AI is getting strong enough that if people give some general direction as well as access to production systems of any kind, things can go badly. It is not true that all implementations of agentic AI requires human intervention for all action.
On a more serious note, they were mostly f*cked by their paas provider imo. Claude will always do dumb shit. Especially if you tell it to not do something... By doing so you generally increase the likelihood of it doing it.
It's even obvious why if you think about it, the pattern of "you had one job, but you failed" or "only this can't happen, it happened!" And all it's other forms is all over literature, online content etc.
But their PaaS provider not scoping permissions properly is the root cause, all things considered. While Claude did cause this issue there, something else would've happened eventually otherwise.
Also, some folks seem to be forgetting the virtues of boring, time-tested platforms & technologies in their rush to embrace the new & shiny & vibe-***ed. & also forgetting to thoroughly read documentation. It’s not terribly surprising to me that an “AI-first” infrastructure company might make these sorts of questionable design decisions.
I’m not sure what the problem is there
Remember that these models are getting better; this means they get trusted with increasingly more important things by the time an error explodes in someone's face.
It would be very bad if the thing which explodes is something you value which was handed off to an AI by someone who incorrectly thought it safe.
AI companies which don't openly report that their AI can make mistakes are being dishonest, and that dishonesty would make this normalization of deviance even more prevelant than it already is.
Further, it’s only a problem to the extent that the downsides or risks are not accounted for which again… is a social problem not a technological problem
This isn’t a problem for organizations that have well aligned incentives across their workflows
A well organized company that has solid incentives is not going to diminish their own capacity by prematurely deploying a technology that is not capable of actually improving
The issue is that 99% of the organizations that people deal with have entirely orthogonal incentives to them. They are then attributing the pain in dealing with that organization to the technology rather than the misaligned incentives
As @TeMPOraL here likes to point out, it can be genuinely fruitful to anthropomorphise AI. I only agree with partially, that this is true for *some* of the failure modes.
> A well organized company that has solid incentives is not going to diminish their own capacity by prematurely deploying a technology that is not capable of actually improving
Sure, but society as a whole doesn't have the right solid incentives to make sure that companies have the right solid incentives to do this. We can tell this quite easily by all the stupid things that get done.
> The issue is that 99% of the organizations that people deal with have entirely orthogonal incentives to them.
This is also fundamentally the AI alignment problem, that all AI are trained on some fitness function which is a proxy for what the trainer wanted, which is a proxy for what incentives their boss gave them, which is a proxy that repeats up to the owners in a capitalist society, which is a proxy for economic growth, which is a proxy for votes in a democracy, which is a proxy for good in a democracy.
I wrote a whole ass paper at the end of 2022 demonstrating that unless we fix society we will deterministically create anti-social AGI because humans do not generate pro-social data.
So that is a structural problem with their data and security management and operations, totally independent of the architecture for doing large scale token inference.
I don't see any stabilizing influences on the horizon, given how much cash is sloshing around in the economy looking for a place to land. Things are going to get weird, stupid, and chaotic, not necessarily in that order.
The risks are similar: No prompts/data that go in can reliably be kept secret; A sufficiently-motivated stranger can have it send back completely arbitrary results; Some of those results may trigger very bad things depending on how you use or even just display them on your own end.
P.S. This conceptual shortcut doesn't quite capture the dangers of poison data, which could sabotage all instances even when they happen to be hosted by honorable strangers.
If I was a Ph.D. student today, I'd probably do a thesis on cheap verifiers for LLM agents. Since LLM agents are not reliable and therefore not very useful without it, that is a trillion dollar problem.
Once a developer groks that concept, the agents stop being scary and the potential is large.
Not even a few years ago if you introduced a component to a system that would result in non-deterministic output... Hell, a single function... You would be named and shamed for it because it went against every principle you should be learning as a novice writer of software.
I have used the LLM tools, and I see the real-world potential for these things. But how it's all being sold and applied now: it's upside down.
With Lee Zeldin heading the EPA is anyone sure we won't?
Bit flips in memory are super common. Even CPUs sometimes output the wrong answer for calculations because of random chance. Network errors are common, at scale you'll see data corruption across a LAN often enough that you'll quickly implement application level retries because somehow the network level stuff still lets errors through.
Some memory chips are slightly out of timing spec. This manifests itself as random crashes, maybe one every few weeks. You need really damn good telemetry to even figure out what is going on.
Compilers do indeed have bugs. Native developers working in old hairy code bases will confirm, often with stories of weeks spent debugging what the hell was going on before someone figured out the compiler was outputting incorrect code.
It is just that the randomness has been so rare, or the effects so minor, that it has all been, mostly, an inconvenience. It worries people working in aviation or medical equipment, but otherwise people accept the need for an occasional reboot or they don't worry about a few pixels in a rendered frame being the wrong color.
LLMs are uncertainty amplifiers. Accept a lot of randomness and in return you get a tool that was pure sci-fi bullshit 10 years ago. Hell when reading science fiction now days I am literally going "well we have that now, and that, oh yeah we got that working, and I think I just saw a paper on that last week."
This is a disingenuous comparison.
First of all, what you're talking about is nondeterminism at the hardware level, subverting the software, which is, on an ideal/theoretical computer, fully deterministic (except in ways that we specifically tell it not to be, through the use of PRNGs or real entropy sources).
Second of all, the frequency with which traditional programs are nondeterministic in this manner is multiple orders of magnitude less than the frequency of nondeterminism in LLMs. (Frankly, I'd put that latter number at 1.)
This is part of a class of bullshit and weaselly replies that I've seen attempting to defend LLMs over the years, where the LLMs' fundamental characteristics are downplayed because whatever they're being compared to occasionally exhibits some similar behavior—regardless of the fact that it's less frequent, more predictable, and more easily mitigated.
Malloc and free were never deterministic outside of the simplest systems.
The second we accepted OS preemption we gave up deterministic performance.
Good teams freeze their build tools at a specific version because even minor revs of compilers can change behavior.
I've used way too many schema generator tools that I'd describe as "wishfully deterministic".
Heuristics have been used for years in computer science, resulting in surprising behavior. My point is that if we ramp up the rate of WTF we are willing to tolerate, the power of the systems we can build increases drastically.
> Second of all, the frequency with which traditional programs are nondeterministic in this manner is multiple orders of magnitude less than the frequency of nondeterminism in LLMs. (Frankly, I'd put that latter number at 1.)
Building a RAG lookup system that takes in questions from the user, looks up answers in a doc, and returns results, can be built with reliability damn near approaching 99.99%.
I have seen code generation harnesses that also dramatically reduce non-determinism of LLM generated code, but that will continue to be a hard problem.
My phone camera applies non-deterministic optimizations to images I take, and has done so for years now.
GPS is non-deterministic (noisy), we smooth over the issues. GPS routing is also iffy, but again we smooth over the issues.
The question is can useful products be made with a technology. You can shove enough guardrails on an LLM interface to make it useful. That much is clear. I derive massive value from LLMs and other transformer based systems literally everyday. From the modern speech transcription systems, that are damn near magic compare to what we had a few years back, to image recognition, to natural language interfaces to search over company documents.
If we completely discard coding agents, LLMs are still an insanely impactful technology.
Those guardrails add costs, and latency. For some scenarios that is fine, but for others it isn't. Chat bot support agents implemented by the lowest bidder don't have any attempt at guardrails. Better systems are better built.
I agree that current LLMs all suffer from the problem that the control messages are intermixed with data, that is a crappy problem that the industry has known is a bad pattern for literally decades (since the 70s, 80s?). It seems like an intractable flaw in the systems.
But that doesn't make the system unusable any more than the thousand other protocols suffering from the same flaw are unusable.
Neither is stochastic gradient descent which is the cause of the LLM problem. Nor is UDP, the network protocol that powers video calls, live streaming, and online gaming.
It's not obvious if that's the case with llm based development. Of course you could 'use llms until things get crazy then stop' but that doesn't seem part of the zeitgeist.
Harnesses are coming online now that are designed to reduce failure rates and improve code quality. Systems that designate sub-agents that handle specific tasks, that put quality gates in place, that enforce code quality checks.
One system I saw (sadly not open source yet) spends ~70% of tokens on review and quality. I'll admit the current business model of Anthropic/OpenAI would be very unfriendly to that way of working. There is going to be some conflict popping up there. Maybe open weight models will save us, maybe not.
If Moore's Law had iterated once or twice more we wouldn't be having this conversation. We'd all be running open weight models on our 64GB+ VRAM video cards at home and most of these discussions would be moot. AI company valuations would be a fraction of what they are.
put this exact value inside this exact register at the right concurrent time and all the tedious exactness that C required
into now:
"pretty please can you not do that and fix the bug somewhere a different way"
PhD thesis are for (ideally) setting up a new world standard in some research area (at the end, you build your PhD thesis out of the deep emotional shards of this completely destroyed life dream), and not for some personal self-discovery project of which you hope that it will turn you into the popular kid on the block.
No decent PhD advisor would let their PhD student base their PhD thesis on such well-known concepts: a doctoral study programme is a journey into something never-seen-before (with a very high likelihood of faling and shattering your life). Anything else is failure.
(Obvious exception: either he or the PhD student can convince the other one that there could be something really, really deep in, say, "superscalar out-of-order execution", "stochastic gradient descent" or UDP be found that generations of researchers overlooked, and which once discovered might necessitate rewriting all the standard textbooks about this topic).
I'm still not sure if HAL-9000 was supposed to be conscious or just an interesting plot device with a persona as superficial as LLMs are dismissed as today.
LLMs could definitely play the part of all three of your examples, given the flaws they showed on-screen. Could even do a decent approximation of Data (though perhaps not Lore without some jailbreaking).
Still weird that even the best of them isn't really ready to be KITT.
Have you ever seen Claude Code launch a subagent? You've used it, right? You've seen it launch a subagent to do work? You understand that that is, in fact, Claude Code running itself, right?
They create the illusion of being able to make decisions but they are always just following a simple template.They do not consider nuance, they cannot judge between two difficult options in a real sense.
Which is why they can delete prod databases and why they cannot do expert level work
Well this is just factually incorrect considering they are currently on par with grad students in some areas of mathematics.
Even then, they are most effective in assisting and are not able to produce results independently. If you have proof otherwise I would love to read up on it
With humans, you can kind of interview/select for a more normalized distribution of outcomes, with outliers being less probable, but not impossible.
The key break here is the lack of predictability and I think it's important that we don't get too starry eyed and accept that that might be a weakness - not a strength.
AI in the hands of an expert operator is an exoskeleton. AI left alone is a stooge.
Nobody has built an all-AI operator capable of self-direction and choices superior to a human expert. When that happens, you'd better have your debts paid and bunker stocked.
We haven't seen any signs of this yet. I'm totally open to the idea of that happening in the short term (within 5 years), but I'm pessimistic it'll happen so quickly. It seems as though there are major missing pieces of the puzzle.
For now, AI is an exoskeleton. If you don't know how to pilot it, or if you turn the autopilot on and leave it alone, you're creating a mess.
This is still an AI maximalist perspective. One expert with AI tools can outperform multiple experts without AI assistance. It's just got a much longer time horizon on us being wholly replaced.
They're tool calls. Claude Code provides a tool that lets the model say effectively:
run_in_subagent("Figure out where JWTs are created and report back")
The current frontier models are all capable of "prompting themselves" in this way, but it's really just a parlor trick to help avoid burning more tokens in the top context window.It's a really useful parlor trick, but I don't think it tells us anything profound.
The OP says AI requires human interaction to work. This simply isn't true. You know yourself that as agents get more reliable you can delegate more to them, including having them launch more subagents, thereby getting more work done, with fewer and fewer humans. The unlock is the Task tool, but the power comes from the smarter and smarter models actually being able to delegate hierarchical tasks well!
The only reason to launch subagents is to avoid poisoning the LLM's already small context window with unrelated tokens.
It doesn't make the LLM smarter or more capable.
Do you think this means "Build a car" can be accomplished just because an LLM can send a prompt to another LLM who reports back a response?
If that is software running itself, then an if statement that spawns a process conditionally is running itself.