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The Future of AI Software Development

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Re: The Future of AI Software Development

#21

Post by Quothling »

adregan wrote: post #3

In the section on security: > One large enterprise employee commented that they were deliberately slow with AI tech, keeping about a quarter behind the leading edge. “We’re not in the business of avoiding all risks, but we do need to manage them”. I’m unclear how this pattern helps with security vis-à-vis LLMs. It makes sense when talking about software versions, in hoping that any critical bugs are patched, but prom…

I work in a NIS2 regulated sector and I'm not sure we can ever let any AI agent run in anything we do. We have a centralized sollution where people can build their own chatbots with various configurations and cross models. That's in the isolation of the browser though, and while I'm sure employees are putting things into it they shouldn't, at least it's inside our setup and not in whatever chatbot they haven't yet run out of tokens on. Security wise though, I'm not sure how you can meet any form of compliance if you grant AI's access unless you have four eye validation on every single action it takes... which is just never going to happen.

We've experimented with rolling open source models on local hardware, but it's so easy to inject things into them that it's not really going anywhere. It's going to be a massive challenge, because if we don't provide the tools, employees are going to figure out how to do it on their own.

Re: The Future of AI Software Development

#22

Post by deadbabe »

There have been some back of the napkin estimates on what AI could cost from the major platforms once no longer subsidized. It does not look good, as there is a minimum of a 12x increase in costs.

Local or self hosted LLMs will ultimately be the future. Start learning how to build up your own AI stack and use it day to day. Hopefully hardware catches up so eventually running LLMs on device is the norm.

Re: The Future of AI Software Development

#23

Post by neebz »

simonw wrote: post #9

> LLMs are eating specialty skills. There will be less use of specialist front-end and back-end developers as the LLM-driving skills become more important than the details of platform usage. Will this lead to a greater recognition of the role of Expert Generalists? Or will the ability of LLMs to write lots of code mean they code around the silos rather than eliminating them? This is one of the most interesting questi…

I've faced the same but my conclusion is the opposite.

In the past 6 months, all my code has been written by claude code and gemini cli. I have written code backend, frontend, infrastructure and iOS. Considering my career trajectory all of this was impossible a couple of years ago.

But the technical debt has been enormous. And I'll be honest, my understanding of these technologies hasn't been 'expert' level. I'm 100% sure any experienced dev could go through my code and may think it's a load of crap requiring serious re-architecture.

It works (that's great!) but the 'software engineering' side of things is still subpar.

Re: The Future of AI Software Development

#24

Post by petcat »

simonw wrote: post #9

> LLMs are eating specialty skills. There will be less use of specialist front-end and back-end developers as the LLM-driving skills become more important than the details of platform usage. Will this lead to a greater recognition of the role of Expert Generalists? Or will the ability of LLMs to write lots of code mean they code around the silos rather than eliminating them? This is one of the most interesting questi…

Code is, I think, rapidly becoming a commodity. It used to be that the code itself was what was valuable (Microsoft MS-DOS vs. the IBM PC hardware). And it has stayed that way for a long time.

FOSS meant that the cost of building on reusable components was nearly zero. Large public clouds meant the cost of running code was negligible. And now the model providers (Anthropic, Google, OpenAI) means that the cost of producing the code is relatively small. When the marginal cost of producing code approaches zero, we start optimizing for all the things around it. Code is now like steel. It's somewhat valuable by itself, but we don't need the town blacksmith to make us things anymore.

What is still valuable is the intuition to know what to build, and when to build it. That's the je ne sais quoi still left in our profession.

Re: The Future of AI Software Development

#26

Post by HPsquared »

petcat wrote: post #24
simonw wrote: post #9

> LLMs are eating specialty skills. There will be less use of specialist front-end and back-end developers as the LLM-driving skills become more important than the details of platform usage. Will this lead to a greater recognition of the role of Expert Generalists? Or will the ability of LLMs to write lots of code mean they code around the silos rather than eliminating them? This is one of the most interesting questi…

Code is, I think, rapidly becoming a commodity. It used to be that the code itself was what was valuable (Microsoft MS-DOS vs. the IBM PC hardware). And it has stayed that way for a long time. FOSS meant that the cost of building on reusable components was nearly zero. Large public clouds meant the cost of running code was negligible. And now the model providers (Anthropic, Google, OpenAI) means that the cost of prod…

Like column inches in a newspaper. But some news is important and that's the editor's job to decide.

Re: The Future of AI Software Development

#27

Post by fullstackchris »

embedding-shape wrote: post #19
chadash wrote: post #11

> Will LLMs be cheaper than humans once the subsidies for tokens go away? At this point we have little visibility to what the true cost of tokens is now, let alone what it will be in a few years time. It could be so cheap that we don’t care how many tokens we send to LLMs, or it could be high enough that we have to be very careful. We do have some idea. Kimi K2 is a relatively high performing open source model. Peopl…

> a near-frontier model Is Kimi K2 near-frontier though? At least when run in an agent harness, and for general coding questions, it seems pretty far from it. I know what the benchmarks say, they always say it's great and close to frontier models, but is this other's impression in practice? Maybe my prompting style works best with GPT-type models, but I'm just not seeing that for the type of engineering work I do, wh…

regardless its been 3 years since the release of chatgpt. literally 3. imagine in just 5 more years how much low hanging (or even big breakthroughs) will get into the pricing, things like quantization, etc. no doubt in my mind the question of "price per token" will head towards 0

Re: The Future of AI Software Development

#28

Post by mjr00 »

AutumnsGarden wrote: post #20
simonw wrote: post #9

> LLMs are eating specialty skills. There will be less use of specialist front-end and back-end developers as the LLM-driving skills become more important than the details of platform usage. Will this lead to a greater recognition of the role of Expert Generalists? Or will the ability of LLMs to write lots of code mean they code around the silos rather than eliminating them? This is one of the most interesting questi…

I’ve become the same way. Instead of specializing in the unique implementations, I’ve leaned more into planning everything out even more completely and writing skills backed by industry standards and other developer’s best practices (also including LOTS of anti-patterns). My work flow has improved dramatically since then, but I do worry that I am not developing the skills to properly _debug_ these implementations, as…

IMO debugging is a separate skill from development anyway. I've known plenty of developers in my career who were fully capable of writing and shipping code, especially the kind of boilerplate widgets/RPCs that LLMs excel at generating, yet if a bug happened their approach was largely just changing somewhat random stuff to see if it worked rather than anything methodical.

If you want to get/stay good at debugging--again IMO--it's more important to be involved in operations, where shit goes wrong in the real world because you're dealing with real invalid data that causes problems like poison pill messages stuck in a message queue, real hardware failures causing services to crash, real network problems like latency and timeouts that cause services which work in the happy path to crumble under pressure. Not only does this instil a more methodical mentality in you, it also makes you a better developer because you think about more classes of potential problems and how to handle them.

Re: The Future of AI Software Development

#29

Post by newsoftheday »

chadash wrote: post #11

> Will LLMs be cheaper than humans once the subsidies for tokens go away? At this point we have little visibility to what the true cost of tokens is now, let alone what it will be in a few years time. It could be so cheap that we don’t care how many tokens we send to LLMs, or it could be high enough that we have to be very careful. We do have some idea. Kimi K2 is a relatively high performing open source model. Peopl…

> a cost that a (somewhat wealthy) hobbyist can afford

$20,000 is a lot to drop on a hobby. We're probably talking less than 10%, maybe less than 5% of all hobbyists could afford that.

Re: The Future of AI Software Development

#30

Post by consp »

chadash wrote: post #11

> Will LLMs be cheaper than humans once the subsidies for tokens go away? At this point we have little visibility to what the true cost of tokens is now, let alone what it will be in a few years time. It could be so cheap that we don’t care how many tokens we send to LLMs, or it could be high enough that we have to be very careful. We do have some idea. Kimi K2 is a relatively high performing open source model. Peopl…

20k for such a setup for a hobbyist? You can leave the somewhat away and go into sub 1% region globally. A kw of power is still 2k/year at least for me, not that I expect it will run continuously but still not negligible if you can do with 100-200 a year on cheap subscriptions.
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