FR version is available. Content is displayed in original English for accuracy.
Advertisement
Advertisement
⚡ Community Insights
Discussion Sentiment
57% Positive
Analyzed from 3778 words in the discussion.
Trending Topics
#code#more#software#going#company#job#things#own#development#faster
Discussion (46 Comments)Read Original on HackerNews
Code takes 6-12 months to make it from commit to production. Development speed was never the bottleneck; it's all the other processes that take time: infra provisioning, testing, sign-offs, change management, deployment scheduling etc.
AI makes these post-development bottlenecks worse. Changes are now piling up at the door waiting to get on a release train.
Large enterprises need to learn how to ship software faster if they want to lock in ROI on their token spend. Unshipped code is a liability, not an asset.
So much of Management (both mid and executive) still considers Software as if it were an assembly line; "We make software just like how Ford makes cars". Code as a product.
Which isn't to say that most software development isn't woefully inefficient, but the important bits aren't even considered. "The Work" is seen as being writing code, not the research that goes into knowing what code has to be written.
And for AI marketing, this is almost a videogame-esque weakspot. Microsoft proclaims "50% faster code!" and every management fool thinks "50% faster product; 50% faster money!"
> Large enterprises need to learn how to ship software faster if they want to lock in ROI on their token spend.
It's going to be a disaster once ROI is demanded. Right now everyone is fine with not measuring it; Investors are drunk on hype and nobody within the company actually wants to admit that properly measuring software development productivity is almost impossible.
But the hype won't last forever. Sooner or later investors will see the "$2M spend" and demand "$4M net profit", and that's not going to materialize.
Copilot and Claude won't be tackling the real bottlenecks. They're not going to dredge up decade old institutional knowledge, they won't figure out whether code looks bad because it is bad or because it solves a specific undocumented problem, they won't anticipate future uses.
Code just isn't the product. Not the real work. Really, if your codebase is in a healthy state, it's often a literally free output of the design and research processes. By the time you've refined "our procurement team finds the search hard to use" into a practical ticket, the React component for the appropriate search filters has basically already been written, writing up the code is just a short formality. Asking Copilot would turn a 10 minute job into a 5 minute job. Real impressive, were it not for the 6 hours of meetings and phone calls that went into it.
I think this is probably going to happen at the same time that the providers start really jacking up token prices to extract all the value they can.
Right now the subscriptions are still in the range of reasonable business expenses, but pretty soon they'll have to jump and $200/month/seat subscriptions turning into $2000/month/seat subscriptions is going to get even very badly ran companies to re-evaluate.
They haven't even learned that "less code is better" yet, I wouldn't hold my breathe waiting for them to suddenly learn "more advanced" things like that before they learn the basics.
Feedback is often only considered once something is already on fire (financially, functionally, or literally).
I would argue that any sufficiently large system reaches a point where more code is in fact the opposite of what it needs.
Nutrition and calories are only useful up-to a point and then we have diminishing and later on negative returns.
Even-tough it is not the best analogy because we are describing two different system, it helps put a mental model around the fact that churning more is often less.
Side Note: A got a feedback from a customer today that while our documentation is complete and very detailed, they find it to be too overwhelming. It turns out having a few bullet points to get the idea across it better than 5 page document. Now it is obvious.
Tl;dr's, quick references / QuickStarts / cheat sheets and FAQs are also some things they're great at generating.
[0] https://marketoonist.com/2023/03/ai-written-ai-read.html
The Theory of Constraints - AI Era
[0] https://en.wikipedia.org/wiki/Theory_of_constraints [1] https://www.goodreads.com/book/show/113934.The_Goal [2] https://www.goodreads.com/en/book/show/17255186-the-phoenix-...
This was a huge motivation behind me trying to design an AI automation platform that comes "batteries included". I also think a lot of orgs, even engineering orgs do not know how to configure basic things like Claude plugin repositories into their installs.
SAFe is poison.
Organizations "born in AI" appear to buck this trend for obvious reasons (no legacy org. to deal with). My two cents.
At what point is inspiration and thought just devalued and worthless in the name of doing things instantly. The work has no soul.
The loop closes only when customers' insights have a proper place to go, where duplicates can be filtered out and priorities set. This isn't happening for many teams, and the acceleration of code generation will only make things worse.
My company set up a “prompt of the week” award and brown-bag sessions to help spread adoption. We also have teams meant to develop these workflows. Clearly, they set these events up to play it off as their own productivity. Without a real (read “monetary”) incentive or job security, the risk and cost of spreading the knowledge falls squarely on the developer.
If developers are worried about their jobs with the way the market currently is, they should treat their personal workflows as trade secrets. My example was not specific to AI, but it applies just as much to AI workflows. In a worker's market, it was sometimes fun to share that kind of knowledge with an organization. In an employer's market, they can pay me if they want access to my personal choices.
If your employer is expecting that you selflessly share your time for free, you’re getting fucked. Most people are paid to do their job. They are, of course, then expected to work for their employers while on the clock.
The CEO has a youtube style platinum token plaque for their office.
But the internet was a simpler concept for businesses. Basically it was you can now sell to people from their computers. AI’s promise is what? It can approximate reasoning about things? This is much more challenging implementation puzzle to truly solve.
I don’t know that I’ve seen anything of real substance outside coding tasks yet.
In the old model, performance and OKRs were anchored in disciplines, job titles, and role-specific expectations. In the AI era, those boundaries are starting to collapse. The deeper issue is psychological and organizational: people are constantly negotiating the line between “this is my job” and “this is not my responsibility.”
That creates a key adoption problem: what is the upside of being visibly recognized as an expert AI user? If people learn that I can do faster, better, and more cross-functional work, why would I reveal that unless the company also creates a clear system for recognition, compensation, or career growth?
Take Andrej Karpathy as an example. Even if I knew exactly what tools he uses and what his workflow looks like, I still would not be able to produce anything close to what he can produce in a few weeks. And he is not standing still either—he is evolving at the same time.
A lot of real expertise is not in the visible/system-able workflow. It is in someone’s experience, taste, judgment, and wisdom. You can copy the artifact, but you cannot easily copy the thinking behind it: the principles, the decision-making, and the ability to apply those principles across many different/subtle situations.
But I do agree with the concern behind the argument. People may worry that sharing what they know could weaken their own position. And the more uncomfortable question is about peers: if someone’s role can be “retired” because others absorbed their knowledge and skills, then it is hard not to ask, “Am I next?”
While I do believe higher developer productivity can lead to faster reacting to market forces or more A/B testing, that won't necessarily lead to a successful business. Because ultimately it rarely is the software that's the issue there.
It really comes into its own when you treat it as a tool that can build other tools. For example, having it build tools that force it to keep going until its work reaches a certain quality, or runs compliance checks on its outputs and tells it where it needs to fix things. Then and only then, can you trust its work.
Right now most current roles & workflows are designed around wrangling the tools you’re given to do a certain job. In that regime AI can only slide in at the edges.
Our mental models of developments like the industrial revolution, literacy, printing or suchlike tend to be a lot more straightforward than how things play out in practice.
When a bottleneck is eliminated... you tend to shortly find the next bottleneck.
Meanwhile, there is an underlying assumption everyone seems to make that "more software, more value" is the basic reality. But... I'm skeptical.
To do lists, wishlists, buglists and road maps may be full of stuff but...
Visa or Salesforce have already exploited all their immediate "more software, more money" opportunities.
The ones in a position to easily leverage AI are upstarts. They're starting with nothing. No code. No features. No software. With Ai, presumably, they can produce more software and make value.
Also... I think overextended market rationalism leads people to see everything as an industrial revolution...which irl is much more of an exception.
The networked personal computing revokution put a pc one every desk. It digitized everything. Do we have way better administration for less cost? Not really. Most administrations have grown.
Did law fundamentally change dues to dugital efficiency? No. Not really.
If you work on a terrible enterprise codebase... it's very possible that software quality/quantity isn't actually that important to your organization.
Debugging and developing first fixes is also one of the spaces where current LLMs are the biggest force multipliers. Especially if you have reproduction cases the LLM can test on its own
But long-term it might look very different as more and more of the code becomes LLM written
AI content has a look and feel people sense immediately.
It’s amazing to see how quickly things shifted from “wow this is so cool, AI is going to change everything” to folks calling out “you lazy bum, this just looks like some slop you threw together with AI… let’s get some real thinking please.”
We are firmly heading into “trough of disillusionment” territory on the hype cycle.
Can we get some enabling legislation? A UN resolution perhaps?
The more I use AI, the more I see mistakes. I've noticed others see these same mistakes, correct them, then when queried say "Oh, it gets it right all of the time!". No, having to point out "you got this wrong, re-write that last bit" isn't "getting it right". And it's not that the code is wrong overtly, it's subtle. Not using a function correctly, not passing something through it should (and the default happens to just work -- during testing), and more. LLMs are great at subtle bugs.
So moving forward with this isolation you mention, ensures that maybe the guy in the company, the 'answer guy' about a thing, never actually appears. Maybe, he doesn't even get to know his own code well enough to be the answer guy.
And so when an LLM writes a weird routine, instead of being able to say "No, re-write that last bit", you'll have to shrug and say "the code looks fine, right?", because you, and the answer guy, if he exists, don't know the code well enough to see the subtle mistakes.
AI can get a pretty good picture, near instantly, whenever you need it.
It’s not just competent-sounding, it is reasonably competent, and certainly very useful for tasks like that.
Gone are the days of mandatory corporate "synergy" and after-work bar gatherings to promote "team building."
AI is showing people in the tech industry that they're just interchangeable cogs. AI is bringing the offshored Indian work environment to Silicon Valley.
> I do not want to make this a cost panic story, that would be the least interesting way to think about “rented intelligence”. The question is not how to minimize token spend in the abstract, any more than the question of software delivery was ever how to minimize keystrokes.
If tokens were as cheap as keystrokes -that is, effectively free- then "How do we minimize token spend?" wouldn't be a question that anyone asks. It's because keystrokes are effectively free that you only ask "How do we minimize the number of keys pressed during the software development process?" if you're looking for an entertaining weekend project. If keystrokes cost as much per unit of work done as the -currently heavily subsidized- cost of tokens from OpenAI and Anthropic, you'd see a lot of focus on golfing everything under the sun all the damn time.
This is just sales copy for various AI companies, laundered through an "influencer". It might as well be the CIA sending their article to be published in Daily Post Nigeria, so that the NYT can quote it as "sources".
The title is just clickbait. The rest of the content are fluffy bunnies and rainbows. It's all summed up as "continue to consume product, but remember to also do X". Sales copy + HBR MBA bait.
The closest thing to an honest, less-than-rosy example is the "junior person" who has no idea about the code they committed.
What about the "senior person" who has no idea about the code they committed? What about the CISO who doesn't understand that pasting proprietary documents willy nilly into the LLM's gaping maw might have legal/security/common sense implications, and that it is his job to set policy on such behavior? What about the middle manager who doesn't even try to retain the most experienced dev in the company because "we don't need the headcount anymore, now that Claude is so fast"? What about the company eating its own seed corn because every single junior position has been eliminated and there are no plans for the future anymore? What about the filesystem developer who fell in love with his chatbot girlfriend and is crashing out on Discord?
Oh wait, scratch that last one. He left the company and is crashing out on his own.
Carry on, then.