The coming knowledge-work supply-chain crisis

TFA is right to point out the bottleneck problem for reviewing content - there’s a couple things that compound to make this worse than it should be -

The first is that the LLM outputs are not consistently good or bad - the LLM can put out 9 good MRs before the 10th one has some critical bug or architecture mistake. This means you need to be hypervigilant of everything the LLM produces, and you need to review everything with the kind of care with which you review intern contributions.

The second is that the LLMs don’t learn once they’re done training, which means I could spend the rest of my life tutoring Claude and it’ll still make the exact same mistakes, which means I’ll never get a return for that time and hypervigilance like I would with an actual junior engineer.

That problem leads to the final problem, which is that you need a senior engineer to vet the LLM’s code, but you don’t get to be a senior engineer without being the kind of junior engineer that the LLMs are replacing - there’s no way up that ladder except to climb it yourself.

All of this may change in the next few years or the next iteration, but the systems as they are today are a tantalizing glimpse at an interesting future, not the actual present you can build on.

The intro sentence to this is quite funny.

> Remember the first time an autocomplete suggestion nailed exactly what you meant to type?

I actually don't, because so far this only happened with trivial phrases or text I had already typed in the past. I do remember however dozens of times where autocorrect wrongly "corrected" the last word I typed, changing an easy to spot typo into a much more subtle semantic error.

Validating the outputs of a stochastic parrot sounds like a very alienating job.

A few articles like this have hit the front page, and something about them feels really superficial to me, and I'm trying to put my finger on why. Perhaps it's just that it's so myopically focused on day 2 and not on day n. They extrapolate from ways AI can replace humans right now, but lack any calculus which might integrate second or third order effects that such economic changes will incur, and so give the illusion that next year will be business as usual but with AI doing X and humans doing Y.

> What I see happening is us not being prepared for how AI transforms the nature of knowledge work and us having a very painful and slow transition into this new era.

I would've liked for the author to be a bit specific here. What exactly could this "very painful and slow transition" look like? Any commenters have any idea? I'm genuinely curious.

> This pile of tasks is how I understand what Vaughn Tan refers to as Meaningmaking: the uniquely human ability to make subjective decisions about the relative value of things.

Why is that a "uniquely human ability"? Machine learning systems are good at scoring things against some criterion. That's mostly how they work.

And once the Orient and Decide part is augmented, then we'll be limited by social networks (IRL ones). Every solo founder/small biz will have to compete more and more for marketing eyeballs, and the ones who have access to bigger engines (companies), they'll get the juice they need, and we come back to humans being the bottlenecks again.

That is, until we mutually decide on removing our agency from the loop entirely . And then what?

The method of producing the work can be more important (and easier to review) than the work output itself. Like at the simplest level of a global search-replace of a function name that alters 5000 lines. At a complex level, you can trust a team of humans to do something without micro-managing every aspect of their work. My hope is the current crises of reviewing too much AI-generated output will subside into the way you can trust the team because the LLM has reached a high level of “judgement” and competence. But we’re definitely not there yet.

And contrary to the article, idea-generation with LLM support can be fun! They must have tested full replacement or something.

This section heading from the post captures the key insight, is more focused, and is less hyperbolic:

> Redesigning for Decision Velocity

> Remember the first time an autocomplete suggestion nailed exactly what you meant to type?

No.

> Multiply that by a thousand and aim it at every task you once called “work.”

If you mean "menial labor" then sure. The "work" I do is not at all aided by LLMs.

> but our decision-making tools and rituals remain stuck in the past.

That's because LLMs haven't eliminated or even significantly reduced risk. In fact they've created an entirely new category of risk in "hallucinations."

> we need to rethink the entire production-to-judgment pipeline.

Attempting to do this without accounting for risk or how capital is allocated into processes will lead you into folly.

> We must reimagine knowledge work as a high-velocity decision-making operation rather than a creative production process.

Then you will invent nothing new or novel and will be relegated to scraping by on the overpriced annotated databases of your direct competitors. The walled garden just raised the stakes. I can't believe people see a future in it.

This is the same problem as outsourcing to third party programmers in another country, but worse.

The article rightly points out that people don't enjoy just being reviewers: we like to take an active role in playing, learning, and creating. They point out the need to find a solution to this, but then never follow up on that idea.

This is perhaps the most fundamental problem. In the past, tools took care of the laborious and tedious work so we could focus on creativity. Now we are letting AI do the creative work and asking humans to become managers and code reviewers. Maybe that's great for some people, but it's not what most problem solvers want to be doing. The same people who know how to judge such things are the same people who have years of experience doing this things. Without that experience you can't have good judgement.

Let the AI make it faster and easier for me to create; don't make it replace what I do best and leave me as a manager and code reviewer.

The parallels with grocery checkouts are worth considering. Humans are great at recognizing things, handling unexpected situations, and being friendly and personable. People working checkouts are experts at these things.

Now replace that with self serve checkouts. Random customers are forced to do this all themselves. They are not experts at this. The checkouts are less efficient because they have to accommodate these non-experts. People have to pack their own bags. And they do all of this while punching buttons on a soulless machine instead of getting some social interaction in.

But worse off is the employee who manages these checkouts. Now instead of being social, they are security guards and tech support. They are constantly having to shoot the computer issues and teach disinterested and frustrated beginners how to do something that should be so simple. The employee spends most of their time as a manager and watchdog, looking at a screen that shows the status of all the checkouts, looking for issues, like a prison security guard. This work is inactive and unengaging, requiring constant attention - something humans aren't good at. When little they do interact with others, it is in situations where that are upset.

We didn't automate anything here, we just changed who does what. We made customers into the people doing checkouts and we made more level staff into managers of them, plus being tech support.

This is what companies are trying to do with AI. They want to have fewer employees whose job it is to manage the AIs, directing them to produce. The human is left assigning tasks and checking the results - managers of thankless and soulless machines. The credit for the creation goes to the machines while the employees are seen as low skilled and replaceable.

And we end up back at the start: trying to find high skilled people to perform low skilled work based on experience that they only would have had if they had being doing high skilled work to begin with. When everyone is just managing an AI, no one will know what it is supposed to do.