already cratering the housing market, but the actual NY Fed data tells a very different story². He names it “the Fuckening” because it “feels more visceral.” He’s made a career out of being the UBI politician; he links his book tour dates at the bottom. I don’t want to be uncharitable, but it’s worth noting that Yang’s financial incentives are perfectly aligned with maximizing alarm: the scarier the story, the more urgent the book feels, the more relevant the policy proposal becomes. That doesn’t make him wrong, but it does mean we should be especially careful about separating the signal from the sales pitch before engaging with the substance.

He also makes the classic non-tech mistake (intentional misdirection?) of framing AI demos like they’re real, load-bearing parts of software infrastructure. Yes, someone built a website in minutes. But the demo is maybe 20% (or less) of the actual work; the other 80% is edge cases, integration, compliance, error handling, all the stuff that makes things actually work in production. AI is still bad at that part, and I think there’s a meaningful reason it’s going to stay bad at it for a while that’s worth explaining.

The way these models improve is through evaluation: you need to be able to measure whether the model is getting better at a task in order to train it to be better at that task. For the demo stuff, evals are relatively straightforward. “Did the model produce working code that compiles and passes these test cases?” You can answer that programmatically. But most knowledge work isn’t like that. Most knowledge work is a bundle of tasks held together by judgment, context, and institutional memory, and the eval that would capture whether AI is doing the whole job well basically doesn’t exist.

This is Goodhart’s Law applied to AI capabilities: when a measure becomes a target, it ceases to be a good measure. AI benchmarks are saturating – SWE-bench scores went from 33% to over 70% in a single year³ – and labs are increasingly optimizing for the benchmarks rather than for the messy, situated work the benchmarks are supposed to proxy for. Oxford researchers reviewed 445 AI benchmarks and found that most don’t actually measure what they claim to measure, suffering from vague definitions and absent statistical validation.⁴ The model gets better at the test without necessarily getting better at the job.

And the hardest parts of knowledge work are precisely the parts that resist measurement. You can’t easily write an eval for “did this integration handle the edge case that only surfaces when the legacy billing system sends malformed dates on leap years” or “did this PR account for the implicit constraint that the payments team agreed to in a Slack thread six months ago.” These failures are vast yet specific, context-dependent, and often only recognizable as failures when a real user hits them in a real environment.

The common response here is that bigger context windows will solve this: just give the model the entire codebase, the Slack history, the docs, and let it figure it out. And it’s true that context windows are growing fast. But the bottleneck isn’t having the context; it’s knowing which context matters. An experienced engineer reading a PR doesn’t scan every Slack thread from the last six months; they know, from years of working in this system, that this thread about the payments team’s implicit constraint is relevant while ten thousand others aren’t. That’s not a retrieval problem. It’s a salience problem, one that depends on a mental model of how the system actually works, who made what tradeoffs and why, and what’s likely to break downstream. Throwing more context at a model can actually make this worse, not better, because you’re increasing the noise without improving the model’s ability to identify the signal.

In other words, this is a measurement problem, and measurement problems are slow to solve. You can’t easily evaluate whether a model correctly identified the relevant context, which means you can’t easily train it to get better at that task (Goodhart’s Law again, just at a different layer). I think the next generation of companies building vertical AI tooling will start to crack specific domains, but the generic “AI replaces knowledge worker” story requires solving eval problems that the entire field is still struggling with.

But beside all of that, even if the tech were ready tomorrow, have you ever watched a big company try to adopt any new software? Procurement cycles, compliance reviews, legacy system integration, middle managers fighting to keep headcount. Most Fortune 500s are still finishing cloud migrations they started a decade ago. These predictions always come in too hot. ATMs were supposed to kill bank tellers, spreadsheets were going to eliminate accountants, the internet was going to make offices obsolete by 2005. Every time, the tech changed jobs more than it killed them and new roles showed up that nobody predicted. Our economy might reward signals of efficiency, but in practice the underlying processes take forever.

This time COULD be different because AI is software not hardware, scales way faster, deploys way cheaper. I take that seriously. There’s already data suggesting the economics of software are shifting: SaaS gross margins among public companies have been declining, dropping from around 78% in 2020 to 72% by 2023 as product commoditization and competition compress pricing.⁵ The traditional seat-based SaaS model is under real pressure; if an AI agent can access a database and execute a workflow directly, why are you paying $70/seat/month for a dashboard that sits between a human and that same database? That’s a real structural shift worth watching. But “the economics are shifting” and “definitely catastrophic in 18 months” are very different claims.

Software engineering is probably the industry where this conversation is loudest and most specific, which makes sense: it’s the one closest to the technology itself. It’s also the one I know best, so let me talk about what I’m seeing in software engineering (I’ve been writing about this for a minute).

Specifically, I want to address this question: is software engineering in general just going up one abstraction layer? There’s a version of this argument that sounds clean and reassuring. We went from assembly to C to Python to “just tell the AI what to build,” and every time the previous layer’s practitioners were fine because they moved up. And there’s something to that. Gergely Orosz at The Pragmatic Engineer wrote about how even the creator of Claude Code didn’t open an IDE for an entire month; all his committed code was AI-written.⁶ Senior engineers are already spending less time typing and more time shaping systems (defining specs, reviewing output, making architectural decisions). AI just pushes that trend to its logical conclusion.

But I think the abstraction-layer framing obscures something important about what’s actually valuable in software engineering right now. It’s not “knowing how to code” in the syntactic sense. AI can write a for loop. It can scaffold a React app. It can even do a pretty good first pass at a complex feature if you give it enough context. What it can’t do well is hold the full mental model of a production system in its head: the implicit constraints, the historical decisions, the understanding of why this particular service communicates with that particular database in this particular way, and what breaks if you change it. The Stanford study I’ll get to in a minute found something relevant here: employment for developers aged 22-25 dropped nearly 20% from its late 2022 peak, but employment for workers over 30 in the same AI-exposed roles actually grew 6-12%.⁷ The market is telling us something. The value isn’t in writing code; it’s in the tacit knowledge that comes from years of shipping code in messy real-world environments. AI is great at the codified stuff. The un-codified stuff is where humans still dominate, and it’s where the value is concentrating.

There’s a related point here that I think gets lost in the discourse: not all software engineering is created equal, and AI is going to hit different parts of the industry very differently. I work at a product-led company where engineers are expected to talk to customers, make product decisions, think about activation funnels, and ship features that move business metrics. That kind of work is ambiguous, cross-functional, and deeply contextual. It’s hard to automate because the “right answer” isn’t well-defined and changes constantly based on user behavior and market conditions.

Compare that to programming at e.g. a large insurance company, where software is already more of a commodity – maintaining internal CRUD apps, building reports against legacy databases, implementing well-specified business logic. That work has been getting squeezed for years, first by offshoring, then by low-code tools, now by AI. Or think about the kind of programming that happens at a consulting firm, where you’re building roughly similar applications for different clients over and over. AI eats that for breakfast because the patterns are repetitive and the specifications are relatively concrete.

This isn’t a new divide. The frontier of software engineering has always been different from the commodity middle. What’s changing is that AI is dramatically widening that gap. If your work is primarily translating well-understood requirements into code, you’re in trouble regardless of Yang’s timeline, because that’s exactly what AI does best⁸. If your work involves navigating ambiguity, making judgment calls with incomplete information, and understanding complex sociotechnical systems, you’re probably fine for a long time. Arguably more valuable than ever, because AI is making the easy parts of your job faster while the hard parts remain stubbornly human.

Since we’re talking about data, let’s actually look at some, because the picture is more nuanced than Yang lets on (though it’s not exactly rosy either).

Morgan Stanley surveyed 935 executives across five sectors and found an average 4% net decline in headcount over 12 months, alongside an 11.5% productivity increase.⁹ Notably, U.S. companies actually reported a 2% net gain in jobs; the biggest pain was in the UK at 8% net loss, and concentrated among larger firms. Early-career positions were disproportionately affected, which tracks.

The Stanford Digital Economy Lab study is probably the most rigorous thing out there right now.⁷ Using ADP payroll data covering millions of workers, they found a 13% relative decline in employment for 22-25 year olds in the most AI-exposed occupations since late 2022. For software developers in that age range specifically, the drop was nearly 20% from peak. But (and this is the part Yang would leave out) they also found that employment for older workers in the same roles grew 6-12%, and that jobs where AI augments work rather than automates it haven’t seen similar declines. The adjustment is real, but it’s not uniform, and the “automation vs. augmentation” distinction matters enormously for predicting where this goes.

Challenger, Gray & Christmas tracked 696,000 job cuts in the first five months of 2025, an 80% year-over-year jump.¹⁰ But they attribute this to a cocktail of tariffs, funding cuts, consumer spending shifts, and AI, not AI alone. The World Economic Forum’s 2025 report estimated 92 million jobs displaced by 2030 but 170 million new roles created, for a net gain.¹¹ And a Harvard Business School professor studying this put it well: AI exposure overlaps with about 35% of tasks visible in labor market data, but the history of predicting employment effects from technology is “extraordinarily hard,” and the radiologists we were told to stop training in 2017 are busier than ever.¹²

What does all this tell us? The displacement is real, it’s measurable, and it’s hitting early-career workers first and hardest. But it’s also a 4% net headcount decline and a 13% relative employment drop in specific demographics, not the 20-50% apocalypse Yang is selling. The data supports “meaningful structural change that’s already underway and will accelerate” much more than it supports “the Fuckening.”

Plus like a lot of what he’s describing is also just an acceleration of stuff that’s been happening for years. Knowledge work offshoring, junior roles getting squeezed, bad grad employment numbers. AI is pouring gasoline on existing fires not starting new ones. There is something to this, though: economic transitions hurt the people who built their lives around stability. People who followed the script (school, useful degree, knowledge work career) are going to be disrupted. But I also think that’s just capitalism? Things change! We drive towards efficiency! I don’t think the mindset should ever be “learn a thing once and then coast on it”; the whole point is to be constantly examining yourself, updating your priors, and understanding that what worked in the past might not work in the future.

I want to be honest about the limits of that framing, though. “Stay curious and keep adapting” is easy advice for me to give. I’m in my thirties, I work at the frontier of this stuff, and my entire career has been built around the assumption that the tools and the landscape will keep changing. That’s a very different position than someone who’s 50, spent twenty years building expertise in a domain that’s about to get compressed, has a mortgage and kids in college, and is now being told to “upskill.” Yang is right that the social contract of “study hard, get a degree, get a stable career” is under real pressure, and I don’t think “just adapt” is a sufficient answer for everyone. The question of what we actually do for the people who can’t easily pivot is a real one, and I don’t have a clean answer for it. Yang’s answer is UBI, which is at least a concrete proposal, even if the way he’s selling it feels more like a campaign pitch than a policy discussion.

Maybe I’m coming across as too emotional too. I’ve read a lot of these doomsday scenario-type pieces and they always feel like they’re trying to manipulate me rather than inform me. I don’t doubt that things are changing rapidly, maybe faster than ever, and I think that I’m lucky to be in a frontier industry where this idea of adapting and changing and modifying my workflow is endemic. Frankly, the one true thing about software engineering has always been that it evolves and it rewards those who are intellectually open-minded and good at upskilling.

---