Summarizer

I was at a podiatrist yesterday who explained that what he's trying to do is to "train" an LLM agent on the articles and research papers he's published to create a chatbot that can provide answers to the most common questions more quickly than his reception team can. He's also using it to speed up writing his reports to send to patients. Longer term, he was also quite optimistic on its ability to cut out roles like radiologists, instead having a software program interpret the images and write a report to send to a consultant. Since the consultant already checks the report against any images, the AI being more sensitive to potential issues is a positive thing: giving him the power to discard erroneous results rather than potentially miss something more malign.

I had some .csproj files that only worked with msbuild/vsbuild that I wanted to make compatible with dotnet. Copilot does a pretty good job of updating these and identifying the ones more likely to break (say web projects compared to plain dlls). It isn't a simple fire and forget, but it did make it possible without me needing to do as much research into what was changing. Is that a net benefit? Without AI, if I really wanted to do that conversion, I would have had to become much more familiar with the inner workings of csproj files. That is a benefit I've lost, but it would've also taken longer to do so, so much time I might not have decided to do the conversion. My job doesn't really have a need for someone that deeply specialized in csproj, and it isn't a particular interest of mine, so letting AI handle it while being able to answer a few questions to sate my curiosity seemed a great compromise. A second example, it works great as a better option to a rubber duck. I noticed some messy programming where, basically, OOP had been abandoned in favor of one massive class doing far too much work. I needed to break it down, and talking with AI about it helped come up with some design patterns that worked well. AI wasn't good enough to do the refactoring in one go, but it helped talk through the pros and cons of a few design pattern and was able to create test examples so I could get a feel for what it would look like when done. Also, when I finished, I had AI review it and it caught a few typos that weren't compile errors before I even got to the point of testing it. None of these were things AI could do on their own, and definitely aren't areas I would have just blindly trusted some vibe coded output, but overall it was productivity increase well worth the $20 or so cost. (Now, one may argue that is the subsidized cost, and the unsubsidized cost would not have been worthwhile. To that, I can only say I'm not versed enough on the costs to be sure, but the argument does seem like a possibility.)

I work in insurance - regulated, human capital heavy, etc. Three examples for you: - our policy agent extracts all coverage limits and policy details into a data ontology. This saves 10-20 mins per policy. It is more accurate and consistent than our humans - our email drafting agent will pull all relevant context on an account whenever an email comes in. It will draft a reply or an email to someone else based on context and workflow. Over half of our emails are now sent without meaningfully modifying the draft, up from 20% two months ago. Hundreds of hours saved per week, now spent on more valuable work for clients. - our certificates agent will note when a certificate of insurance is requested over email and automatically handle the necessary checks and follow up options or resolution. Will likely save us around $500k this year. We also now increasingly share prototypes as a way to discuss ideas. Because the cost to vibe code something illustrative is very low, an it’s often much higher fidelity to have the conversation with something visual than a written document

What an uncharitable and nasty comment for something they clearly addressed in theirs: > It is more accurate and consistent than our humans. So, errors can clearly happen, but they happen less often than they used to. > It will draft a reply or an email "draft" clearly implies a human will will double-check.

> "draft" clearly implies a human will will double-check. The wording does imply this, but since the whole point was to free the human from reading all the details and relevant context about the case, how would this double-checking actually happen in reality?

> the whole point was to free the human from reading all the details and relevant context about the case That's your assumption. My read of that comment is that it's much easier to verify and approve (or modify) the message than it is to write it from scratch. The second sentence does confirm a person then modifies it in half the cases, so there is some manual work remaining. It doesn't need to be all or nothing.

Here's some anecdata from the B2B SaaS company I work at - Product team is generating some code with LLMs but everything has to go through human review and developers are expected to "know" what they committed - so it hasn't been a major time saver but we can spin up quicker and explore more edge cases before getting into the real work - Marketing team is using LLMs to generate initial outlines and drafts - but even low stakes/quick turn around content (like LinkedIn posts and paid ads) still need to be reviewed for accuracy, brand voice, etc. Projects get started quicker but still go through various human review before customers/the public sees it - Similarly the Sales team can generate outreach messaging slightly faster but they still have to review for accuracy, targeting, personalization, etc. Meeting/call summaries are pretty much 'magic' and accurate-enough when you need to analyze any transcripts. You can still fall back on the actual recording for clarification. - We're able to spin up demos much faster with 'synthetic' content/sites/visuals that are good-enough for a sales call but would never hold up in production --- All that being said - the value seems to be speeding up discovery of actual work, but someone still needs to actually do the work. We have customers, we built a brand, we're subject to SLAs and other regulatory frameworks so we can't just let some automated workflow do whatever it wants without a ton of guardrails. We're seeing similar feedback from our customers in regard to the LLM features (RAG) that we've added to the product if that helps.

This makes a lot of sense and is consistent with the lens that LLMs are essentially better autocomplete

This to me seems like saying you can learn nothing from a book unless you yourself have written it. You can read the code the LLM writes the same as you can read the code your colleagues write. Moreover you have to pretty explicitly tell it what to write for it to be very useful. You're still designing what it's doing you just don't have to write every line.

It was not a well thought out piece and it is discounting the agentic progress that has happened. >The industry had reason to be optimistic that 2025 would prove pivotal. In previous years, AI agents like Claude Code and OpenAI’s Codex had become impressively adept at tackling multi-step computer programming problems. It is easy to forget that Claude Code CAME OUT in 2025. The models and agents released in 2025 really DID prove how powerful and capable they are. The predictions were not really wrong. I AM using code agents in a literal fire and forget way. Claude Code is a hugely capable agentic interface for sovling almost any kind of problem or project you want to solve for personal use. I literally use it as the UX for many problems. It is essentially a software that can modify itself on the fly. Most people haven't really grasped the dramatic paradigm shift this creates. I haven't come up with a great analogy for it yet, but the term that I think best captures how it feels to work with claude code as a primary interface is "intelligence engine". I'll use an example, I've created several systems harnessed around Claude Code, but the latest one I built is for stock porfolio management (This was primarily because it is a fun problem space and something I know a bit about). Essentially you just used Claude Code to build tools for itself in a domain. Let me show how this played out in this example. Claude and I brainstorma general flow for the process and roles. Then figure out what data each role would need, research what providers have the data at a reasonable price. I purchase the API keys and claude wires up tools (in this case python scripts and documentation for the agents for about 140 api endpoints), then builds the agents and also creates an initial vesrion of the "skill" that will invoke the process that looks something like this: Macro Economist/Strategist -> Fact Checker -> Securities Sourcers -> Analysts (like 4 kinds) -> Fact Checker/Consolidator -> Portfolio Manager Obviously it isn't 100% great on the first pass and I have to lean on expertise I have in building LLM applications, but now I have a Claude Code instance that can orchestrate this whole research process and also handle ad-hoc changes on the fly. Now I have evolved this system through about 5 significant iterations, but I can do it "in the app". If I don't like how part of it is working, I just have the main agent rewire stuff on the fly. This is a completely new way of working on problems.

I think it depends on what "join" means. I see no reason why it has to be "replace a human". People used to have secretaries back in the day, we don't anymore, we all do our own thing, but in a way, LLMs are our secretaries of sorts now. Or our personal executive assitants, even if you're not an executive. I don't know what else LLMs need to do? get on the payroll? People are using them heavily. You can't even google things easily without triggering an LLM response. I think the current millenial and older generation is too used to the pre-LLM way of things, so the resistance will be there for a long time to come. but kids doing homeworks with LLMs will rely on them heavily once they're in the work force. I don't know how people are not as fascinated and excited about this. I keep watching older scifi content, and LLMs are now doing for us what "futuristic computer persona" did in older scifi. Easy example: You no longer need copywriters because of LLMs. You had spell/grammar checkers before, but they didn't "understand" context and recommend different phrasing, and check for things like continuity and rambling on.

> You no longer need copywriters because of LLMs You absolutely do still need copyeditors for anything you actually care about.

"People using AI" had a meaningful change when they "joined the workforce" in 2025. We may not have gotten fully-autonomous employees, but human employees using AI are doing way more than they could before, both in depth and scale. Claude Code is basically a full-time "employee" on my (profitable) open source projects, but it's still a tool I use to do all the work. Claude Code is basically a full-time "employee" at my job, but it's still a tool I use to do all the work. My workload has shifted to high-level design decisions instead of writing the code, which is kind of exactly what would have happened if AI "joined the workforce" and I had a bunch of new hires under me. I do recognize this article is largely targeted at non-dev workforces though, where it _largely_ holds up but most of my friends outside of the tech world have either gotten new jobs thanks to increased capability through AI or have severely integrated AI into whatever workflows they're doing at work (again, as a tool) and are excelling compared to employees who don't utilize AI.

Agents as staff replacements that can tackle tasks you would normally assign to a human employee didn't happen in 2025. Agents as LLMs calling tools in a loop to perform tasks that can be handled by typing commands into a computer absolutely did. Claude Code turns out to be misnamed: it's useful for way more than just writing code, once you figure out how to give it access to tools for other purposes. I think the browser agents (like the horribly named "ChatGPT Agent" - way to burn a key namespace on a tech demo!) have acted as a distraction from this. Clicking links is still pretty hard. Running Bash commands on the other hand is practically a solved problem.

But it isn’t joining the workforce. Your perspective is that it could, but the point that it hasn’t is the one that’s salient. Codex might be able to do a substantial portion of what a freelancer can do, but even you fell short of saying it can replace the freelancer. As long as every ai agent needs its hand held the effect on the labor force is an increase in costs and an increase in outputs where quality doesn’t matter. It’s not a reduction of labor forces

I use an agent in all my day to day coding now. It's a lot small tasks to speed me up, but it's definitely in use.

I've seen organizations where 300 of 500 people could effectively be replaced by AI, just by having some of the the remaining 200 orchestrate and manage automation workflows that are trivially within the capabilities of current frontier models. There's a whole lot of bullshit jobs and work that will get increasingly and opaquely automated by AI. You won't see jobs go away unless or until organizations deliberately set out to reduce staff. People will use AI throughout the course of their days to get a couple of "hours" of tasks done in a few minutes, here and there, throughout the week. I've already seen reports and projects and writing that clearly comes from AI in my own workplace. Right now, very few people know how to recognize and assess the difference between human and AI output, and even fewer how to calibrate work assignments. Spreadsheet AIs are fantastic, reports and charting have just hit their stride, and a whole lot of people are going to appear to be very productive without putting a whole lot of effort into it. And then one day, when sufficiently knowledgable and aware people make it into management, all sorts of jobs are going to go quietly away, until everything is automated, because it doesn't make sense to pay a human 6 figures what an AI can do for 3 figures in a year. I'd love to see every manager in the world start charting the Pareto curves for their workplaces, in alongside actual hours worked per employee - work output is going to be very wonky, and the lazy, clever, and ambitious people are all going to be using AI very heavily. Similar to this guy: https://news.ycombinator.com/item?id=11850241 https://www.reddit.com/r/BestofRedditorUpdates/comments/tm8m... Part of the problem is that people don't know how to measure work effectively to begin with, let alone in the context of AI chatbots that can effectively do better work than anyone a significant portion of the adult population of the planet. The teams that fully embrace it, use the tools openly and transparently, and are able to effectively contrast good and poor use of the tools, will take off.

It seems like we are using AI to automate the unimportant parts of jobs that we shouldn’t have been doing anyway. Things like endless status reports or emails. But from what I’ve seen it just makes that work output even less meaningful—who wants to read AI generated 10 pages that could have been two bullet points? And it doesn’t actually improve productivity because that was never the bottleneck of those jobs anyway. If anything, having some easy rote work is a nice way to break up the pace.

In December of 2025, I took five tickets I was assigned in Jira and threw them at codex, which just did them, and with the help of MCPs, codex was able to read the ticket, generate some code, test the code, update gitlab, create a merge request on Gitlab, and update the Jira with the MR. CodeRabbit then reviewed the MR before a human had to look at it. It didn't happen in 2025, but I see it happening for 2026.

There’s often the question of communication overhead between people; Claude would remove that.

No that’s not true at all. Humans can deal with ambiguity and operate independently. Claude can’t do that. You’re trading one “problem” for an entirely different one in this hypothetical.

If you’re already communicating with Claude, it’s not additional overhead.

I get the point you are making, but the hypothetical question from your manager doesn't make sense to me. It's obviously true that any of your particular coworker wouldn't be useful to you relative to an AI agent, since their goal is to perform their own obligations to the rest of the company, whereas the singular goal of the AI tool is to help the user. Until these AI tools can completely replace a developer on its own, the decision to continue employing human developers or paying for AI tools will not be mutually exclusive.

"I have to either get rid of one of your coworkers or your laptop, which is it?"

You would probably have the same answer if your boss said, I have to get rid of one of your co-workers or your use of editing tools - ie all editors. You either get rid of your co-worker or go back to using punch cards. You would probably get rid of your co-worker and keep Vim/Emacs/VsCode/Zed/JetBrains or whatever editor you use. All your example tells us is that AI tools are valuable tools.