The only things that I do with AI at my job are easy things. AI hasn’t transformed hard work in quite the same way, but it has given me access to knowledge and information about things and processes that I didn’t have before
You could say in a years time that going in now was kind of insane.
Waiting is also a risk averse take and many organisations don't want to wait and are prepared to spend extra
Not only is predictability very important for publicly listed companies if they want any investors what so ever, many organizations older than say 25 years have a near constant hangover caused by poor tech decisions they've made in the past, redundant duplicate systems due to mergers and acquisitions, half-finished attempts to migrate a COBOL system to something newer, an Oracle DB license that costs a third of the IT budget they can't migrate off. The older the org the nastier it gets.
This is why the business world not only runs on Java, but old Java, it's why AAA game studios keep rehashing the same safe bets rather than trying something different, why Hollywood has turned into an assembly line for making superhero movies, why the sky is full of airliners designed in the 1960s.
Some of the largest and longest institutions are going in on AI.
And is exactly my point that these comments are coming from that these companies are going in on AI when some people think its not ready yet. My point is when exactly do you define that AI is ready to be used or when should you wait. Nobody can answer that question right now and is my response to the comments we're talking from.
> The sane move in such an environment is to wait and let other people leeroy jenkins in and make mistakes, learn from their blunders, and only move once clear best practices are in place.
How we do actually know when we have reached this point, is it now? Was it 12 months ago? Will it be in 12 months time? And should you just let your competitors invest in having a go while you wait. No I don't think you can.
Unfortunately, time is a constraint for every company that exists, and a kinda-crappy implementation of something that's available today often beats a perfect implementation tomorrow.
Like with accelerating productivity gains, why does it matter if I do most of the work in the beginning of a project, or toward the end?
Changing vendors is a pain in the ass, especially in B2B. Like case in point, Confluence has been around for 20+ years and still doesn't have a working search function, and is comically useless it is at everything it does. Yet businesses all over the world are all in on that garbage.
Holy strawman batman!
Wouldn't you prefer confluence fix their search now rather than hold off until the can fix it perfectly according to your end-all-be-all spec (that'll be invalidated after 2 weeks of actual use anyway)?
What I'd preferred was if they took the time to implement it correctly from the get go. What they built instead is of no use to anyone. It's a search function in name, but it doesn't provide useful search results.
Search is hard though. From how it's behaving I think they're just sticking the data in Elasticsearch or Lucene or some such, without having done the long and arduous effort of then making it good.
Though my point is that even though confluence has a search function so broken they effectively don't have a search function, they aren't meaningfully bleeding customers over it. Business sales are incredibly sticky that way. Regardless of what pleases me as a user, Atlassian has no real incentive to fix the problem, and evidently hasn't (at least last I checked).
Which is why I consider it better to use the tools that I have today to make a feature that is good enough. Rather than cynically leaning on some vendor lock-in to hope the customers stay.
Given that you seem to consider Atlassian's software so crappy, it's a bit confusing that you bring them up as an example of good management? Your advice is basically, write crappy software and don't spend any energy improving it, until you can do so for the minimum cost.
Pretty much 0% of users want a hastily implemented search bar that doesn't really work.
I would argue that 0% of users want the world's most perfect search bar, lovingly crafted, that took 2 years to make and went through multiple rounds of gold-plating, and occupied all of the team's time while their competitors expanded their product offering with tons of new features.
Somewhere in the middle there's going to be a distribution of acceptable-ness. I would wager a functional but unimpressive search bar that can be turned around in a few days beats something that's designed to the nines but takes a few months to ship.
I feel like the mandate thing is silly though. Go "all in" in the sense that you enable employees who want to use these things, but let engineers adopt at their own pace.
The proof is in their work output and overall job satisfaction.
Go faster now and get the benefits now (as TFA said, they did ship a lot of stuff very quickly), but risk piling up a huge amount of tech debt? Or wait and get less short term benefit, but more long term benefit?
What I can confidently tell you is writing a 2-sentence description of an application you want built does not work in any sense of the word. And, the difference between vibe coding and agentic engineering is vast.
Ok if this is the advice then I'm gonna look for a second opinion first. :)
More seriously: making any crap with AI is trivial and doesn't need learning. Making something better and more valuable than all the competitors *that have the same tools as you* is more involved, and relies 100% on knowing how to extract unique value from the tools. A part of that comes from your product/industry expertise (acquired before AI, much like your competitors), but unless you're a genius or in a greenfield, what will matter will be the other part where you have trained hard to be a better driver than your competition who just waited and watched a few youtubes.
If not, what are they even doing that they keep burning through piles of cash?
Not really. Models eat harnesses. Once the new model drops it invalidates lots of harness engineering people have done, and invalidates things they have learnt about how to efficiently use AI. The better models get the less need there is to need to know how to use them efficiently. They're also getting better at helping you use them efficiently.
It's weird. It's like no matter what you do, it's all just a waste of time.
or maybe double-down extra hard
But if that isn't your competitive landscape...
"reverse token value inflation"?
Do you perhaps means "deflation"?
I recall mid-to-late November 2025 being a big inflection point where it finally became obvious that these things could actually be trusted to do a decent amount (but not all) of the coding. So maybe it was more like 8 months ago or whatever that they went all-in, close enough to a year for a random reddit rant.
The overall gist of what the person posted matches my experience though. The fall-off on understanding the code to any meaningful degree happens very quick once you give full rein to the LLM and lean into mostly-automated reviewing.
You haven't really said how you actually get "more". Selling stuff usually works by resolving a need or want your customers have. If you can use AI as a tool to resolve one such want today, how will resolving it for 1/10th the cost in a few months actually help your business?
Even if you're making a new invention or something, sitting on the idea until you can make it for fewer dollars, will just open up to somebody else doing it earlier. And they'll have time to work out any kinks that inevitably only appear during actual testing...
That's not really what you claimed in the first part though. Your argument there was more along the lines of "regardless of how well your current tool works, you shouldn't use it if there's a likelihood a more efficient tool will come along in the future".
I mean, doesn't the below argument apply to like, buying a computer? According to Moore's law one has/had a "late mover's advantage" by where waiting to buy for two years doubled your value for money. So why do you own a computer already, aren't you better of saving your money?
> There's really a weird sort of late mover's advantage to just holding off and saving your money unless you expect your profit to grow faster than AI becomes more powerful.
You can plug in whatever numbers you want for the growth rate, but I don't think there are many uses of LLMs that require anything like months' worth of tokens to generate results at this point. Feel free to slack off if you are one of the exceptions.
But now having a few thousand hours experience building nontrivial systems through AI intermediation was worth it.