Mean? Sure. Reality? You betcha. It's incredibly rare these days to encounter truly competent professionals. Most are just hoping the guy below them doesn't know enough to spot their shortfalls and speak up.

This aligns shockingly well with Uncle Bob's rough stat: “The number of programmers doubles every five years or so. This means that half the programmers in the world have less than five years of experience.”

It's also true in any industry that has high turnover. The people that fake it the best rapidly rise to the top and then just before their reputation pops, they move somewhere else and run the same routine all over again.

So where do they all go (I doubt the number of grads is doubling)?

I think a lot realise that programming is not their bag and move into account management, IT support, Business Support, or even other career's entirely.

This part is honestly the most worrying to me, as compliance with customers and legal would really need you to not lie about this.

There's definitely a rush of people trying to upskill/reskill into this technology space despite having no formal training or background beyond basic dev skills. There's other people (such as myself) that came from the big data/NLP space (ads & search) that are trying to add AI to our extensive skillsets but aren't necessarily deep-math experts.

Unfortunately there's not a lot of room at the top and the vast majority of AI implementations at smaller companies are just OpenAI API wrappers. Essentially there's very little lived experience since it's expensive to experiment at home and smaller companies just aren't going to invest in self-hosted models that are expensive to run and quickly fall behind state of the art.

I’m at a company, on an AI team. I’ve been in this role for a year and we’ve not delivered a single AI… anything. Worse, we had a failed launch midway through the year and instead of re-evaluating our focus, are doubling down. New people have been brought onboard and the culture has completely shifted.

If anyone is looking for someone with ~20 years of SWE experience who really, really wants to deeply understand internal processes, how they relate to the company’s product and bottom line, and implement with an eye toward efficiency and automation… let me know.

Right now this understanding is a liability. Having someone able to do reality checks can only slow down a team and expose everyone else.

I don't think we will ever see deep knowledge of computers and ability to develop non trivial software independently being as valued as it was by the job market from 1990 to 2020. That was a fluke caused by a confluence of factors that won't repeat.

The edge of the tech hype has already moved on. We can at best look forward to a normalization to back where things were, remuneration wise, to late 20th century, at a much lower level of demand.

Similarly if they participated in all the early arguments about where your models would be located then they have no idea now that they are fed up with the endless thread of subtle change requests.

Many times plum resume building assignments at big companies go to the best politicians rather than the biggest experts.

In a good market I’d say you should look around. In this market, keep your head down and get some experience.

e.g. I've worked at firms with 300,000 people and 150 people. The smaller company had MORE politics in some ways.

Or as a manager of mine in Hong Kong once said:

"In Chinese there is a saying: if there are 3 people then there will be politics"

I’ve seen what you’ve seen too though: companies that haven’t grown past everyone thinking they report to the CEO turning into gossip factories.

I think you mean non-associativity.

And you can’t ignore that.

This isn't some hypothetical - it happens all the time with LLM's - it isn't some freak accident that isn't probable

I don't think my tech-lead was trying to suggest the floating-point error/non-associativity was the real source.

Yes I would because it causes exponential divergence (P(correct) = (1-e)^n) and doesn't have a widely adopted solution. The major labs have very expensive researchers focused on this specific problem.

There is a paper from Thinking Machines from September around Batch Invariant kernels you should read, it's a good primer on this issue of non-determinism in LLM's, you might learn something from it!

Unfortunately the method has quite a lot of overhead, but promising research all the same.

I dont think this is relevant to the main-point, but it's definitely something I wasn't aware of. I would've thought it might have an impact on like O(100)th token in some negligible way, but glad to learn.

I came to the same observations; lots of experts not much expertise.

I think my wider team are on par with their ability and understanding so we now can sift through the BS a bit easier.

Nod, smile, accept that no one has a clear understanding.

I can tell you how a house is built, that doesn’t make me a builder that makes me informed and opinionated. I can decorate my house however I like but im not a painter/decorator or a tradesman. I can assemble some ikea furniture, but I’m not a carpenter. I’m a consumer and I can tweak something to my liking but I can’t do anything significant.

One can argue that a lot of "building with AI" is commoditized by fine-tuning and RAG libraries or even reduced to prompt engineering. A lot of it is also tricks that might work on one dataset but not others. Putting together libraries fueled by pizza and coke gives an illusion of skill and speed.

Are there grifters who are jumping onto the AI bandwagon? Of course! In spades. Are there also engineers who want to build up their skills and are failing to do so or in the process of doing so? Of course, this happens too! But there are also people who are trying to understand, debug and improve models who are not necessarily "building". After all, the scaling laws paper (the original one) was a result of pure analysis of empirical data.

If you want to know why Hacker News is full of people disappointed or skeptical with AI ask yourself why they put 99.9% of their effort into “zero-shot” when it is clear as day that if you get a few thousand examples and train in that you wipe the floor with “zero-shot”

Agencies are like a production line, they need raw materials coming in; clients with cash, armed with opportunities, scraps of ideas or formed briefs to be worked on. They need this business so they can generate the output and keep the lights on.

AI is everywhere and everything for a lot of people now. You can be sure that Exec’s are asking their teams how are we using AI, how is it helping the business grow etc. However there’s so much AI news, it’s moving so quick and seeping into everything that difficult (from a naïeve client point of view) to know what’s fantasy and what’s reality.

So my perception is… agencies do the sifting and maintain visibility of what is real or not because they have to start drumming up future sales and business, and AI is hot right now.

Perhaps they have some training in CoPilot etc, or with some experience of creating a model, maybe they have integrated something small with something big. It may even be that being ann angency they have a more open way of working that a corporate does, and that’s the sell.

Anyway, the sales teams will proclaim themselves experts because they have to sell.

And then there’s of course career climbers playing politics and people getting into the field because of interest or resume building.

He didn't say what the app would do or offer, he just said "an AI medical app hasn't been done before", he just wanted it to be "AI"

Before that I had people ask me if I could help them make a website, or an app, and they wouldn't say what the app/website would actually do, just that it would be nice to have one.

If your current job is unstable because nobody there knows what they're doing, it's good to have a fallback.

But I know enough to know neither AI nor machine learning are subfields of the other. AI just developed out of the very earliest days of electronic computing as an expression of the desire to get intelligent behavior out of computers by any means possible. Machine learning arose from the desire to express functions in which we know the inputs and outputs but not the form of the function itself, so we use various estimation methods that can be learned from the data itself. A whole lot of overlap and parallel efforts simultaneously developed the same or similar techniques between computer scientists and software engineers on the one side and statisticians and applied mathematicians on the other side. It seemed to have turned out that statistical methods generally seem to provide the best algorithms for machine learning, and machine learning has seemed to provide the best algorithms to get intelligent behavior out of computers.

So they've kind of grown together, stats, automated learning, and AI, but they're still distinct things that developed independently of one another and still exist independently of one another.

This is putting aside all the various "big data" technologies and efforts that grew out of the 2007 or so era of collecting enormous amounts of user or machine-generated data that required new tech to store, query, and new ways to perform parallel batch processing often married to the storage and query tech, all of which was necessary for and enabled statistical machine learning to become as successful as it has become, but is completely separate from the mathematical and algorithmic discipline itself.

Even the guys I named above are probably not really experts in all of these things separately. As with anything, it takes a village.

That's what I tried to explain then as well, and i brought up stuff like path-finding algorithms for route-finding (A*/heuristic-search) as an more old-school AI part, which didn't really land I think.

> Not really stochastic as far as I know. The whole random seed and temperature thing is a bit of a grey area for my full understanding. Let alone the topk, top p, etc. I often just accept what's recommended from the model folks.

I mean LLMs are often treated in stochastic nature, but like ML models aren't usually? Like maybe you have some dropout, but that's usually left out during inference AFAIK. I dont think a Resnet or YOLO is very stochastic, but maybe someone can correct me.

> AI for the most part has been out a couple years.

With this you just mean LLMs right? Because I understand AI to be way more then just LLMs & ML

so the order in which floating-point additions happen is not fixed because of how threads are scheduled, how reductions are structured (tree reduction vs warp shuffle vs block reduction)

Floating-point addition is not associative (because of rounding), so: - (a + b) + c can differ slightly from a + (b + c). - Different execution orders → slightly different results → tiny changes in logits → occasionally different argmax token.

But at that point i feel like we are getting close to "everything that isn't a perfect Turing-machine is somewhat-stochastic" ;)

Edit: someone corrected me above, it does seem to matter more then I thought

if you llm agent takes different decisions from the same prompt, then you have to deal with it

1) your benchmarks become stochastic so you need multiple samples to get confidence for your AB testing

2) if your system assumes at least once completion you have to implement single record and replay so you dont get multiple rollout of with different actions

This "senior dev" has it all mixed up and is incorrect.

"AI" is all encompassing umbrella term that includes other fields of "AI" such as the very old GOFAI (good old fashioned AI) which is rule-based, machine learning (statistical, bayesian) methods, and neural networks which deep learning and more recently generative AI (which ChatGPT) uses.

More accurately, it is neural networks which are more "stochastic" with their predictions and decisions, not just transformer models which ChatGPT is based on.

> Am I just too junior/naive to get this or am I cooked?

Quite frankly, the entire team (except you) is cooked, as you have realized what you don't know.

And also just to nitpick/joke:

> More accurately, it is neural networks which are more "stochastic" with their predictions and decisions <...>

I would defend NNs to not even be necessarily stochastic. I had to handwrite weights for NNs in atleast two exams, to fit XOR for example ;)

Early on in my career I was hyper-fixated on building features correctly at this particular company, according to what I thought was a proper way to build websites. I was probably right, but my job wasn't to be right, my job was to get things done in a certain period of time according to whatever people who controlled the money at the company thought was important, not what a nerd would necessarily care about.

When you're in school or just graduated, you're basically qualified to start learning (outside academia) and it's important to pay attention to what other people value, then do your best within that until you have the power to determine what's worth valuing.

Almost nobody knows what they're doing

But since I wasn't really around for either of those ... ¯\_ (ツ)_/¯