Join the discussion
Write your take first — we'll ask for email only when you're ready to publish.
- Hacker News
- It took me a while to realize that the cringe AI hype bro is just wearing a tie this time. Unsubstantiated fluff with anonymous sources that wants to disguise itself as legitimate research.
Maybe at least these charts are based on real data - albeit self-reported by AI startups likely talking to their investors.
Either way it's useless unless hopping on this train is a past time of yours or you make a living taking investors - to poor to fund an OpenAI, but just rich enough to fund someone eating OpenAI's scraps - for their money.
It has been how many years of people trying to create businesses around chatgpt prompts? I think we need to bring bullying back. This is getting ridiculous.
by chmod775 - All of these are temporal. It's the only thing we can be sure of. Very limited value in these "lessons" 6 months ahead.by arisAlexis
- Do you mean “temporary”?by layer8
- I know some people in business who won’t even delegate to very competent humans who have worked closely alongside them for years. And now we have to believe that letting AI agents autonomously roam without oversight is going to be acceptable to people like that.by gizajob
- I talked to some enterprises and saw similar patterns:
1. Agentic AI systems are hard to measure and evaluate methodologically.
2. Quote from Salesforce analyst day: "it's been so easy to build a killer demo, but why has it been so hard to get agents that actually deliver the goods.”
3. Unfortunately, small errors tend to compound over time, which means most systems need a human in the loop as of 2025.
4. A lot of enterprise buyers feel the huge potential (and FOMO), yet ROI is still unclear as of 2025. MIT report "State of AI in business 2025": Despite $30–40 billion in enterprise investment into GenAI, 95% of organizations are not seeing profit and loss impact.
by jakozaur - The Salesforce analyst quote is ChatGPT, though.
- "Working with AI is like being a mentor for a monkey pissing into its own mouth. Using agentic pipelines is doing the same, but now it's 5 monkeys pissing into each others' mouths in a Roman fountain kind of way."
Except it's worse than that because we'll all end up having to do it anyway, because the overall velocity of emitted working code will be faster, and productivity > all.
by pmarreck - > The main blockers aren’t technical. Most founders pointed to workflow integration, employee trust, and data privacy as the toughest challenges — not model performance.
These, outside of employee resistance are technical problems. The insistence they aren’t seems to be the root of the misunderstanding around these tools. The reality is that “computers that speak English” are, at face value, incredibly impressive. But there’s nothing inherent to said systems that makes them easier to integrate with than computers which speak C. In fact I’d argue it’s harder because natural languages are significantly less precise.
Communication and integration is incredibly challenging because you’re trying to transfer states between systems. When you let “the machine carry a larger share of the burden,” as Dijkstra described of the presumed benefit of natural language programming but actual downside[0], you’re also forfeiting a large amount of control. It is for the same reason that word problems are considered more challenging than equations in math class. With natural languages the states being communicated are much less clear than with formal languages and much of the burden assumed to be transferred to the machine is returned in the form of an increase in required specificity and preciseness of which formal languages already solve for.
None of this is to say these tools aren’t useful nor that they cannot be deployed successfully. It is instead to say that the seduction of computers which speak English is more exactly that. These tools are incredibly easy to use to impress, and much more challenging to use to extract value.
0: https://www.cs.utexas.edu/~EWD/transcriptions/EWD06xx/EWD667...
by roxolotl - I think I disagree here.
The integration parts aren’t natural language issues but connecting systems and how to put these things in your workflow.
For example. I have a bunch of off the cuff questions and problems and tasks. I want to have these in one place and have that trigger a conversation with ChatGPT, which shows the results in the first place but can be continued easily.
Before it was added the other week, I could track issues in linear and I could have codex write the code to solve them but only by manually moving text from one place to another, manually moving the tickets, checking on progress, click g buttons to open prs - all of that is integration hassle but none is about the model itself. I think now with GitHub copilot I could just assign a task.
by IanCal - "AI agents can reason toward a goal, make dynamic decisions on the fly, and learn or improve over time"
I've yet to see an 'agentic' setup that actually learns or improves over time. There are many techniques for this, but I don't see them used.
- > There are many techniques for this, but I don't see them used
Why do you think that is?
by htrp - I've seen companies including my own pouring lots of money into AI. Outside of "replacing developers", I am genuinely curious what have people done that's actually useful?
We've got a sort of "business intelligence" AI they poured a lot of time and money into, and I don't think anyone really uses it because it makes stuff up.
I'm sure there are things. I just haven't seen them. I would love to hear concrete examples.
The cynic in me says I wouldn't want something with the error aptitude and truth telling of a small child taking any sort of important action on my behalf.
by donatj - We had a lack of (digital images of) training cases in emergency surgery. You’d prefer to give ML experience with many rare cases, but must resort to style transfer. Humans can do this, but variously problematic and you’re taking them away from necessary work.by sigwinch
- Two use cases with my corpo are:
1) Enhanced User guides/manuals.
We sell some very complicated and expensive instruments. As such, making them work is quite hard. One of our biggest expenses is our engineers that go out, physically, to help customers. Company policy is that the first visit is always free. These customers can be very remote (Deep sea oil platforms, Australian outback, quite nice ski country ;) , etc). Often their issue is simple but they can also be very complex. We have phone trees, email, texts, iridium phones, etc. to talk customers through things to avoid these first visits and then hep them afterwards. So adding in AI chatbots is a natural way to help out. People don't feel quite the same 'shame' in asking really dumb questions to a chatbot that they do to a real person. So, to make these chatbots smarter, we use some of this AI mumbo-jumbo (RAG), to help them out. So far, it seems successful and the customer and engineers like the enhanced/AI manuals.
2) Making said manuals
We support 35 languages and many regulatory environments. Our instruments are all compliant with whatever version of a government agency you've got (modulo a lot of time, money, ITAR regulations). As such, making all that paper (manuals, compliance docs, contracts, etc) takes a lot of time and effort and has to pass the legal tests too. So AI is really helpful with it. Most of the work for these large stacks of paper is essentially boilerplate, but all subtly different so that literal copy-pasting doesn't get you quite that far. AI systems have been able to, last I checked, get that team about 5x faster, as it cuts out ~85% of the process and drudgery. Since these documents get hauled into courts, they can't just be blindly AI made, and a human always has to go over everything with a sharp eye still, but AI helps out there a bit too. Last lunch I had with them, they were saying that they were actually working on their burn-down charts now and not just going from panic to panic. As in, they could actually do their jobs.
by Balgair - Prediction: Non-determinism will become acceptable in areas we used to expect accuracy.
For example we will accept 'probabilistic bookkeeping' because it's cheaper than requiring ledgers to balance to the penny.
But this leeway won't be equally applied. Powerful institutions like banks will use “probabilistic models” to decide they probably don’t owe you that refund, but if they decide you owe them money, they will still hold you to every cent.
Nondeterminism for the powerful, determinism for everyone else. Yay!
- From what I remember reading on some tutorial about Random Tree classifiers banks on the USA have to justify the specific reasons why a credit was denied, so hence why blackbox models cannot be used for this.by yomismoaqui
- The bookkeeping can often be fully automated, if you care to do so, so there probably isn't a point.
People have used machine learning for fraud detection for a long time at this point. They do tolerate the false positives.
by nitwit005 - Don't forget that this will likely be paired with rubber-stamping one-sided arguments maskerading as quality control processes where some maliciously-biased oversampling of (probably paid-for) good reviews takes place in order to consistently reach the conclusion that there's nothing wrong going on.by rolandog
- Companies can decide today that they don't need near-perfect accuracy in bookkeeping to save a few bucks and no one does that. One of the major factors is regulatory requirements. Even without I'm sure investors would apply a hefty discount to any public company that decided to save a few pennies on accountants while sacrificing accuracy.by skystarman
- A bunch of people are going to become "acceptable risk" and "cost of doing business". Companies will opt to get it 95% right for cheap over getting it 100% right, and for 95% of the population, it will be good enough.by nicbou
- Agree with your prediction in many cases but not bookkeeping. The whole point of bookkeeping is to balance the ledger to the penny so people would toss bookkeeping altogether before accepting a 'probabilistic' output. Agents will be used to accelerate the recon process though, or maybe they will become advanced enough to provide a (correct) deterministic result.by creaghpatr
- We recently spoke with 30+ startup founders and 40+ enterprise practitioners who are building and deploying agentic AI systems across industries like financial services, healthcare, cybersecurity, and developer tooling.
A few patterns emerged that might be relevant to anyone working on applied AI or automation:
- The main blockers aren’t technical. Most founders pointed to workflow integration, employee trust, and data privacy as the toughest challenges — not model performance.
- Incremental deployment beats ambition. Successful teams focus on narrow, verifiable use cases that deliver measurable ROI and build user trust before scaling autonomy.
- Enterprise adoption is uneven. Many companies have “some agents” in production, but most use them with strong human oversight. The fully autonomous cases remain rare.
- Pricing is unresolved. Hybrid models dominate; pure outcome-based pricing is uncommon due to attribution and monitoring challenges.
Infrastructure is mostly homegrown. Over half of surveyed startups build their own agentic stacks, citing limited flexibility in existing frameworks.
The article also includes detailed case studies, commentary on autonomy vs. accuracy trade-offs, and what’s next for ambient and proactive agents.
If you’re building in this space, the full report is free here: https://mmc.vc/research/state-of-agentic-ai-founders-edition...
Would be interested to hear how others on HN are thinking about real-world deployment challenges — especially around trust, evaluation, and scaling agentic systems.
by advikipedia - Honestly, just sad seeing AI posts on HN now.
- > The main blockers aren’t technical. Most founders pointed to workflow integration, employee trust, and data privacy as the toughest challenges — not model performance.
What does that even mean? Are you trying to say that the problem isn’t that the AI models are bad — it’s that it’s hard to get people to use them naturally in their daily work?
by baxtr - Lack of employee trust in these systems is caused by model (under)performance. There's a HUGE disconnect between the C-suite right now and the people on the ground using these models. Anyone who builds something with the models would tell you that they can't be trusted.by woeirua
- Perhaps I simply don't understand what you mean, but it sounds like the first point could be rephrased in some way. To me, workflow integration and data privacy sound very much like technical blockers.by Etheryte
- What you actually need in most business cases is a 100% auditable, explainable and deterministic workflow. While AI is strictly deterministic - it is technically chaotic. Introducing this in large customer pipelines or in intensive data applications means that even if the AI only does something a bit off 99%, 99.9% or 99.99% you will see large spurious error rates in your workflow. Worst of all these will be difficult to explain - or maybe even purposely hidden, as I have seen some agents attempt to do.by reedf1
- How can the agent hide the error?
You log the interaction, you see what happened, no?
by flir - > Introducing this in large customer pipelines or in intensive data applications means that even if the AI only does something a bit off 99%, 99.9% or 99.99% you will see large spurious error rates in your workflow.
You just described how you get your google account locked... :-)
by belter - This comment is a bit strange.
>> While AI is strictly deterministic - it is technically chaotic
AI is neither deterministic nor chaotic. It is nondeterministic because it works based on probability, which means that for open-ended contexts it can be unpredictable. But properly engineered agentic AI workflows can drastically reduce and even completely eliminate the unpredictability. Having proper guardrails such as well-defined prompts, validations and fallbacks in place can help ensure mistakes made by AIs don't result in errors in your system.