Choosing the right problem to solve was always very important. The code wasn’t always the hard part, but arguably it really was the hard part more often earlier on.
For example, a personal computer operating system that runs all your existing programs at full speed but doesn’t crash all the time and the ability to run a virtual machine on an x86 PC both took as long as they did to get to market primarily due to the technical difficulty, not because it was hard to find people ready to pay for them. Those kinds of hard coding problems are less plentiful, or at least less obvious today.
I’m curious, what are some examples of XYZ? What does “mostly technical” mean to you, and why do you think it is the industry doesn’t want that? Just trying to understand, because I don’t think I’ve experienced this myself.
I'm talking about the day to day work. At least in my experience at several big tech companies, the more senior an engineer is, the more you need to delegate work and are expect to lead projects by managing them. There's technical knowledge involved of course but it's a small part of it, mostly directing other more junior engineers.
AI allows you to move faster, but it also demands that you pay more attention in proportion to how fast you want to move. This requires more focus, i.e. more time developing. Effectively the structure at companies should swing back to how it used to be a few decades ago (I think, maybe I'm wrong and it's always been like this), where engineers mostly focused on delivering code. The difference now is that they can be expected to deliver much more code at higher quality.
To summarize, using a little AI doesn't pay off, you need to be doing that all day, and that is not how engineers spend their time in most of the industry anymore.
Thanks for clarifying. There have always been technical leads and hands-on engineering managers, but two things have changed:
1. The balance of responsibilities attributed to different titles has varied over time and across different companies.
2. Early personal computers were so resource limited that there were only so many programs you could write that actually made effective use of large teams. Today we can write much larger programs, which requires more coordination (across people and with AI—which can reduce the number of people required).
I would also strongly push back on the assertions that being very technical means spending most of your time on the code, and that you have to be using AI all day for it to be useful. Let’s say I’m a technical lead or a manager reviewing a design doc for a large project. I have an idea based on my years of technical involvement for how to significantly improve the quality or delivery timeline for the project. I used to have to ask the doc author to look into this approach, not knowing for sure if it would directly translate. Now I can take my crazy idea, quickly dispatch a coding agent to try it out against the codebase, go to a meeting, and then check on what it came up with to give a much more informed and technically accurate suggestion on the doc. I still have to have the technical expertise to iterate a bit with the agent and not over or under state the possible impact of my idea. But this lets me take say an hour rather than a week to come up with a vetted recommendation that saves months on the schedule.
I'm just speculating why the current structure of companies is not maximizing what AI can offer. Until it becomes completely self-directed and reliable, using a tool that allows you to move fast requires more technical knowledge, not less. What you're missing from your example is releasing to production without breaking it, dealing with increasing complexity as more features are added, maintaining development pace as more features are added, and trying not to create more bugs than are fixed.
The idea is more or less benefiting from doing work in bulk which AI is now allowing. I even think multiple engineers (human redundancy to double check the same code) should team up with one AI system and form a small team that handles millions of lines of code, with the only job to feed incoming tickets and check the AI work. Anyone not reviewing code should be facilitating everything else with their own AIs. I think large companies can collapse entire engineering departments into a few people. The cost would be that everyone else has to think like an engineer to sort out requirements and provide direction, but as you point out, this is now easier with the help of AI.
IMO, a large amount of work, including a large amount of technical work is wasted effort. At startups you see it when they go out of business, at a big companies you see it as bloat. So unless AI is directed at finding the right problems to solve, past some point engineering efficiency via AI at best saves some money and at worst means more ultimately low, zero or negative value work gets done.
Maybe this is why you’re seeing more effort shifted away from technical work. Maybe I’m a nihilist. Maybe both?
That was true when implementation was expensive. Now that implementation is cheap, doing it in bulk is the best way to maximize AI usage, and that means fewer engineers doing more of the technical work.
> unless AI is directed at finding the right problems to solve
This direction can be done by non-engineers with AI to consult (replacing that part of human engineer's role).
My guess is in the end we’ll find the approach that works depends more on the personalities of the people involved as opposed to one approach being strictly better.
Actually, there is one other important thing that has changed: as developing a certain amount of functionality has gotten easier with modern languages, tools, libraries and coding agents, it’s also gotten less interesting for technology-driven people and more accessible to career-driven people.
Implementing the first text edit field in your program is kind of interesting when all you have is asm and 4k of RAM. Nobody wants to bother doing it manually when you have a web browser that can eat gigs of RAM.
This has been happening before AI, but AI has turned it up to 11. The way I usually put it, senior engineers are the new engineering manager, engineering managers are the new product managers, and product managers are now something in between sales, consultants or social media contacts. AI made senior engineers pick up more product management from their engineering managers, and engineering managers are picking up more sales and social engagement, and product managers are in a limbo but the young ones are pushed to junior engineer roles.
In large companies there is also the politics game. The more freed time senior engineering manager and senior product managers get, the more politics intensives and waste more of their time.
I use the term “product management” in its traditional sense of establishing scope, feature prioritization, user experience, shape of integrations, etc.
I’ve worked most of my career without dedicated product management support (very early startups and internal infrastructure teams at large companies), having to do that myself (both as an individual contributor engineer and as a very technical manager). So I have to wonder how much has the industry changed, and how much has your location within the industry drifted?
Yes my view mostly from within large engineering organizations. And yes, what you describe is pretty typical for engineers (and especially founding engineers) in young startups. Doubly so if it’s a startup with a technical product. Large organizations always envied startups’ efficiency and pace because of that, which is why the traditional sense of product management has been handed to engineering teams. This has been happening in the enterprise side for a long over the last 15 years and predates AI as I mentioned. It’s just more collapsing of disciplines now which is the answer I was giving to OP’s question.
That’s not at all what I’ve seen. If someone’s properly leveled at Staff, it tends to be a bit easier to sustain than Senior in the same company, but you do have to be selective about new roles at new companies. Age shouldn’t be an issue for someone with Staff+ skills except in the most dysfunctional, unprofitable companies.
I am going to judge, because people need to hear it. Anyone with $5M in invested assets who still thinks of making money as their top career priority needs to learn to think bigger. That’s your safety net, not your score in life. Now you can focus on what you find it rewarding to be doing, and how you can provide the most value to others.
This is about budget caps (“I don’t want to spend more than $100, cut me off once I spend that much”) not price caps (“no one is allowed to charge more than this price per token”).
If you just need to receive an SMS but don’t actually need a cellular network, Google Voice is free.
If you can stomach $3/mo + tax (totaling a little over $40/year), UltraMobile PayGo is the cheapest consumer option I’ve seen. It’s a T-Mobile owned MVNO. It comes with 100MB, 100 minutes and 100 text messages per month, and is pay as you go after that.
There is a huge business opportunity to become the world leader in sustainable data center design. Whoever wins could one day have immense influence over every single data center in the world (or even out of this world, where efficient resource utilization would likely be essential). It’s disappointing to see the United States miss out: US companies and research institutions would actually be extremely competitive in this field with the right government incentives.
Sustainable design for datacenters is orthogonal to success in data centers. That's like saying a local bakery should become a leader in pet adoption - it'll score some feel good points, but is almost counterproductive to the actual mission.
A data center (right now) needs to be built fast, with a large amount of always-on power. Sticking a gas turbine behind the meter in the parking lot is one of the most practical ways to get there.
It’s not just a feel good thing. There are real resource limits, even if someone chooses to foolishly not believe that. If you can get more done with a given level of power and other resources, you get more out of your data centers.
"Given level of power" is meaningless for data centers. If they can get 50MW of power, they'll obviously try to use it as efficiently as possible to generate as much compute/value.
But that's not what anyone means when they're talking about sustainability for data centers in terms of power usage: they're either talking less power (ie less value) or power from renewable sources, which can't compete with sources like gas turbines across a mix of setup time, consistency, and power/$.
If solar and batteries were available sooner than gas turbines, GE wouldn't have a multiyear waiting list.
If solar/batteries were cheaper than gas turbines after accounting for land/permitting/maintenance, every DC would be buying the empty land next to them for an eventual switch to solar.
Notably these many-million dollar investments aren't doing those things, and I guarantee you they have tried every angle imaginable to get power to the DC as quickly and cheaply as possible.
I don’t mean for one data center, I mean globally. There are resource limits to this planet, and we’ve been stretching past them in the short term, and are starting to see the long term consequences. The problem is people think “oh, it’s ok if I pollute a little more because the world is large, and the god we’ll create in our image with these machines will figure it all out for us.” That’s an unsustainable business model based on impressive technology managed by people stuck in childlike fantasies.
I think most people who are working towards greater AI would consider that not an opportunity, but planning to lose in a game that is winner(s) take all.
Winning scenarios in AI don't include being accountable for sustainability and they don't include expertise being valuable. Winners will decide what sustainable means for them.
Winner take all is a farce. It didn't work for Uber when they tried to out-spend the competition, Lyft is still very much a thing, and so are taxis (the non-robo kind). There will always be many, many AI companies and trying to execute "winner take all" in this space is only going to make more problems and waste a shit-ton of money.
If you subscribe to an AGI/ASI view, the stakes are different than the network effects that Uber thinks they could get. Success here would mean you could theoretically pull away from the competition so far and so fast, whether that be at a company or nation-state level.
The situation only has to be sustainable for those entities that get a vote that matters. I don't think they've given us any reason to believe they intend on that being a whole lot of people. In fact they've been quite up-front about saying they are concerned about the possibility of that number being zero
Isn't the goal to power most non-earthbound data centers with solar? I am no scientist, but going out to space feels like it gets rid of most of the downsides to solar.
There once was a time when tech companies mostly stayed out of SF. People who worked in the valley but hated it lived in SF amongst mostly people who had nothing to do with the tech industry. Then someone had the bright idea to put branch offices or even whole companies in SF. Convenient for the commute (and you could move to the East or North Bay), but then SF got overrun by the tech monoculture + NIMBYs trying to “keep it the same” by preventing construction, effectively kicking out so many of the people who were the reason why valleyphobic tech workers sought out the City of Saffron in the first place.
For example, a personal computer operating system that runs all your existing programs at full speed but doesn’t crash all the time and the ability to run a virtual machine on an x86 PC both took as long as they did to get to market primarily due to the technical difficulty, not because it was hard to find people ready to pay for them. Those kinds of hard coding problems are less plentiful, or at least less obvious today.
reply