Software engineering is falling to this trend too (somewhat)

The solution is to stop merely thinking of yourself as a software engineer and move up to the level of “manager of agents”.. but actually, managers deal with human stuff and this is fascinatingly mechanical - in fact even the unpredictability of these new tools is quite predictable. And so, a more useful framing is “software development process engineer”.

You can look at all the literature on building factories and production lines for ideas on what you’ll be doing.

You shouldn’t ever just have your agent write the software then review and ship it. You are missing massive opportunities to take yourself out of more loops over time. What self-reflection are you and the model doing to catch opportunities to improve? What is your method for codifying your acceptance criteria, so your agents can do the work to higher quality over time without you in the loop to get it there? What’s your process for continuous improvement? How do your models know what work other team members’ models are doing simultaneously so there’s less stepping on toes? Can THAT be automated so you don’t need to sit in Slack and trade “human-verbal locks” on areas of the architecture?

There’s immense room for creativity in the role of a software development process engineer.

I would have been interested in the experience and thoughts of someone whose opinions I respected, both as a social thing and to learn something.

In other words, some types of questions are aimed at 1) building a social connection with the person you’re asking and 2) because you want to know what they, specifically, think about their topic.

AI can’t really replace either of these. AIs might function as a weak social replacement for some people, but you aren’t really going to advance in your personal or professional life by making friends with Claude.

A good example of the second one are AskMeAnything type forum posts: I don’t care what some generic celebrity/famous figure thinks about something, I care specifically about what George Clooney thinks about it. The AI will always be guessing, building a model on what George has said in the past, but it will never actually say what he thinks right now.

For a more serious and contemporary example: there are dozens of videos on YouTube right now, interviews with various experts and pundits on the situation in Iran. Many of them have hundreds of thousands of views. But why would someone watch this instead of just asking ChatGPT what’s going on in Iran? Because we want to know what this particular person thinks.

The way AI replaces work is in that there is an enormous ROI to work with fewer (and smarter) people. Those social interactions are a big part of work, but they are only very rarely "the work", and they cost time. In the cases that they are required; they seem to cluster and the ROI of fewer social synchronization problems increases even more.

But that might all be wrong. I'm not confident enough to say where we'll land. I also see its possible demand will go up faster because of/and enabled by the increase in supply, and the social aspect is "the real work" to be done.

If block succeeds, we'll see more layoffs of that kind, probably even more extreme ones. You are not top senior level employee? Out. You don't single handedly cause 30% of the AI spend on your 15 person team? Out.

People say how in five years there won't be seniors because one stopped junior hiring... in five years the seniors won't be needed either. Already today, we have single person billion dollar exits, high schoolers making millions from food apps. This is thanks to LLMs.

The technology is there to replace most of the white collar work, it's just not applied enough yet. The economic system needs to adapt to not having labor being such a big redistributor.

The chain of operation never ends either. Every AI system needs someone to run it. Whatever runs it needs to be built and maintained. Follow that chain as far as you like — human agency doesn't disappear, it scales up. The universe is not running out of things that need doing.

"AI will take our jobs" is not a civilisational concern. It's a failure to imagine what civilisation could actually be.

We need judgement when we can't verify/prove that the answer is correct so we need a human we can trust. For example in author's example the pandas snippet is verifiably correct and I don't really care about judgement in that case. When there is a verification/test that gives a clear pass/fail to AI, the AI can just keep throwing stuff at the wall until it's green and it's good enough for a lot of use cases.

I suspect that will change as trust in automated systems increases. (For example the author seems to consider AI a source of "correctness", which implies this trust is already surprisingly high.)

Or why he couldn't have asked a human about the NaN thing.

I know those are arbitrary examples but.. the behavior doesn't really seem to depend on the category? It might have to do more with how urgently the knowledge is needed?

It’s not about where we are today folks (the intercept of the line). It’s about the rate of progress (the slope of the line).

For example UI design can be replaced by AI. Unless UI or UX design people were bringing something like _taste_ instead of simply mechanically operating figma - they are not keeping their jobs.

I genuinely don't need to learn SQL ever in my life. I just don't need it for dashboards or analytics use. A person whose main job was to translate requirements to SQL into a dashboard and nothing else would not keep their job anymore. The person to whom they were providing the analysis to could just perform the analysis themselves using AI.

I do think that most jobs would change dramatically but for sure some of them would be eliminated completely.

OP clearly does not have a whilte collar job.

There are cases and cases of IT folks being replaced by AI because companies think that AI is better than humans on everything.

The unemployment rate during the peak of the Great Depression was 25%, not 100%.

By providing productivity tools you do effectively replace jobs because there's only so much of a good or service a person will want to consume.

For example, just because a game dev studios can make 10x more games with AI, this doesn't mean the industry will make 10x more money unless demand for video games increases. Instead what is likely to happen as the cost of making games reduces is that the price of games for consumers will drop too as competition increases, which will turn hurt game dev profits, so game dev studios will likely have to be 10x smaller in the future – even if there's still technically people working in the industry.

However when the work of agricultural workers became increasingly automated there were lots of other industries people could work instead, at the time that was factory work, and although the details will be different, I'm sure to some extent this will happen with white collar work too. But the question I'd ask today is what is that alternative source work, and is it as good as white collar work?

Our economy went from, farming -> factory work -> office work. I strongly suspect the next step will be more people working in manual labour jobs and working in servant type roles. It's hard to see where else the demand will come from.

We are only one major incident away from this trend reversing. Now that we have AI, regulation is less burdensome. More testing requirements, more certification requirements, more security requirements, more accessibility requirements.

Everyone keeps their jobs; the bar goes up. Whenever an industry gets better tools, we raise standards instead of making more cheap junk. We make $25K cars instead of $5K cars at 1960s engineering standards.

Main Points, in Order of Importance

1. Most White Collar Work Is Relationship-Based, Not Transactional The central claim. A dominant share of workplace "questions" aren't requests for correct answers -- they are social, trust-based exchanges where the relationship and the advisor's judgment are the actual product.

2. Two Kinds of Question-Answering That Keep Getting Conflated The foundational distinction. Transactional questions have a correct answer and an imminent need. Relationship-based questions use the question as a pretext for social exchange, shared perspective, and felt understanding. AI handles the first well; it cannot substitute for the second.

3. AI Cannot Replace Trust and the Weight We Give to Respected Opinions Even a correct AI answer carries less weight than advice from someone whose judgment you trust. This isn't irrational -- it reflects that the value in consulting, advising, and managing is partly in the relationship itself, not just the information delivered.

4. Strategy Consulting as the Illustrative Case A concrete test domain. Buyers of consulting aren't purchasing correct answers; they want advice from trusted people, catharsis in being heard, and help clarifying their own thinking. None of that is substitutable by an AI regardless of output quality.

5. Human Factors Intensify in Procedural Organizations An underappreciated corollary. In government and military contexts, lacking market feedback mechanisms, human trust and social organization become even more load-bearing, not less.

Opinion

It's a short, clear piece with a genuinely useful distinction at its center -- but it doesn't fully earn its conclusion.

The two-question-types framework is clean and rings true experientially. Most people have felt the difference between wanting an answer and wanting a conversation, and the observation that these get conflated in AI replacement debates is fair and underappreciated.

Where it falls short is in the leap from "relationship-based questions exist" to "therefore white collar work won't be replaced." The argument proves that AI can't fully substitute for trusted human relationships -- it doesn't prove that organizations will continue to pay for those relationships at current rates, or that AI won't restructure which human interactions are deemed worth paying for.

A client might still want a trusted advisor but find that one advisor supported by AI can now serve ten clients instead of three.

There's also an implicit assumption that the relationship-based component is dominant in most white collar work. That may be true in strategy consulting, but it's a significant empirical claim that the piece asserts rather than argues across the broader category of white collar work.