AI Augmentation Isn’t Universal Replacement
Fortune recently interviewed Ryan Breslow, CEO of Bolt, where he "dropped shock," when he said he let go of his entire HR department (and replaced it with a small people ops team). Switching up the company for lean and mean, he also aggressively cut Bolt's management layer in an effort to pull the company back into its startup grit. That, by itself, didn’t raise my eyebrow. Startups do this. The vibe right now is, companies are using AI to flatten their org charts. Companies overbuild. Leadership teams calcify. Bureaucracy expands faster than revenue, and eventually someone decides to restore operational discipline. But, what got my attention was the elimination of the human resources department for a people ops team now tasked with handling-all set up.
Whenever I hear a company describe governance infrastructure as expendable overhead, my compliance spidey sense starts to tingle. Who owns employee disputes? Who manages misconduct? Who protects the organization from employment liability? Who handles investigations (ethically, particularly in lean organizations)? Who carries legal and operational accountability when the inevitable human issue arises? HR doesn't exist because companies enjoy administrative complexity. It exists because people (even if there are less of them) create risk.
Now, in fairness, smaller companies can distribute those responsibilities differently. A strong chief operations officer can absorb some of that operational burden. A general counsel can manage employment risk. Outside counsel can provide coverage where internal capacity doesn’t make economic sense. I couldn't tell when I checked their page on LinkedIn, but I hope they have a capable COO, GC or outside counsel holding down the HR fort. I'm also not making the argument that every company needs a bloated HR department. But the Bolt/Breslow post made me pause because I think other leadership teams have been adopting tech CEO narratives and there needs to be caution considered in what other leadership teams are learning from this.
Same consideration: a Fortune piece covering comments from Microsoft AI CEO Mustafa Suleyman suggesting we may be roughly 18 months away from AI automating ("all" was the tone) white-collar work, with law graduates and MBAs explicitly mentioned in the article. I want to be clear: I am not in the camp that believes AI changes nothing. That would be unserious. AI is already reshaping white-collar work. It's already compressing workflows and reducing the amount of human labor required in certain functions.
However, I increasingly think the public conversation around "white-collar disruption" is murky.
“White collar” has become an intellectually lazy category in AI discourse. A software engineer/coder isn't a compliance officer. A junior associate at a law firm isn't a physician. A finance analyst isn't a CFO. A contract reviewer isn’t a regulatory decision-maker. Yet we continue discussing “white-collar work” as though knowledge labor is a homogeneous block that can be uniformly disrupted. We gotta reel in the rhetoric.
The technology sector is seeing real and measurable efficiency gains from AI augmentation. Faster coding. Documentation acceleration. Testing support. Debugging assistance. Research compression. Okay, I'll give it that. But what I increasingly hear is a dangerous extrapolation: AI is producing meaningful labor efficiencies in software-heavy environments, therefore similar levels of disruption are imminent across all professional work?
Ah, that assumption deserves scrutiny.
Disruption is here; we can see it. However, that disruption isn't universal.
AI augmentation isn't the same thing as AI replacement, and I think too many leaders are collapsing those distinctions into the same conversation. Replacement means the role disappears. Augmentation means the amount of human labor required to perform the work decreases. That difference isn't semantic.
If a software team once required ten engineers to support a workflow and now requires 2-3 because AI materially accelerates code generation, debugging, documentation, and workflow execution, that is labor compression. In some technology contexts, yes, that may become role elimination. But it doesn't follow that the same equation applies cleanly elsewhere.
In finance, AI may materially reduce manual analytical preparation while preserving human accountability for interpretation and decision-making. In law, AI can absolutely compress document review, contract summarization, drafting assistance, and legal research preparation. But legal judgment, liability, negotiation strategy, and client accountability don't disappear because the first draft came from Claude or Perplexity for that matter. In compliance, automation can reduce friction, but it can't eliminate accountability. In regulated environments, accountability often becomes more—not less—important as automation scales.
The consideration I'm starting to consider more in AI's impact on white-collar work is "automation rate" in given functions and across industries (more on that in my next post). That is the framework I think leadership teams should be using. I want to see publicly traded company leadership share more about this on earnings calls.
Automation rate, as I think of it, is the percentage reduction in human labor AI augmentation/automation, e.g. how many roles are being truncated and how many are being eliminated.
This consideration forces a much more honest analysis because I'm of the belief that automation rates aren't evenly distributed. Software engineering may have a high automation rate because much of the work happens inside structured digital systems with repeatable logic. Legal work may have a moderate automation rate because preparation can be compressed while accountability remains human. Healthcare may have extraordinary assistive AI potential while still requiring persistent human oversight because trust, diagnosis risk, liability, and regulation are inseparable from execution. Corporate strategy may see significant research acceleration (I haven't seen this consideration, only "get rid of the consultants, they cost too much") but limited autonomous decision transfer because ambiguity and judgment aren't the same thing as structured repetition.
Same broad category. Entirely different operational realities.
One of the assumptions I hear most often (again, from tech CEOs) is that human oversight is merely temporary friction, that as AI improves, humans step back, and machine autonomy expands until human review becomes unnecessary. That may happen in some contexts. But in many industries, I think that assumption fundamentally devalues humans in a way that we haven't yet begun to see how bad that bet can be.
If AI produces flawed hiring logic, unsafe recommendations, faulty financial analysis, defective legal outputs, or discriminatory operational decisions, who owns that? Not philosophically. Legally. Operationally. Reputationally. Human-in-the-loop isn't a technical workaround. In some jobs, it's a requirement.
Boards don’t outsource fiduciary accountability to models. Courts don’t litigate against algorithms in the abstract. Regulators will never accept, “the AI made the decision.” That will not fly. And beyond regulation, there is a legitimacy problem.
People accept machine assistance far more easily than machine authority. A customer may accept AI support. A patient may accept AI-assisted diagnostics. A company may embrace AI-generated workflow acceleration. That doesn't mean people will broadly accept AI-exclusive decision authority and we're already seeing people push back on AI altogether.
Leadership teams need to understand that distinction because this is where I think many organizations are making missteps and getting a little ahead of themselves/their strategies/their true long term objectives, suffering from short-termism. They're watching tech firms cut headcount. They're watching startups flatten teams. They're watching AI productivity gains. And they're thinking because everyone has access to the same tools, they can achieve the same profit margin increases. However, here's where I agree with Torsten Slok’s sentiment, "while profit margins in Big Tech increased by more than 20% in the fourth quarter of 2025, the broader Bloomberg 500 Index has seen almost no change," - see Fortune article below.
Local efficiencies aren't universal.
I do think there will be fewer roles in categories. I do think entry-level professional pathways will continue to narrow. I do think some forms of analytical and administrative labor will continue to require materially fewer humans than they historically did. Law graduates will feel this. MBA pipelines will feel this. Knowledge workers broadly will feel this. But the notion that software-sector labor compression is the blueprint for all professional work is where the analysis gets bloated.
Knowledge work isn't homogeneous. Automation (rates) aren't universally transferable. And AI augmentation I don't believe will be an equal, universal replacement.
At the end of the day, we have to see how much augmentation/automation scales across both jobs and industries. Then, we have to put a universal quantifiable metric on augmentation/automation (like when we decided for software and social media: ARR, daily users, etc.). The right question is where automation actually scales and where human judgment remains structurally unavoidable.
A startup restructuring its operating model is one thing. A regulated enterprise deciding governance infrastructure is suddenly optional because AI improves efficiency is something else entirely. I think in the AI jobs conversation, we're experiencing a little "category confusion" at the moment.
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