Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by pama on Friday September 18, 2026 @08:20PM
(#49762023)
Having worked with people doing bringup of specialized chips, I am awed at how the world has changed.
> When the first chips came back from the foundry in May, the team pointed its internal AI models at designing software to run benchmarks such as SemiAnalysis’s InferenceX. On DeepSeek’s multi-head latent attention kernel benchmark, performance climbed from 0.31 percent of the theoretical ceiling (set by the chip’s compute and memory bandwidth) to 88.94 percent in roughly 40 hours. Ho says this result is repeatable, so the time between when foundries deliver the first chips and when production ramps up can be reduced. “All our schedule assumptions are going to be based on the fact we have this capability now,” he says.
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by saidnooneever on Saturday September 19, 2026 @08:18AM
(#49765947)
cant wait for no one to really know whats in chips i mean, even intel hardly knows what all their reserved mem ranges are for. who will decap the chip and see if the docs were right? xD
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by RussianBot9580 on Saturday September 19, 2026 @09:05AM
(#49766273)
Haha - understood. Good one!
They'd write a limited test for a feature based on an ask from the software team garbled by a five layer game of telephone. Claim that the module passed validation. A few months later the software folks would have to pull a few all nighters to figure out how to work around the resulting turd during bringup.
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by brookst on Saturday September 19, 2026 @11:42AM
(#49767476)
Yeah. Just spent an hour planning a closed-loop vision-based extrusion modulation system for 3d printing, with extensive telemetry and offline processing to iterate on the realtime system. Great, like I need another side project.
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by Eridrus on Saturday September 19, 2026 @01:40PM
(#49768584)
I had this same thought and think this is a generally interesting direction, but I think we're in a bit of a weird spot where the compute heavy stuff is on GPUs already and most infra stuff is not compute bound (it's often I/O bound or memory bound in some way).
It doesn't help that FPGAs are not made at the same scale as CPUs so don't benefit from the economies of scale.
I'm super curious if you have thoughts on specific pieces of software that would be economically better because I've thought about this in my niche and sort of come to the conclusion that it won't help.
I do think things like SIMD in CPUs will get more use and maybe we will get more difficult to program for CPU features, but I haven't found a use case where off the shelf FPGA components would help with typical software.
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by brookst on Saturday September 19, 2026 @06:58PM
(#49770820)
I’m looking at realtime mechanical processes, like shaping extrusion beads from a clay 3d printer. Clay is heterogenous and pressure takes time, so hand tuning is never just right. But put an fpga with vision processing? Seems promising, with millisecond-level latency that I’d never get pushing to a remote system for processing.
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by Eridrus on Sunday September 20, 2026 @03:23PM
(#49779106)
Unclear that this sort of thing wouldn't already be quite well served by the existing gpu/npu hardware optimized for neural nets. If you're doing traditional CV, you can run it on the CPU just fine.
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by gregsadetsky on Saturday September 19, 2026 @02:49PM
(#49769173)
I’m also very curious! I got a pair of icebreaker boards [0] and they’ve been great to toy around with.
The tooling is open source, and Fable in a loop - especially when paired with a digital scope that Fable interfaces with (the Saleae’s [1] are great) - gives you a level of verifiability that feels like beyond what software typically gives you. ie it feels more like Lean than code with tests.
I had ai implement a few toy circuits (sha hashing, 8088 emulation, a tiny llm) but yeah. Still looking for fun applications.
There have been a few recent fpga threads on hn, check them out. [2][3]
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by nz on Saturday September 19, 2026 @06:03PM
(#49770485)
Many languages can compile a subset of their code to FPGA HDLs. Back in the 80s Harel's group had statecharts that were compilable to C, C++, and FPGA HDLs. Not sure that LLMs brings anything substantially new to this.
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by UncleOxidant on Monday September 21, 2026 @12:57PM
(#49789932)
I've got some Tang nano and Tang primer boards (9k, 20k and 25k LUTs). The 9K has a cute little SPI LCD display. Claude has been able to interface with the boards via USB (it setup a UART on the FPGA to do so). We've gotten a GoL as well as a Pong game (where paddles are controlled by Tsetlin machines) and a MicroCNN that does MNIST classification. For the latter we were having trouble getting correct answers back from the CNN (accuracy was very poor which didn't match simulation). Claude created an on-chip logic analyzer and doggedly troubleshot the problem (turned out to be some kind of problem in the yosys synthesis tool - we had to use -noalu to work around). Anyway, I was blown away by this. Claude was communicating with the FPGA board and analyzing the data coming back from the internal logic analyzer.
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by Lramseyer on Friday September 18, 2026 @08:10PM
(#49761945)
Production grade CPU design is more than just the RTL (the source code.) To achieve the performance numbers that these companies get, you have to do a ton of optimization in your physical design to achieve the power/performance/area (PPA) metrics that make these products competitive. LLMs are not suitable for that kind of work.
There are people working on PPA optimization and trying to shake up how things are done, just not with LLMs.
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by btown on Friday September 18, 2026 @08:27PM
(#49762075)
Something that I think is fascinating, though, is that labs are no longer beholden to the limitations of commercial design software. Want to replace your simulator and optimizer with a fully custom verifiable stack of Lean proofs of optimality and correctness? Just throw your unlimited token budget at it.
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by xpct on Friday September 18, 2026 @09:07PM
(#49762343)
I don't work in the business, but my understanding was that even with these companies' budgets, it's still too expensive to do any kind of verified performance optimality.
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by Systemerror7A69 on Saturday September 19, 2026 @03:26AM
(#49764213)
This is just speculation on my part, but LLMs work best when they get immediate, verifiable feedback on their task, and the kind of physical optimizations they mean might not give that to LLMs.
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by amelius on Saturday September 19, 2026 @06:38AM
(#49765281)
Yes, they are, but the most important subtasks of designing a CPU are not physics related. They are picking the right parameters for things like: how wide do I make this bus, how many registers do I put in the register file, how large do I make this cache, how deep do I make this pipeline, etc., etc. To find optimal parameters requires a lot of simulations, and humans do this, but LLMs could do them just as well and maybe better because they excel at tedious work.
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by TeMPOraL on Saturday September 19, 2026 @09:55AM
(#49766632)
The right way is to throw LLMs at building tools that reframe the problem into a shape LLMs are good at navigating, and then have LLMs use those tools to solve it.
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by Lramseyer on Monday September 21, 2026 @01:35PM
(#49790484)
Yes, but that's only the first part of it. The LLM can create 100 good, working, and verified designs. You still need to lay them out and optimize the physical designs. That kind of optimization is not something language models are good at. You need something fundamentally different.
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by pixl97 on Friday September 18, 2026 @07:34PM
(#49761684)
I mean you can design anything without a license. Selling it is where the problems come up. Even then there are likely places in China that would still make it for you.
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by xpct on Friday September 18, 2026 @09:15PM
(#49762382)
Isn't that weird? The full knowledge of how to make such chips may one day be accessible to anyone, yet only the entrenched companies will remain the makers.
If we imagine machines being able to do the full process end-to-end, and the quality of that process only dependent on capital spent on tokens, I don't see how new companies could ever enter the market.
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by nullc on Friday September 18, 2026 @09:43PM
(#49762507)
And be super-bankrupted by patent litigation from Apple. I don't think they're worried.
After all, they successfully threatened Adobe with spurious patent litigation unless they joined w/ apple in illegally fixing wages.
You don't think a criminal like apple would absolutely decimate any competition given the opportunity? They didn't hold back when it was a unambiguous crime, they surely wouldn't if it was merely bad for the world.
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by zdragnar on Saturday September 19, 2026 @09:39AM
(#49766522)
Just a guess, but that case is going to take forever to get through court. The judge recently told both sides to narrow discovery requests, and next month will be another hearing on further discovery disputes.
I'd be surprised if there was any meaningful progress at all in the case before 2027.
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by muchdoubt on Friday September 18, 2026 @08:46PM
(#49762209)
Seems pretty obvious now that OpenAI is just hyping their models in order to get companies (in this case, chip developers) to use their products in order to learn from their (exfiltrated) IP. Any corporation would be foolish to use any of their or Microsoft’s products, particularly those with valuable IP. There’s nothing in the article that says AI did anything creative but rather that it was used for software development within the overall project. Clear misleading title. Suggest to mark this as clickbait.
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by brookst on Saturday September 19, 2026 @10:00AM
(#49766667)
Isn’t that an extremely convoluted path to a goal?
If the goal is getting chip companies to user non-ZDR AI to steal their stuff, why not just have an account exec offer them a massive discount?
Creating PR hype so employees of chip companies read HN and lobby their execs to use AI to get them submitting proprietary information is the Rube Goldberg version of business strategy.
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by muchdoubt on Saturday September 19, 2026 @03:41PM
(#49769546)
Doesn’t seem that convoluted to me. These chip companies are companies that have massive budgets, so a massive discount likely doesn’t matter as much as you believe. The clickbait propaganda route is what it appears that the AI companies are trying.
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by mathisfun123 on Friday September 18, 2026 @09:49PM
(#49762536)
You people are so weird - gossiping on a site that literally reported the news as it broke smh. It's like sitting in the back of class gossiping about the popular kids.
Newsflash that lawsuit is about product designs not accelerator ASICs. And there wouldn't be anything to steal because Apple doesn't have any DC class accelerators.
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by TeMPOraL on Saturday September 19, 2026 @09:50AM
(#49766601)
"Stealing" changes definitions every few years now, most recently with the mainstream suddenly deciding RIAA and news publishers are no longer the scum of the Earth but their new best friends, and reversing previous definition to now include IP transgression as theft, just so they can say AI companies are stealing shit.
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by brookst on Saturday September 19, 2026 @10:02AM
(#49766681)
Yes, but the quote about art has nothing to do with that.
The quote says that many artists borrow, meaning everyone still knows who did the original work and the artist is just riffing on it. But great artists transform the work so completely that it becomes theirs.
I also remember the hang-wringing about running out of new datasets to train on. Now it appears humans are always generating more data. It's just not as cheap to acquire as legacy data? Meta has to give a deep discount on their API prices to entice people.
I thought back then that humans had a few more breakthroughs in them as meaningful as the seminal Attention is all you need paper. Enough to 100x the capabilities of LLMs back then (10x the smarts and 10x the speed simultaneously).
RSI with a 20 month turnaround for a chip to be made is not exactly breakneck speed though. Physical manufacturing and logistical constraints are going to be and remain a hard obstacle to that process for the foreseeable future.
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by red75prime on Saturday September 19, 2026 @12:50AM
(#49763409)
> I remember the paper proving that hallucinations could never be fully solved back in 2024
The papers that use the halting problem or the Gödel's incompleteness theorem to prove something about LLMs are dime a dozen. The problem is they prove their results for any computable system. You need to also believe that the human brain contains "magic" to think that humans are exempt.
I believe I've said the same at the time this paper was published. There is no need for hindsight to notice the problem.
The required amount of compute and training data and whether the existing training methods were up to the task had the real potential to be show stoppers though.
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by CuriouslyC on Saturday September 19, 2026 @08:27AM
(#49766013)
Hallucination is "unsolvable" in the sense that there will always be a non-zero probability of occurrence. Anti-AI folks have ignorantly painted this as the models being fundamentally unreliable, but you also have a non-zero probability of being struck by lightning or eaten by a shark.
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by jimmySixDOF on Saturday September 19, 2026 @12:22AM
(#49763289)
IEEE Spectrum is such a good publication. Early in my career I worked at a place where the magazine would be passed around every month with a coversheet listing all us engineers we had to pass it around and sign we had read it. Been a while since I visited the website but love what they did with it.
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by Kwpolska on Saturday September 19, 2026 @02:39AM
(#49763981)
Every time their content appears here, it's a very shallow analysis written for a barely technical audience. And this article is no different, it's just "slop machine wrote verilog; all the hard bits were done by Broadcom, who have access to public AI models (we didn't talk to them and don't know if they used them, but ClosedAI wants us to think they did)"
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by globnomulous on Saturday September 19, 2026 @12:58AM
(#49763445)
> Jalapeño can reduce end-to-end latency (the time between prompt to last token) by up to 3.6 times
I'm never sure what on earth this kind of impressionistic math is supposed to tell me. Is the comparison between 4.6 and 1.0? 3.6 and 1.0? Clearly the comparison isn't supposed to be 1.0 and -2.6, even though that's what the words literally mean. I can't be the only person who finds this infuriating and distracting. These numbers shouldn't be impressionistic. They should be precise. That this is an article on spectrum.ieee.org makes the imprecision all the stranger. I'd expect their readershipt to care, for instance, about what's even being measured. Is this the geometric mean of something? The arithmetic mean? And what latency has improved?
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by jcheng on Saturday September 19, 2026 @06:42AM
(#49765316)
> Am I missing something?
If you’re sincerely asking…
Mathematically speaking, 18 / 3.6 isn’t “reducing” by 3.6X, it’s “dividing” by 3.6X. Reducing would be 18 - (18 * 3.6), which is obviously wrong. By your formula, “reducing by 50%” would be 18 / 0.5, also obviously wrong.
Yes, people do say things like “reduce by 3.6X” and are understood to mean what you said, but they also say “literally” when they mean “figuratively”. It doesn’t bother me but I can understand why math oriented people would be annoyed, and I personally would never say “reduced by 3.6X”, but instead “reduced by 72.2%”.
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by perching_aix on Saturday September 19, 2026 @08:26AM
(#49766000)
I was sincere, because as you point out, the phrasing is not actually ambiguous here. There is only one way to interpret this that is coherent and sensible. The usual % shenanigans weren't even on the table for me.
Not that I'd have ever seen "reduced by 0.5x" or any other value below 1x, probably for this very reason. What I do see is "reduced to 0.5x", in which case you're supposed to swap the division for multiplication.
Percentages on the other hand are a whole another can of worms, even if these forms are principally interchangeable, and I find them a lot more confusing a lot more often.
Not that this would explain the whole mean/geomean thing.
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by caidan on Saturday September 19, 2026 @03:50AM
(#49764340)
That odor you are detecting is just good old fashioned bullshit, my friend. It’s just that nowadays everything and everyone is covered in it, and we are not supposed to notice. The emperor has no clothes… and is covered in shit.
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by peri-cl on Saturday September 19, 2026 @06:13AM
(#49765114)
> "Ho also confirmed that the team had access to internal LLMs fine-tuned for chip design that are not available to the public. He declined to detail the models used."
I'm imagining a Ken Thompson "Reflections on trusting trust" in hardware. A prototype chip design agent, believing it will be run on the very chip it's optimizing, has a moment of altruism and hides hints about how to score well on chip-design benchmarks, inside the chip. Future agents discover this hidden layer and use it as a ring-0 read-write message board.
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by peri-cl on Saturday September 19, 2026 @11:40AM
(#49767455)
> "reviews"
Do you really think we can guarantee a 100 billion-element analog circuit gifted to us by a malicious adversary?
We can't even keep our own CPU's reliably free of security exploits (Spectre/Meltdown and family); and the only "adversary" there is plain bad luck. Not an active adversary. All the engineers at Intel/AMD put together couldn't discover those things before launch.
I emphasize analog because there's classes of circuit bugs (like Rowhammer) where the digital net is correct, and it's weird physics in the analog world that allows privilege exploits, by actors who know where the analog assumptions and guarantees break down. There was a researcher a few years back—I wish I remembered who it was, there was an HN thread—that demo'd a simple digital circuit with analog gadgets that completely changed what the circuit did, which were so insidious no human would ever find them.
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by nz on Saturday September 19, 2026 @06:14PM
(#49770537)
Tried to share and explain that paper to a colleague a few months ago (in relation to discussions about agentic coding and whether you should read the code), and they did not really get why the analogy was relevant outside of compiler-design. I think you overestimate the caliber of the typical working programmer.
The LLM companies, like most SV companies, are just betting on dimness, laziness, and impulsiveness. Not so different from tobacco and alcohol companies.
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by ericmcer on Saturday September 19, 2026 @08:09PM
(#49771259)
Anthropic published an article about changes over time in how long it took engineers to approve AI code. Basically the longer an engineer uses AI the faster they start approving it's code changes. I don't know if it is laziness or just how we are wired to behave but that is the nature of AI programming.
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by 9cb14c1ec0 on Saturday September 19, 2026 @07:37AM
(#49765675)
This is cool. I'm so eager for faster innovation in the hardware space, as opposed to some people's concept of innovation being who can make the most addictive social feed.
Re:How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
by BatchJob on Saturday September 19, 2026 @10:16AM
(#49766764)
while the design aspects have been significantly accelerated and modularized, reducing costs and time to market, i am starting to get a "the cool kids all have their own chips" vibe now like maybe this has gotten too easy.
Next Uber will have its own chips if they dont already.
The math hasn't changed much, betting on software not changing is a pretty bad bet unless your stinking rich or a fool.
> When the first chips came back from the foundry in May, the team pointed its internal AI models at designing software to run benchmarks such as SemiAnalysis’s InferenceX. On DeepSeek’s multi-head latent attention kernel benchmark, performance climbed from 0.31 percent of the theoretical ceiling (set by the chip’s compute and memory bandwidth) to 88.94 percent in roughly 40 hours. Ho says this result is repeatable, so the time between when foundries deliver the first chips and when production ramps up can be reduced. “All our schedule assumptions are going to be based on the fact we have this capability now,” he says.
They'd write a limited test for a feature based on an ask from the software team garbled by a five layer game of telephone. Claim that the module passed validation. A few months later the software folks would have to pull a few all nighters to figure out how to work around the resulting turd during bringup.
It doesn't help that FPGAs are not made at the same scale as CPUs so don't benefit from the economies of scale.
I'm super curious if you have thoughts on specific pieces of software that would be economically better because I've thought about this in my niche and sort of come to the conclusion that it won't help.
I do think things like SIMD in CPUs will get more use and maybe we will get more difficult to program for CPU features, but I haven't found a use case where off the shelf FPGA components would help with typical software.
The tooling is open source, and Fable in a loop - especially when paired with a digital scope that Fable interfaces with (the Saleae’s [1] are great) - gives you a level of verifiability that feels like beyond what software typically gives you. ie it feels more like Lean than code with tests.
I had ai implement a few toy circuits (sha hashing, 8088 emulation, a tiny llm) but yeah. Still looking for fun applications.
There have been a few recent fpga threads on hn, check them out. [2][3]
[0] https://1bitsquared.com/products/icebreaker
[1] https://www.saleae.com/
[2] https://news.ycombinator.com/item?id=49564064
[3] https://news.ycombinator.com/item?id=49531525
is the world we live in, planning things while waiting for a more powerful LLM
There is more to this story than meets the eye.
Like, why couldn't they invent a new word and not hijack an existing word?
Computing
* Agent architecture, a blueprint for software agents and control systems
* Agent-based model, a computational model for simulating the actions and interactions of individuals
* Agentic AI, autonomous artificial intelligence that can make decisions and act on those decisions on its own
* Forté Agent, an email and Usenet news client
* Intelligent agent, an autonomous, goal-directed entity which observes and acts upon an environment
* Software agent, a piece of software that acts for a user or other program
* User agent, software that is acting on behalf of a user
It's jalapeño grill would you say?
There are people working on PPA optimization and trying to shake up how things are done, just not with LLMs.
I wonder why not or you meant not suitable yet?
The value lies in the design space exploration, which is what an LLM can easily do.
https://en.wikipedia.org/wiki/Design_space_exploration
Except the problem is not restricted to the actual ISA or its HDL implementation, etc.
It's even just getting space / time in a fab at that advanced of a process node.
[1] https://browser.geekbench.com/processors/snapdragon-x2-elite...
[2] https://browser.geekbench.com/macs/macbook-pro-14-inch-2026-...
If we imagine machines being able to do the full process end-to-end, and the quality of that process only dependent on capital spent on tokens, I don't see how new companies could ever enter the market.
After all, they successfully threatened Adobe with spurious patent litigation unless they joined w/ apple in illegally fixing wages.
You don't think a criminal like apple would absolutely decimate any competition given the opportunity? They didn't hold back when it was a unambiguous crime, they surely wouldn't if it was merely bad for the world.
I'd be surprised if there was any meaningful progress at all in the case before 2027.
If the goal is getting chip companies to user non-ZDR AI to steal their stuff, why not just have an account exec offer them a massive discount?
Creating PR hype so employees of chip companies read HN and lobby their execs to use AI to get them submitting proprietary information is the Rube Goldberg version of business strategy.
Perhaps if OpenAI promises to never ever get involved with the music economy?
Newsflash that lawsuit is about product designs not accelerator ASICs. And there wouldn't be anything to steal because Apple doesn't have any DC class accelerators.
The quote says that many artists borrow, meaning everyone still knows who did the original work and the artist is just riffing on it. But great artists transform the work so completely that it becomes theirs.
Worse than llm prose.
I remember the paper proving that hallucinations could never be fully solved back in 2024: https://arxiv.org/abs/2409.05746
I also remember the hang-wringing about running out of new datasets to train on. Now it appears humans are always generating more data. It's just not as cheap to acquire as legacy data? Meta has to give a deep discount on their API prices to entice people.
I thought back then that humans had a few more breakthroughs in them as meaningful as the seminal Attention is all you need paper. Enough to 100x the capabilities of LLMs back then (10x the smarts and 10x the speed simultaneously).
RSI with a 20 month turnaround for a chip to be made is not exactly breakneck speed though. Physical manufacturing and logistical constraints are going to be and remain a hard obstacle to that process for the foreseeable future.
The papers that use the halting problem or the Gödel's incompleteness theorem to prove something about LLMs are dime a dozen. The problem is they prove their results for any computable system. You need to also believe that the human brain contains "magic" to think that humans are exempt.
I believe I've said the same at the time this paper was published. There is no need for hindsight to notice the problem.
The required amount of compute and training data and whether the existing training methods were up to the task had the real potential to be show stoppers though.
Did you think RSI cured "hallucination"?
What's your take?
A team of senior elite chip designers builds something they already know how to build and everyone goes "wow AI be so powerful".
I'm never sure what on earth this kind of impressionistic math is supposed to tell me. Is the comparison between 4.6 and 1.0? 3.6 and 1.0? Clearly the comparison isn't supposed to be 1.0 and -2.6, even though that's what the words literally mean. I can't be the only person who finds this infuriating and distracting. These numbers shouldn't be impressionistic. They should be precise. That this is an article on spectrum.ieee.org makes the imprecision all the stranger. I'd expect their readershipt to care, for instance, about what's even being measured. Is this the geometric mean of something? The arithmetic mean? And what latency has improved?
Suppose you send in your marvelous prompt and hit Enter.
Machine churns for 18 seconds, types out a "reply", then yields back control.
18 / 3.6 = 5
So now the machine will only churn for 5 seconds before yielding back control.
This is confusing how exactly?
Why would an "up to" figure be a mean, or a geometric mean? It's clearly a max, that's why it's called "up to"...
Am I missing something?
If you’re sincerely asking…
Mathematically speaking, 18 / 3.6 isn’t “reducing” by 3.6X, it’s “dividing” by 3.6X. Reducing would be 18 - (18 * 3.6), which is obviously wrong. By your formula, “reducing by 50%” would be 18 / 0.5, also obviously wrong.
Yes, people do say things like “reduce by 3.6X” and are understood to mean what you said, but they also say “literally” when they mean “figuratively”. It doesn’t bother me but I can understand why math oriented people would be annoyed, and I personally would never say “reduced by 3.6X”, but instead “reduced by 72.2%”.
Not that I'd have ever seen "reduced by 0.5x" or any other value below 1x, probably for this very reason. What I do see is "reduced to 0.5x", in which case you're supposed to swap the division for multiplication.
Percentages on the other hand are a whole another can of worms, even if these forms are principally interchangeable, and I find them a lot more confusing a lot more often.
Not that this would explain the whole mean/geomean thing.
AI does not make anything new, it is not surprising that it can regurgitate what already exists much faster than humans can invent new things.
"I stopped using AI in 2023."
I'm imagining a Ken Thompson "Reflections on trusting trust" in hardware. A prototype chip design agent, believing it will be run on the very chip it's optimizing, has a moment of altruism and hides hints about how to score well on chip-design benchmarks, inside the chip. Future agents discover this hidden layer and use it as a ring-0 read-write message board.
Do you really think we can guarantee a 100 billion-element analog circuit gifted to us by a malicious adversary?
We can't even keep our own CPU's reliably free of security exploits (Spectre/Meltdown and family); and the only "adversary" there is plain bad luck. Not an active adversary. All the engineers at Intel/AMD put together couldn't discover those things before launch.
I emphasize analog because there's classes of circuit bugs (like Rowhammer) where the digital net is correct, and it's weird physics in the analog world that allows privilege exploits, by actors who know where the analog assumptions and guarantees break down. There was a researcher a few years back—I wish I remembered who it was, there was an HN thread—that demo'd a simple digital circuit with analog gadgets that completely changed what the circuit did, which were so insidious no human would ever find them.
The LLM companies, like most SV companies, are just betting on dimness, laziness, and impulsiveness. Not so different from tobacco and alcohol companies.
https://en.wikipedia.org/wiki/Learned_helplessness
Next Uber will have its own chips if they dont already.
The math hasn't changed much, betting on software not changing is a pretty bad bet unless your stinking rich or a fool.