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Comment by ddosmax556 | Hacker News Reader

This article assumes that AI only has an impact on the development phase which is certainly not true. It can speed up every part of the step. Including ideation, legal, documentation, development, and deployment.

Ideation: Throw ideas back & forth, cross reference with knowledge bases, generate design documents. Documentation: Generate large parts of docs. Development: Clear. Deployment: Generate deployment manifests, tooling around testing, knowledge around cloud platforms.

Every single step can be done better & faster with AI. Not all of them, but a lot.

Even development. Yes some part of your job involves understanding the problem better than anyone & making solutions. But some parts are also purely chore. If you know you keed a button doing X, then designing that button, placing it, figuring out edge cases with hover & press states, connecting to the backend etc - this is chore that can be skipped. Same principle applies to almost all steps.

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I tend to agree with the article.

A typical example of trying to add a new significant capability involves many meetings (days, weeks, months, etc. )with the business to understand how their work flows between systems X, Y and Z as well as all of the significant exceptions (e.g. we handle subset A this way and subset B that way, but for the final step we blend those groups together, except for subset C which requires special process 97).

Then with that understanding comes the system solutioning across multiple systems that can be a blend of internal system or vendor's system, each with different levels of ability to customize, which pushes the shape of the final solution in different directions.

There is certainly value in speeding up coding, but it's just one piece of the puzzle and today LLM's can't help with gathering the domain information and defining a solution.

I've seen proposals for Product Managers to define those conditions themselves by speaking with the LLM. A continuing architectural diagram is constructed and graph is updated until all cases are covered and then the LLM writes the code, writes the validations, pushes to CI environments, runs tests, schedules prod deploy (by looking at company event schedule), gets CAB approval, deploys code, tests in prod, and fixes regressions.

I'm not saying this is the correct thing, but companies are implementing it and it is "working". I don't think keeping our head in the sand is helping.

is it working though? The main outcome we've seen with companies that drink the AI Kool aid en masse is buggy unstable systems. clearly there's a level of rigor that's being missed for ship velocity

> I've seen proposals for Product Managers to define those conditions themselves by speaking with the LLM.

But the LLM is not aware of how the business works and why, so someone needs to work with the business to extract the information. Typically it's not well documented.

> someone needs to work with the business to extract the information. Typically it's not well documented.

LLM extraction of the information from the Product Owner is becoming the way to overcome poorly-documented business context.

Non-technical folk are using things like `/grill-me` [0] to seed the LLM with the long-tail complexities that they didn't know they didn't know they needed to put out.

[0] https://www.aihero.dev/my-grill-me-skill-has-gone-viral

They can ask, they can do a back and forth and they can write documentation to be used from that point onwards and write it in a common style and structure.

These are language models, being able to talk through something with them and have them extract some information is what they excel at. Given that you’d probably get a halfway decent result with a literal fixed set of questions (an Eliza level docbot) gpt 5.5 is going to nail that as a task.

What I've seen in an AI-forward looking environment is that it's much more common for PM/POs to be knocking up at least a UI prototype now, and experimentation is happening often even before writing the tickets. Similarly when devs are proposing something they often are coming with a couple of prototypes already implemented. Both of those mean decisions are coming a lot quicker.

I wouldn’t discount the value of moving small tasks away from developers, nor the value of fast cheap prototypes.

Product owners can very quickly get, for many problems, an interactive demo without coding. For lots of problems this can be somewhere from a static html page which shows the interactions to a hacked in feature that lets them actually test if it solves the customer need and try several variations before handing over much more concrete specs of what they want to happen. So much time is lost between getting an idea from someone’s head to code to use to then find out it wasn’t communicated well and then finally that the idea didn’t help anyway and we want it in a different way.

Yes yes I know someone is about to say that now there’s pressure to push the prototype out but that’s an organisational level problem that existed anyway.

And small problems can much faster to solve as well, or even move away from devs. Often people just need some text changed somewhere or html putting together, or some basic code for analysis. They could understand the logic, but the task of writing it from scratch and how to run things may be too much - now you don’t need to prioritise work for a dev to get some sql written and they can spend their time on the larger more software engineering level problems.

"that’s an organisational level problem that existed anyway"

That's very true to many organizations. One cannot just slap an AI tool on it when you are dealing with fundamental organizational problems in the first place.

"they can spend their time on the larger more software engineering level problems"

For sure, devs still needs to focus on the right type of work and maintain the balance. I built a tool to just do that: https://worktypefocus.com/

All of the above points align with our organization’s experience. But there is one more thing happening as well: we have more people in more roles able to create software solutions for issues that used to be brute forced via physical processes. (We are a small manufacturing business.) While these aren’t big giant enterprise projects that require deep swe experience, they are simple software tools that are improving process and productivity everywhere. It is pretty amazing what happens when your head of shipping can build a bespoke tool to solve a problem that previously they dealt with through burning through a lot of labor hours.

yeah the Coinbase CEO gleefully pointed that out as well and now the market thinks they are totally incompetent every time some UX quirk is found

looks like orgs have to have engineers on for optics. like having a legal staff with no lawyers, or a cybersecurity staff with no IT or certified people. Software has famously not needed state licenses or industry certification, but maybe thats a direction to consider to give utility to company optics.

One of our BAs created a site that tests the effectiveness of copy / layout adjustments. I don't even know exactly what that's called but he's able to do statistical analysis much faster on what works and what doesn't. It's really cool to watch him thrive and I feel like some of the thinkers that were not devs are going to find themselves to be one but in their specific domain in a few years

I would be really interested in the details of these kind of tools that are improving processes and productivity.

Are they reasonably documented/audited/put into any sort of version control like a lot of internal tooling? Or are they the kind of the thing that gets whacked together on the fly in a "move spreadsheet data from A to B", "I want a list of people's schedules with custom highlighting" kind of things.

Not doubting your productivity increase, I'm just curious how people quantify that when they say it.

Yes. In the same way that spreadsheets are the dev tools for non-devs, LLMs could step into that role, but with much more powerful end result. With the caveat that in the same way you can create a powerful foot-gun with a spreadsheet you can probably create a foot-cannon with an LLM.

One of my beliefs about AI, for small / medium sized companies it allows them massive speed ups and generally increases their capability (I'm also in this space), existing employees of all types essentially get massive speed boosts / opens pathways not available before. For big companies, they are likely to have a bunch of problems due to size, communication pathways, management structures, decision making structures, etc.

The article pretty much plays out whats happening in our place, heavy use of AI in software development but we dont see us shipping faster, about same or perhaps slower (for other reasons). Its a weird feeling as were waiting for this utopia to kick-in but its not and were cant fully put our fingers on it.

The article and the AI skepticism crowd on HN read like the blind leading the blind to me.

I'm at a FAANG. My org is moving much more quickly, maybe between 3-10x more quickly than we were pre-AI. We aren't seeing a spike in reliability issues. Things just get done faster. An org as large as mine has no right to move as fast as it does.

I’ve been back through your post history (not entirely) - you mention multiple times you work at a FAANG - so you work at one of 5 very public companies.

You have been asked multiple times by multiple commenters to provide a single example of something that reflects this incredible boost achieved by <massive tech org>, you have ignored every request for this, and I suspect will ignore this one as well. HN is going to die unless we all start calling these constant deceptive practices out. I’ll leave others to parse your history and make their own judgements.

“Judge them by their fruits”

Not going to break NDA and give up our competitive advantage for HN, sorry! I can tell you it's been useful for us, but thinking about how to use it is an exercise left for the reader.

Perfect excuse for avoiding any substance (except no one asked you to give up the advantage or secret sauce of using LLMs, only the end examples of what has been achieved with it). It is also funny how you always leave out the name of the company you allegedly work at. It is perfectly clear why you do that, though: no matter what company you name, its actual employees on HN will quickly disprove all the ridiculous claims you have made regarding LLMs and AI. Keeping the name ambiguous lets you get away with it.

Anyways, solenoid0937 is an LLM-hype peddler and an Anthropic shill, not an actual FAANG-employee. See proof here: https://news.ycombinator.com/item?id=48269250

It's highly team dependent. Shortly, the more "coding monkey" the work is, the more velocity you can get with AI. As soon as you need to interface with customers and extract requirements, that becomes the bottleneck.

The main problem is, it's not a one-size-fit-all tool, you need to understand what it speeds up to benefit from the speed up.

And if it is a chore, we already have some tools to speed it up, only if it is worth it though. Placing a button is actually easy if you get all the design system down usually with a component library, visual regression automation and testing automation.

If a team doesn't have tools and automation in place, AI might speed them up a little but it adds a layer of complexity, i.e. everyone have to manage their own workflow and tools. And when you try to align the team, you get the tools and automation that the team is supposed to have in the first place.

As for ideation, the problem isn't the speed of information ingestion but the ability to connect and understand different parts of the information, which require thinking. More information at times is just going to hinder the ability to think. For example, it is obvious to developers why there is a rate limit for the APIs but for PMs it might not be obvious. They might ask the AI whether or not a rate limit can be removed easily, how many days if you vibe code it and ignore the possibility that the rate limit might by abused by users just to improve a feature because it is too slow.

We are still doing alot of work with new tools but old methods though, it will be interesting to see how far can we go if we forget about the old rules and embrace the chaos entirely.

The onus isn't on people using AI effectively to prove it to others.

In fact, these disagreements and disbeliefs create opportunities and salients in the market.

Indeed. I suspect most effective AI users are quietly making real progress toward their objectives.

Anecdotally, I see a lot of problems/solutions content about AI that doesn't reflect at all the challenges I face. But trying to tell people that there are other ways of doing things, especially when it conflicts with token-maxxing, is a lost cause

I know and I agree. It sounds incredibly arrogant but it's frankly is a bid sad to see how much HN is lagging behind AI adaption. It's been 90% noise over the last 3-6 months about problems that aren't truly problems if you really look hard at what AI is capable to do already today. It's mostly ppl & process problems. I could post a comment like the one above below almost every article on AI. But it is what it is. It's an opportunity for anyone who doesn't bite into the cynical tone here for sure.

The HN AI skeptics are just bizarre to me. They are insisting to us that, no, the productivity gains we're experiencing every day, simply don't exist!

It's not that they're using the tool wrong, it's that the tool just isn't capable of what we see before our own eyes! I guess our eyes and ears are simply lying to us?

And then they ask for how we are managing to make things move faster. When you refuse to breach NDA and give up your competitive advantage on HN, this somehow confirms their belief that AI is useless.

> When you refuse to breach NDA and give up your competitive advantage on HN

I want to know your alpha because that excites me.

These people have their heads buried in the sand.

Also things like improving devx - nicer log analysis, speeding up test suites, auto handing some CI failures, improving scripting, tooling etc.

Precisely. People don't realize that it's all numbers. Given average IQ of people involved in a project is 140, an AI with an IQ of 150 can replicate each and every such individuals in the pipeline. People saying AI can't do this or AI can't do that should come to terms with the fact that this IQ gap is monotonously increasing.

Monotonically although I do find the discourse on AI rather monotonous.

Funnily enough, though, I think it makes dumb people dumber.

I agree. Inexperienced people (not necessarily "dumb") are likely to accept everything at face value, not apply critical thinking skills, and not even check the AI generated output.

An AI does not have an IQ.

Sure it does. IQ is simply a measure of performance on an IQ test. A simple Python loop around Google search in 2012 had an IQ.

IQ is a (biased) proxy measure for human intelligence. It is not a meaningful measure when applied to a computer system.

What IQ "means" is separable from what it is. IQ is a measure of performance on IQ tests. That's literally what it is. If a computer system can complete IQ tests, it has an IQ.

The issue is that IQ means less than you want it to.

I don't want IQ to mean anything. pkoird clearly wants it to mean something.

IQ is a terribly flawed measure of human intelligence. But it measures nothing when you apply an LLM that contains multiple IQ tests in its corpus. IQ is deeply flawed, but the point is not to "measure performance on IQ tests". If someone cheats on an IQ test and scores 200, no reasonable person would say they have 200 IQ.

This is bizarre to me on so many fronts.

1: When was the last time you worked on a project where you thought the average IQ was 140? I don’t even think I have worked on a project where the maximum IQ was 140.

2: Who thinks the IQ of people on the project determines its success? There’s so much more to it than just “high capability team members” (to give IQ a generous interpretation).

3: (math joke) A sequence like (AI IQ - Human IQ) can be negative and monotonicly increasing and still never reach 0.

Pattern matching against millions of IQ test questions from a training set in order to score 150 on an IQ test doesn't give you an intelligence equivalent to 150.

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