voncheese

Yeah came here to say the same thing on the lack of absolute units.

Speaking as an older vinyl collector myself, with kids who have also gotten into it...it would surprise me if the total unit numbers were even within 2 orders of magnitude.

Vinyl just feels different. The feeling of holding the cardboard cover and the weight of the album on a turntable can't be replicated at all with CDs.

Agreed and it's unlikely to slow down that descent into slop coding until there are some visible issues with it. The market (i.e. companies) are going all in on AI coding because everyone else is doing it and the concern (understandably) is that if a company doesn't join that race they will lose because they never even entered the race.

The trick will be for companies to go fast enough to be in the race, not winning it, just in it. That will allow the time/space to let someone else, whoever is going fastest, to trip and fall so the rest of the pack can learn.

The tip and fall moment could come as a major incident (reliability and/or security) or loss of revenue because of bad products that customers don't like enough to use.

There's a world of difference between managing a dozen or so individual contributors vs managing senior managers / directors.

I agree, but in my opinion, your point about needing to know how things work still holds. It's less relevant as you move up to managing managers and larger orgs, but it never goes to zero if you're going to be successful.

Could this be a good thing - yes

Will it be is a different thing though. And if it’s not, who exactly is accountable?

With funds and portfolio managers that run them, there’s a clear accountability model (if the fund sucks, the manager loses their job and the company loses credibility)

With AI agents doing the management, who is accountable when the fund sucks? If it’s the customer, we’ve moved accountability from someone who at least in theory, knows what they’re doing to someone who has little to no clue.

Sure, still need to enable access the same info but feels like bucketing the clients into

bucket1 = clients that were working just fine before (users and whatever automation they had in place) bucket2 = ai clients that contributed to, if not flat out caused, the scale problems

then slowing down/limiting the bucket2 clients while keeping the bucket1 clients rolling as-is, is both doable and keeps existing customers happy while the underlying infra gets scale/perf improvements needed to support ai clients at scale.

Per a report that came out the other day, the GitHub move to Azure has been slowed down (i.e. I don't think it's done). But maybe you have newer/better info than me

Yeah, that and Microsoft has been slow to move the infrastructure to something that scales better to handle that load.

The more surpassing part is that Microsoft hasn't figured out a way to manage/contain the AI-sourced traffic better so it doesn't create all this noisy neighbor problems for non-AI usage/users.

Every use of AI for these robs the employee culture of a genuine trust building moment.

Spot on.

The erosion of communication and relationships between people in the workplace (or even outside it) that AI contributes to is something that we don't talk about nearly enough. Society today has already suffered greatly in these areas thanks to social media, and AI just makes it worse.

People (in general) are really struggling to understand when/how to use AI to be more productive and happier (and imo there is a way to do it, by offloading the grunt work to AI). With the constant rush and jamming of AI down everyone's throats though, its hard to be able to take that step back and think "is this use of AI making me happier/more productive".

This is awesome to hear about. The community aspects (both being part of the community and building community) can't be understated.

Thanks for sharing!

This sounds like an attempt to rationalize the fact that your business isn't that effective, otherwise adding more people would result in making more money.

Yes, or that businesses are expecting a slow down in the economy that hinders their ability to sell (i.e. their customers are going to cutback on spending)

This was the case last year (or maybe it was the year before) where technology companies saw their customers reducing spend and tightening belts.

The current economy feels hard to figure out, in that the market keeps going up but so is inflation and the struggle of the everyday American at least.

Perhaps that is leading technology companies to be more conservative in how much they produce.

This, and the fact that AI is taking people's jobs away without (as far as we can tell) significantly creating new jobs for those people to move to.

The job impact is really pissing people off, rightfully so.

+1 to all of this. The challenge can be staying focused and thinking when the AI assistant is (1) moving very fast and (2) often times doing multiple things at the same time.

I know I have struggled to keep up, and fall into the trap of approving things (either commands or recommendations) without taking the time to really process and think about them.

It's a bit like the age old problem of "it's super easy to ask questions, and can be super hard to answer many of them". So the economy of the conversation gets out of whack fast.

From reading the text of the article, and the direct quotes, I'm also unclear on why they booed him.

My guess is because of what he's done, or at least perceived to have done, in the area of AI. Because what he said (at least to me) didn't seem boo-worthy, but in the context of who is saying it, I can see it.

Put another way, if someone that the audience liked said the same things, its not clear the person would get booed.

New knowledge doesn't necessarily push out old knowledge, and we probably don't have infinite capacity for knowledge. That being said, at least in my experience, the time when new pushes out old is when old is less useful than new.

Retaining (again just speaking for myself) requires actually using / applying the knowledge at some point within some timeframe of learning it. Otherwise yeah it fades to the point of disappearing over time.

Relatable! Or at least making me feel dumb (at times). Things that help me feel smarter are

* actually writing more on my own - created a personal blog just to get myself to write more

* upleveling my thinking - think more about problems and framing

* leverage my experience - guide (or sometimes force) the AI assistant to leverage my experience to avoid problems

* learning new things - rather than let AI just replace things I can do, I use AI to help me learn new things/technology faster than I would have pre-AI

The problem with the current political situation/administration in the US is that there's so much existing conflict of interest going on that anytime the government investigates concerns about conflict of interest, it feels politically motivated because of the uneven investigation.

Unless I'm missing something, the linked article from MIT is about more than graduate students. That article talks about how changes introduced in 2025 are causing taxation on budgets that (as far as I can tell) affect all students.

The prior poster is making the case that might not be a bad thing, but its not just graduate students

Yeah, conceptually this isn't all that different from new VM SKUs coming out in clouds. The costs and rate of change for AI hardware may be higher, and perhaps enough higher to mess up the math, but conceptually its a model that has been proven to work.

Yeah that's a good callout for sure, the spending here is nuts so agree that it's not "just another business that has to price itself right to be competitive".

I guess if the time horizons is long, like 20 years, then maybe the spending, as it begins to amortize, gets more in line?

I was thinking that a comparison could be to cloud providers, each of which had to spend a lot of money to build out datacenter before making money. Difference there is AWS proved the product first, so when Microsoft and Google came along, they knew it would work and be profitable. With AI, nobody has proven it will work and be profitable, they're all competing for that at the same time which is a potentially dangerous mix for the reasons you cited.

As long as Apple and Google put reasonable AI capabilities on device, then software engineers will use those capabilities when it makes sense (the article gives lots of good examples of capabilities that make sense to run locally). As the author notes, it's cheaper and more reliable to run these things locally.

That also doesn't preclude LLM services from being massively successful, they'll just have to justify the pricing and complexity that comes with their adoption, just like any other product.

The article is good in that it highlights the need for AI agents/assistants to help with different parts of software development, not just the up front "build me a new widget" part. The author (correctly imo) frames that if someone just uses an AI agent/assistant at the new widget part, then they'll end up with a lot more code to maintain since with AI, they crank out more code. Even if it's high quality, there is maintenance cost over time.

That being said, the problem the author talks about is more of a self imposed thing than everyone is going to suffer thing. The author correctly points out the startup scenario, where its just "get this damn thing to work somehow so I can see if there's market fit and nab some customers". That scenario has typically always come with higher maintenance costs down the road because quality is (rightfully) lowered in the name of speed to see if there's a business and if there is, get it going.

Also felt like the author was reluctant to talk about how AI can actually help with the maintenance part. AI can be great at fixing old dependencies and annoying bugs (with human guidance). Those tasks can feel like toil for software engineers and the kinds of things a software engineer will want AI to help with

My point is that maybe it was intentional, but just bad UX culture.

This may be valid, but even if it is someone (or a group of people) at Amazon are violating one of their core leadership principles - Customer Obsession

https://www.amazon.jobs/content/en/our-workplace/leadership-...

A useful (and hopefully delightful) UX is key to showing customer obsession.

That being said, I personally feel the UX at Amazon sucks overall, not just for pricing/packaging but even getting basic shit done. So perhaps Amazon (or at least AWS) doesn't think a good UX is a key ingredient to demonstrating Customer Obsession.

To put the 14% into some context, per Google, Coinbase headcount has had the following headcount each year

+ 2021 | 3,730 employees + 2022 | 4,706 employees + 2023 | 3,416 employees + 2024 | 3,772 employees + 2025 | 4,951 employees + 2026 | 4,250*

*Estimated following May 2026 layoffs.

So the reduction gets them closer, but still higher than where they were in 2024. Given the fact that the crypto business doesn't seem to be growing much over the last few years it can be argued that they over hired in 2025 and going back to 2024 numbers just makes sense. And as others have said in the comments, they haven't turned a profit so likely this makes business sense and the AI shine is trying to make the news less ugly for investors.

To expand on this a little more, the absence of accountability contributes to the loss of learning. Mistakes and errors will always happen, whether they are sourced by humans or machines. But something (the human or the machine) has to be able to take accountability to have the opportunity to learn and improve so the chances of the same mistake happening again go down.

Since machines don't yet have the ability to take accountability, it falls on the human to do that. And organizations must enable / enforce this so they too can learn and improve.

Without that, there's a lot of dependency being pushed on the machine to (cross fingers) not make the same mistake again.

Oh man is right! The making stuff up is going to make this problem even bigger.

There will be people that correct those hallucinations, in that scenario it’s “only” the applicants time that is wasted.

There will be other people that don’t correct those hallucinations, in that scenario the best case outcome is wasted time for the applicants and interviewers (who find the mistake later). The worst case scenario is people are hired who aren’t capable of doing the job and that’s all kinds of messy and inefficient for all.

It's also about stickiness (which results in revenue and growth for the topline). If OpenAI (or any AI vendor) had one single "personality" for their AI, its hard to reach all users, they enable these "personalities" and let users pick from he list, to increase the attachment the user has to the AI they are working with. That then reduces churn and (in theory) increases consumption and revenue.

Agree that priorities aren't exclusive and there may be teams/individuals that aren't able to contribute if they stay in their current teams/roles

Where it becomes questionable though is when enough progress isn't being made on the top priority (reliability). If Github is being true to their word, they need to be pulling people off of teams that are working on features to work on reliability so that top priority gets the resourcing it needs.

Given the pace of improvement, and the cited example of moving to Azure from months ago, it's not super clear they are doing that. Also not clear that they aren't, maybe the move to Azure is just a more than 6mo project no matter how many people are on it.

Not directly related to the valuation question you asked, but for Google there's a lot of value in getting as much Anthropic workload to run on their hardware as possible. The value comes from getting the insights and learnings of running these workloads, especially when they run on custom Google hardware. That hardware will get better as a result and increase the likelihood that Google has world class AI hardware in the future.

I can't say with any confidence that the $40B is a reasonable amount to pay for that value, but it doesn't seem unreasonable over a multi year time horizon given the stakes.

Yeah and with long development, lead and change horizons that come with hardware, that's a super hard thing to do.

Software is easier given the shorter cycles. Caveat is, the shorter cycles also benefit competitors.

Thank you for sharing the link, it's a good read.

Also want to second your point about the need for having good people leading large organizations like Apple. Especially so as things are changing so fast in technology, with a widening impact across more and more aspects and parts of lives of people and society. We certainly see the negative impact that comes with questionable and/or short term decisions (see social media), so I too am hopeful that above all else, Ternus is a good person and makes (for the most part) good decisions for people and society first and foremost.