Who's "we"? I am not in that group...
224 karma · joined January 19, 2012
But it’s more than that. I don’t have any conscious notions of thought at all. It’s much more like these guys in the tv show Severance who stare at numbers until “it feels right”.
The end result just pops into my head, often just as a feeling, not even as anything graspable, and only takes semantic shape when I say it out loud or write it. And even when I write it, it continues — it’s like a constant irking feeling of calibration that never feels quite right until it does.
There is one exception: demi-sleep is the only state where I can ruminate (e.g. stress-related), but then it's also non-verbal -- more like a combination of images and sensations. It's hard for me to put into words (no pun intended)...
I couldn’t do that. I have no insight into my thought processes whatsoever. There are no semantic (or any other) processes to witness. It’s complete crickets at all times…
My wife is the opposite - constant chatter in her head. And she can trace every thought exactly. (No idea how people like her can focus or even function!)
While it is continuously updated, it lacks many modern white-collar occupations, such as occupations in finance, tech, or consulting. And when it does carry an occupation, it often misses the mark on the relevant task set.
Every immigration attorney who had to build labor certs around O-Net can confirm how frustrating and backwards the database is. Just imagine trying to justify an H1-B for, say, a sales & trading associate at an investment bank when the only available options are bank teller, financial analyst, risk examiner, and 2-3 other ill-fitting categories.
That being said, occupational classification is an impossible task for a variety of reasons..
The average person does not have the time to occupy themselves with a new thing to see what it can maybe do for them if they combine it with hundreds of others. Until non-chat-bot AI comes in discrete usable packages, mass adoption will be limited, because it's all just a big bunch of lego pieces whose utility is hard to grasp, because it isn't self-evident.
This isn't new, though. It's how "technology" usually works. For example, human(oid)s have been able to stick sticks into things for ~500k years, but the fork wasn't "invented" until ~2-4k years ago.
AI is a background technology, a building block. Something that is best appreciated when you don't know it's there -- when it just does a job (well). The end-in-itself POV of the tech industry is self-indulgent and irrelevant for the average person.
There is zero value in showing them. All they do is demonstrate a dumb pricing game that could easily be ended if the GOP didn’t conflate “single-payer” with “single-provider” and scream socialism.
Moreover 80%+ of hospitals no longer charge on a fee-for-service basis but through DRG (diagnosis related groups) as part of a value based care model. So these itemized list prices are doubly moot.
This is a common problem with all dating apps. Specifically, interests, hobbies, daily routines, objectives, etc. have nothing to do with compatibility.
“We like the same things!” is the compatibility signal of a naive 22-yr old…
Neither the foreign-born population nor the proportion of foreign-born individuals say anything about the “current immigration”.
But I guess it works, because nowhere is “immigration” defined either…
Babbling toddlers can do logic. I sometimes can also do logic, even though I don’t have an inner voice and don’t formulate thoughts in words of any kind. As I’m relatively sure I’m not an ET alien, there are probably plenty of people like me.
The current output style does work, but it’s a Sisyphean task to tweak it constantly only to find out that CC adhere’s to only 75% of it, no matter what…
The number of politicians that conflate healthcare and health insurance (notwithstanding the actual convergence of the two through payers’ purchasing provider entities) is mind boggling.
Striking the word “healthcare” from the conversation would make this much more digestible for folks afraid of the “socialism boogeyman”…
For that reason I exit session quickly when I can. It used to be that the context of a session is very valuable, because it was so hard to get CC there, but now, this isn't the case anymore, so I only hold onto sessions when there is really hairy stuff that I know would be hard to replicate.
I think the whole notion of full automation (long-horizon, subagent swarms, single shot prompting) to have CC build you the whole thing is a pipe dream. CC cannot even write a single doc consistently well. It is excellent at implementing well scoped plans, though, and that's the way to go IMHO. You still gotto refactor the sh*t out of it afterwards but it works.
CC attempts to communicate in English the same way it does in code -- squeezing as much information into as few words as possible, and including justifications for everything, no matter how trivial. To do that, it coins terms and presupposes all of its context exists within the reader also.
So, the crux is: CC has no clue what is and isn't "necessary" for a human reader, and teaching it to understand that (if at all possible) is going to be very valuable...
I usually think of it in terms of having a "good" or "bad" session. In a bad session, there is a harmful bias that you can only get rid of through a new session. For example, if you exposed too much context about, say, a variable that features prominently in a doc. The entire session will be anchoring on the importance of that variable. Or if you introduced the notion of CC having to ask for permission for stuff you will have a hard time getting it to "think on its feet" or propose an effective solution (you have made CC so insecure that it now relies on you even for little things that wouldn't normally require your input). In some cases (let's say you have important context in that session) you can overcome this by upping the reasoning level or switching to Fable, but usually a new session is the way to go.
Because it's so easy to bias the session I wouldn't even want to use any of these tools that pretend to give Claude "a brain" or "remember" things. That was en vogue a year ago and helpful then, but now, it's plain harmful IMHO. The key is to have just enough context.
Subagents often have the reverse problem in that they tend to have too little context to make "judgment calls", which is why the tasks for them must be either deliberately basic or mechanical in nature, or their output should be audited by the main session agent.
As for "thinking" it's not clear that that's even a thing (https://arxiv.org/abs/2510.24941)...
CC’s communication violates almost every grammatical rule that’s tested on, say, the SAT. And yet I’m sure if you had Claude take the verbal section of the exam it would ace it.
Biggest issues: dense sentences, constant metaphors, abstractions, and seemingly no understanding of correct anaphora use. For example, “the x”, with x having not only no antecedent but also being a coined word or quasi-synonym for something that is already named in the code base. This gets compounded by its being unable to regress to a baseline (existing names in code) and instead anchoring on newer (vague or wrong) terms, for example, that crept in through a plan.
CC tells me this is because the speedy and precise fulfillment of a current task will trump every other tendency, so it adheres poorly to whatever “semantic baseline” the project represents.
Of course, it also has no concept of what context the user has and assumes that it must be the same it holds in its memory, which creates this “I didn’t know that you didn’t know” type of communication.
I have managed to wrangle some of these issues with a custom output style, but wish a pre-report hook were an option, as it could force CC to rewrite plan implementation take-aways…
Btw: Fable has the exact same issues, just somewhat less pronounced.
I have been working on a project with about a dozen generation tasks, each of which comes with a fixed token budget. The nature of this system requires that most tasks be completed by distinct model families.
As a result, I tested ~50 models across as many model families as I could gather, frontier and open weight, API (gateway and direct) and self-hosted. Evaluation was based on a set of cosine similarity validations that was repeated across ~50 different embedding models.
Interestingly, frontier models did worse on the tasks than open weight models. However, when it came to costs, the picture was reversed: frontier models were much, much more token-efficient. In fact, almost no open-weight model was able to meet the initial token budget, while almost all frontier models did. Moreover, open weight models struggled massively with reasoning, in terms of latency and token consumption.
I also found that the latest models did not perform better than older models. And any a priori benchmarking data was utterly useless.
So, I ended up using a set of open weight models without reasoning, as it turned out reasoning as well as frontier negatively correlated with the tasks. However, before I knew this, I had spent a lot of time running each available reasoning level for each model.
Lastly, as an aside, when it came to embedding models, size (dims as well as model size) did not correlate with quality, once a hurdle figure (~2k dims) was met. In fact, sweet spot was 3-5K, and for my (text-based) set of tasks, dense models tended to outperform MoE ones.
One thing to add: software maintenance costs. The build has never been the bottleneck.
The notion that most companies will suddenly institute developers to build all kinds of software inhouse and maintain it is silly. Most companies are not google et al, even in 2026.
The insurance industry built almost everything custom in the 70s and 80s, simply because that was the only option. The more software became commercially available elsewhere the more this effort was pruned back.
Another thing: knowing what to build — another big bottleneck. Most people cannot articulate what they want and even fewer can articulate it at a level that would enable them to build durable software, even with AI. Case in point: the majority of AI-built stuff you see are point solutions or small productivity items, etc. “Systems thinking”, as some people call it, is hard, even for most software engineers.
Yes, you can “rebuild” tools you’ve previously purchased as SaaS but at some point you gotto use your brain to come up with something new. Systems thinking on blank-page challenges is even harder…
It’s not like people feel the need to explain that they used a spellchecker or thesaurus or googled the correct use of an idiom, etc.
There seems to be a general need for some people to dunk on valuable AI use by refusing to acknowledge that there are a many ways to use a tool. (Similar story on using AI for coding.)
Echoing a comment from above, why would I care whether a sentence was formulated by AI or John or a ghostwriter or John who asked Jane for feedback before rewriting? I care about the content. If I don’t like the way it’s written or if I’m irked by how it is written (ooh, an emdash — how embarrassing!) then I don’t need to read it.
Personally, I’m much more annoyed when I click on an article that sounds interesting, and the author tries to show off their penmanship by starting with five pages of “a history of X” or a tangential but redundant personal story before getting to the point. IMHO most non-fiction writing on the web could be accomplished in bullets. But again, that’s just my very personal preference…
Companies don’t sell their goods/services at cost. A model’s being priced at, say, $30/M for output tokens doesn’t say anything about what it costs the company to provision the 1M tokens via the model.
And no, you cannot extrapolate from any company margins that someone may have overheard in an SF coffee shop onto individual product line margins or their trajectory either. This information is usually unknowable even in most SEC filings for public companies.
It’d be great if people who wrote these articles used, say, AI to look up some basics on how a business operates. It’s really easy to do, believe me ;)
Just because the stock market dips, and a bunch of media outlets ascribe this to AI doesn’t mean that AI ‘caused’ the dip.
If people really think non-tech businesses will start running their own software organizations and maintain all these tools themselves is an idiotic assumption at best.
The “building” of software has never been and will never be the bottleneck.
In other words, when I have a task that specifically should not have project context, then subagents are great. Claude will also summon these “swarms” for the same reason. For example, you can ask it to analyze a specific issue from multiple relevant POVs, and it will create multiple specialized agents.
However, without fail, I’ve found that creating a subagent for a task that requires project context will result in worse outcomes than using “main CC”, because the sub simply doesn’t receive enough context.
That being said, US drug prices are 2-4 times higher than they are elsewhere. In fact, the US market essentially subsidizes international drug markets, where it is much more difficult to charge higher rates due to regulations and lower purchasing power. This also means that, even if the allowed price in the US were known in this example, it would still have to be PPP adjusted to be compared.
Prescription card coupons such as those you get from GoodRx et al apply only to cash prices. These are sometimes lower and sometimes higher than what you’d pay out of pocket with your insurance. to compare, you’d basically have to ask the pharmacy to ring up a drug twice, once with cash price + coupon and once with insurance price.
“Copay assistance” are programs by drug manufacturers, PBMs, or employers to defray the cost of drug prices. This is usually done for specialty drugs that are much more expensive and can often only be purchased from a mail order pharmacy designated by the payer. For example, United Health (left pocket) will only cover the drugs if you get them from their Optum Specialty Pharmacy (right pocket).
As for numbers, here an example: I’m receiving a monthly specialty drug that my insurance is billed ~$9,000/month. In order to arrive at that figure, the drug company proposed, say, a $50k/month list price, and my insurer countered with $9k, using the size of their member pool as leverage. Of course, the drug manufacturer knew they would arrive at approx that figure, which is why they started negotiating that high. Well, sorta. The PBM gets compensated based on a % of the “savings” (spread between list price and final price), so naturally, they want as high as possible a list price, because 5% of a big amount (in my example: $50k-$9k = $41k) than 5% of a smaller amount. Because the PBM often has most of the leverage, the drug manufacturers (most of whom actually dislike PBMs) have to go along with this stupidity.
How much of the $9k I pay depends on a variety of factors, including my insurance plan, which has a specialty drug tier. This means the drug is not handled via the cost sharing accounting mechanism (deductible/copay).
Now, through that specialty tier, my monthly responsibility is set at approx $1,300. I’m not privy to the math behind this, because my insurer has outsourced all drug-related administration to a PBM, which is only loosely regulated and doesn’t even have to issue explanation of benefit statements that would normally disclose the full accounting.
Of the $1,300, I pay nothing, because the drug manufacturer provides me with a copay assistance card. Again, they must keep the list price high to placate the PBM.
I’ve simplified a few things here. For instance, there are now alternative comp models for PBMs. But for those, PBMs have also found ways to manipulate the system in their favor (eg colluding with drug manufacturers). There’s also often a wholesaler involved in the “value” chain.
But by and large, this is roughly how it works…
And yes, individual market plans are substantially inferior. Not only do they have lower actuarial values and higher cost per $ of coverage (which is unavoidable, because they are not risk pools) and narrower networks, but they also usually have built-in mechanisms to further prune away coverage in many subtle ways (they have that in common with self-insured plans): - more prior approvals, - “step therapy” (must first not tolerate cheaper drugs before can receive pricier drug), - not covering the pricier drug tier at all, etc.
These things shave some dollars off the premium, which appeals to price-elastic consumers and employers.
The article is quite embarrassing - it’s ok if you don’t know how P&L statement and balance sheet work, but writing an entire blog post without ever feeling the need to verify basic terminology is either very lazy or very ignorant…