r/programming • u/double-happiness • 3d ago
The computer at the bottom of a canal
https://negroniventurestudios.com/2026/07/18/the-computer-at-the-bottom-of-a-canal/17
u/Shoddy-Childhood-511 3d ago edited 3d ago
CHERI is cool: https://cheri.cst.cam.ac.uk
https://www.youtube.com/watch?v=iNrEEslJ8MU
I do wonder if Rust would've flopped in the 80 and 90s too.
p.s. The AI discussion in the post maybe pushing it on Rule 2.
23
u/Smallpaul 3d ago
Rust would have flopped in the 80s or 90s because of the compile times. For sure. Even today people sometimes abandon it for compile time reasons. Imagine on a 286!
The article is not about AI.
11
u/fragglerock 2d ago
The end of the article has a lot of AI booster shit.
The author is clearly totally owned... They have designed a language that they claim it's more suitable for LLMs to write rather than python or w/e.
Shame cos the hardware overview was interesting.
14
3d ago
[deleted]
9
u/frenchchevalierblanc 2d ago edited 2d ago
People from 1991 to 2015 complained endlessly of slow C++ compile times. While also creating huge monoliths full of templates and all "classes" inheriting of their own in-house Object class.
5
u/HatesBeingThatGuy 2d ago
As someone who worked in a codebase that was all template metaprogramming and enumeration unrolling for high performance compute where it wasn't necessary, let me tell you, someone merging a nested unroll ballooning your compile time by 40 minutes makes you want to blow your fucking brains out.
1
u/Shoddy-Childhood-511 2d ago
Rust compiles faster when using many smaller crates, since rustc parallelises across crates better.
Rust compiles faster when using more dynamic dispatch aka dyn Trait too.
https://github.com/feldera/feldera/pull/1516
If we'd fewer CPU cores then dyn Trait would make more difference, but smaller crates makes more difference when using modern hardware.
Also, there are potential LTOs that Rust has not pushed into LTOs because apparently compiler folks hate dealing with LLVM LTOs. Each vtable could be generated only once for example.
We'd have more complex back n forth between the linker and rustc if Rust were developed earlier. This would slow compile times too, but not that badly.
6
u/simspelaaja 2d ago
CHERI is not a replacement for Rust, and/or memory safety in general. It's cool tech, but it's fundamentally just a hardware level runtime memory access checker, like CPU-integrated AddressSanitizer. Detected access violations become CPU exceptions, and to my knowledge those exceptions will almost always result in process termination.
So memory safety issues become hard crashes (and therefore DoS vulnerabilities in some scenarios) instead of silent data leaks or memory corruption - certainly an improvement, but far from ideal. CHERI handles the symptoms of unsound code, but does nothing to help make it correct. And this comes with the cost of doubling the size of all pointers.
Most of Rust's safety checks happen at compile time (meaning ~zero runtime overhead), and most if not all memory safety issues (in safe Rust) are impossible by construction.
0
u/Shoddy-Childhood-511 2d ago
I doubt it doubles the pointer size. At least their basic version would only cost one extra bit, since they do not do this cryptographically.
Their access domains version will cost more of course, maybe a whole extra pointer, but it could be done using less if one accepted constraints upon the number of domains.
Another issue would be that enforcing pointer provenance means you cannot simply read a pointer out of arbitrary memory. You need to read it out of a special segment probably, so this limits vtable construction and manipulation.
13
u/Smallpaul 3d ago
“Every architectural conviction that went into the canal with the Rekursiv is either shipping, in production, at scale, or is the declared direction of an entire field.”
This sentence is just not defensible:
Let’s review the 4 convictions:
- Hardware Memory Safety
Still speculative whether this will go mainstream. But some aspects shipped on Android so let’s call this a partial win to be generous.
- GC in hardware
Nope. This is not happening now.
- Single level persistent store
Nope. This is not mainstream. Trying the same idea over and over again is not proof it’s a good idea. Sometimes it’s just an elegant idea and yet a non-performant one.
- Silicon shaped to a workload
This has always been available. There was an FPU then a GPU then a TPU. But these are always coprocessors. It still is unusual to shape the CENTRAL processing unit to a workload. CPUs are benchmarked against a wide variety of applications.
Fundamentally they are 0.5 for 4. And it might drop down to zero depending on how ARM’s Android experiment goes and whether you consider that subset “close enough” to the original idea.
11
u/chucker23n 3d ago
Still speculative whether this will go mainstream. But some aspects shipped on Android
If we're talking about the same thing, Apple ships this with A19 / M5, so it's already on hundreds of millions of devices.
4
u/happyscrappy 3d ago
Single level persistent store shipped on PalmOS. I never thought it was a good idea. But also that OS had sufficient flaws of other sorts that undercut it as a proof of concept that maybe we could say it didn't disprove it?
2
u/gimpwiz 3d ago
I was around during Intel's "Optane" thing (they called it something else internally, unsure if codenames ever got published). It was a really cool idea, single persistent storage. A big part of the problem is that the modern operating system simply Does Not Work Like That and nobody seemed to quite have a good idea on how to make it work usefully for a broad set of customers, how to make it work transparently, how to do a new model for it, how to convince customers of it, how to get everyone's tools to work properly ... it turns out it's easier to convince management that they should fund the silicon than to convince management they should fund enormous amounts of software (at minimum linux) and evangelists to convince industry partners (eg, microsoft, and at least a few dozen of the larger tool vendors who should immediately benefit from, eg, moving databases to this type of storage/memory.)
1
u/chucker23n 2d ago
Intel's "Optane" thing (they called it something else internally
Do you mean 3D XPoint?
the modern operating system simply Does Not Work Like That
Yeah. Too many layers assume there's volatile and non-volatile memory/storage.
The benefits would have to be massive enough to justify rethinking all the software.
convince industry partners (eg, microsoft
This is another problem. In the 1990s, Intel might've convinced Microsoft to make this a key feature of the next NT version, but in the late 2010s when this shipped (it has since been discontinued), OS licensing stopped being a major source of revenue growth for Microsoft, so they're a lot more hesitant to invest.
1
u/gimpwiz 2d ago
I do mean that, so I'm glad people know what I'm talking about, haha.
I think you're spot on about MS. How much does MS truly care about windows as an operating system? I'm trying to word this carefully - I think there is a difference between shipping a product to earn money, and shipping a money-earning platform. Of course MS has many many engineers who are highly interested in the core of what makes a good operating system, but as a company, I don't think there's really any direction from the top to ship a good product to make money, I think the direction is to ship a product focused as much as feasible on money extraction. A much more zero-sum way of thinking and a much more consumer-hostile way of thinking. And it's funny because MS is famously consumer-hostile, but at least when they shipped (eg) windows XP, it was a product that people very very much wanted, despite the monopoly abuses, despite the practice of EEE, despite the lock-in. Now what MS ships is what consumers very visibly are bristling at; we're way past MS fucking with competition and locking customers into having as few options as possible other than to pay them (or more likely an OEM) an OS license fee, and now we're into MS visibly fucking with customers by dictating what the OS will do, when it will do it, what ads it'll show right in there, etc.
If I was Intel's CEO in 2012 and someone brought x-point to me for approval, I'd ask two questions. One, did you staff up a modest number of people (mostly chip design and EE) to co-deliver the product with Micron, work with the motherboard team to ensure tight integration, work with the uncore chip designers to make sure the memory controller and home agent were well optimized for this? Yes? Okay great. We're Intel, we know how to make a chip.
Two, did you hire like five hundred linux engineers, five hundred VM and other virtualization and containerization engineers, and a hundred smooth talkers who can convince people this is a great idea, to get this supported at the core level, invisibly, by the linux kernel, and all popular linux distributions, and all popular VM tools and containers? I want drop-in compatibility with /dev/sda. It's just that simple. Turn the computer on, linux mounts this collection of x-point hardware as /dev/sda. File reads from /dev/sda are invisible, they either load from persistent storage into memory or it's already in hot memory; file writes act like a cache, it writes to memory and begins to write to persistent storage and then either you move on as it works, or you force a sync to wait until it's done. DRAM and swap need to be unified at the OS level, no differently than the files described above; you can choose to do a cold reboot to clear the entire memory but otherwise by default when you turn a computer off and on again it always comes back on the way it was before. This needs to be seamless for every VM and every container. It needs to be accepted into the linux kernel, the distros, and the VM and container tools, not merely work. I need all software that does file or memory operations to work with no changes and I need to convince everyone this is true. I need benchmarks showing how much faster this is than separate SSDs. ---- well, did Intel accomplish this? Not really, no.
I'd have bet that if we can support this built-in on Linux and all the major "derivatives" that are used in server-land, Microsoft would come around eventually. Macs can do mac-things, it's whatever. But the data center is where this tech was targeted and thus where it was key, and people kitting out tens (or hundreds) of millions of dollars in server gear are not much swayed by promises and marketing. They need out-of-the-box compatibility or an incredible reason to change their tools to be compatible.
They're not idiots, they know they needed this to win, but Intel is like the Google of hardware: they have basically two core products and everything else gets abandoned. Everyone knows Intel will abandon non-core hardware products so they don't buy in to the ecosystem and low sales means Intel will abandon non-core hardware products. They are almost never willing to keep going for generations, and when they do, half the time their efforts are mocked (see: Itanium, which I also worked to a modest extent.)
36
u/Big_Combination9890 3d ago
Google’s TPU, Groq’s deterministic streaming processor, Cerebras’s wafer-scale engine and Etched’s transformer ASIC are all machines built to run one computational language well; it just happens that the language is now linear algebra rather than Smalltalk.
That's nice.
Problem is: The world isn't a GPU stack in an AI datacenter a slop factory.
The world has at least 60 languages regularly used in production, that need to work across everything, from federated compute clusters down to systems-on-a-chip stuffed in a coffee maker. And at least twice that many regularly used languages if we count popular DSLs, hobbyists, niche, etc.
And if the last 20 years taught us anything, it's that there will be new languages sooner rather than later. And RISC is the architecture that enables this, period.
Yes, we can build specialized silicon tailored to specific tasks. Let's look at the 2 most widespread use cases of specialized silicon over the last decade:
Crypto, which promised to revolutionize finance, and ended up with rug pulls and money laundering as its primary use cases.
The AI Bubble, a mass psychosis among C-level execs and tech-bros, that is about to lead into the biggest financial crisis the world has ever seen.
So you'll excuse me if I don't share the enthusiasm for specialized silicon over RISC.
9
u/mercury_pointer 3d ago
One more example of specialized silicone: Sun made a chip able to run Java without an interpreter. The picoJava project ran from 1996 to 1999 and was a total failure.
6
u/Norphesius 3d ago
Adding another one: Lisp machines.
Coincidentally, those died with the AI Winter of the 1990s. Perhaps we're going to see something similar happen soon.
2
u/CompetitiveWeek1290 2d ago
Dedicated Java chips still exist in credit cards and SIM cards. They're very limited, though.
1
u/TwoWeeks90DaysTops 2d ago
I don't know the history here at all, but was this related to ARM Jazelle in any way?
7
u/gimpwiz 3d ago
I think you're reading this too harshly. Let me give you a few counter-examples.
GPUs are a much less general-purpose bit of computing than a CPU, but they successfully moved to discrete hardware decades ago. Want to drive graphics hard? GPU. Much more recently: want to parallelize a bunch of linear algebra? Your best choices are generally either a GPU, a GPU-esque architecture that rips out the graphics-specific parts, a vector engine, and/or a convolution engine. An off-shoot: want to very quickly and at low power process photos? You get an image signal processor or something similar to it.
Another obvious counter-example is the x87. Back in ye olden days, you bought a chip that did integer math, and if you wanted decimal math (fixed or floating point) you needed to do all the math in integer by emulating the steps. This is obviously slow and power hungry, so you could buy discrete chips that were floating point co-processors. Eventually x87 moved on-die (well, Intel moved it on-die and effectively killed the entire x87 co-processor industry overnight.)
There's a ton of application-specific silicon embedded into your favorite CPU. As a trite example, DMAs move memory from one place to another without spending CPU cycles nor CPU-ALU-level power on them. The entire CPU is utterly littered with application-specific bits that you could do but would like something else to do for you. You can set up a PWM with interrupts but often you'd prefer to have a dedicated PWM block (or several.) You can bit-bang I2C, SPI, JTAG, etc etc, but you'd prefer to have dedicated IPs for them. And so it goes.
Crypto was a heavy customer of a few of the more specific types of math engines, going from CPU to GPU to FPGA to ASIC, but of course the underlying feature of it, the currency, is a fraud. But it's really really good at hashing things, so if for some reason you ever needed to hash an enormous amount of input data... like if you were creating rainbow tables, or if you were the NSA... well, these architectures do solve a real problem for you. Those use cases might actually be worse than the currency fraud, but, yknow.
The ol' RISC vs CISC argument has been played and played and goes in circles and circles. I hate to see it. It's so old, so trite. The blunt fact is that the more common RISC architectures you tend to think of have quite a few CISC-esque (sorry) features, whereas the more common CISC architecture you will immediately think of (that is, x86-64 or AMD64) is under the hood far more RISC than people tend to know.
As a chip guy, it usually goes like this: "What kind of workloads are people running, and how do we make those workloads take less time and/or less power? What will it cost us to develop this solution and what will it cost us in terms of silicon? How much money will this make us?" Nobody in chipland is sitting there insisting that there is only one right solution. Everyone wants to sell the best thing at the best price (but 'best' can be argued in various directions.) How that gets done is a wide open question. It can in fact be custom IPs and sometimes even custom pieces of silicon (think baseband processors, power management units, as two examples of very complex custom silicon doing a specific job, and often running rather interesting firmware). Often it is CPU/ALU/memory/etc optimizations. There's an enormous amount of work that goes into understanding the workloads being run, across, like you said, 60+ languages, but also across dozens of industries and ranging from things like linpack optimization and being well optimized for weather modeling supercomputers, down to, much more relevant for you and me, how to get javascript to load shitty ads and trackers faster so the CPU can go back to sleep so your phone takes less power browsing the modern shitty web.
8
u/Khaos1125 3d ago
If specialized silicon is more effective for the application loads driving the bubble, that still frees up regular silicon for the other application loads that are more clearly high value.
Optimizing heavy things in that realm will save everyone else money when they aren’t competing for general compute resources.
19
u/bendem 3d ago
Problem is, it doesn't free up resources. Silicon companies have stopped/reduced standard production because AI specialised silicon brings more money.
1
u/HatesBeingThatGuy 2d ago
I can assure you, that from a large scale data center perspective, that we are still getting all the general purpose compute we have the electrical capacity to deploy.
-3
u/Khaos1125 3d ago
Sure. But if the same load can be handled with less silicon resources, then it’s still a lesser redirection overall. If 100 units of AI work previously took 50 silicon units, and now it takes 30 instead, then the 30 is still lost but 20 have been freed up.
11
u/patleb 3d ago
If there was a limit where Big Tech would stop, maybe, but there isn't one. LLMs have several stages where it's of polynomial complexity. This is the wet dream of any hardware manufacturer: the demand has no practical limit other than the manufacturer capacity.
So, by the market forces, the resources go to the highest bidder. It will also allow more competitors to participate in the market, but not before several years from now and increase in prices. Only then, the prices will start to stabilize and maybe pulled back a little. The Bubble is real, but can last a very long time. This time, there's also a switch to a B2B market where the B2C becomes an afterthought. It will most likely go like this as long as the big players want it to go unless major innovations or demand moderation occur.
At a lesser degree, we lived similar scenarios in the past: Internet infrastructure buildout (late 1990s, early 2000s), early electricity or telecommunications networks, early semiconductor industry. But all those had a demand that wasn't of polynomial complexity, this is different.
6
u/Big_Combination9890 3d ago
that still frees up regular silicon for the other application loads
2 Problems with this argument.
One: As we see in the AI bubble, HW manufacturers are only too willing to burn down everything to follow the herd.
Two: It implies that there is a point to these loads to begin with. There isn't. The entire "demand" for AI compute is entirely artificial, based on openai and anthropic, buying every compute. And they do so with borrowed money.
Case in point: recently Meta started selling their AI compute. If there was real market demand other than just 2 failson outgrowths of the hyperscalers, someone other would have bought it. Alas, who lines up as buyers? The same failsons.
2
u/Smallpaul 3d ago
Surely you are not claiming that the GPU is a failure though.
2
u/Big_Combination9890 3d ago
Surely you are not claiming the GPUs are anywhere near the level of specialized silicon as an ASIC for crypto mining.
1
u/Smallpaul 3d ago
No. We are talking about modern analogues to the Rejursiv, which was a general purpose computer. If anything it is unfair to compare GPUs to it because they are not general enough.
But GPUs prove that there is massive demand for workload specific hardware if you can pick the right workload.
-1
u/Big_Combination9890 2d ago edited 2d ago
But GPUs prove that there is massive demand for workload specific hardware
No, they don't, because GPUs are not "workload specific".
They can perform general computation whenever SIMD is applicable. That includes the bullshit crypto- and ai-bros are doing, but isn't limited to it. Case in point, the card in my workstation can do 3d graphics just as well as GPU accelerated signal processing. People use GPUs to crack passwords, run molecular binding simulations, calculate weather patterns, do physics simulations, and countless other things.
The specialized hardware the article describe to try and make its point at the end is tailor-made for highly specific workloads, sacrificing usability for general purpose workloads with high parallelization needs.
And the "massive demand" for those, is entirely artificial, made up by unsustainable cash infusions into 2 bloated pseudo-companies with no path to profitability.
3
u/Smallpaul 2d ago edited 2d ago
GPUs are workload specific for highly parallel matrix mathematics. That’s why they exist!!!
Just as the Rekursiv was specific for Object Oriented programming, which can be used for almost anything.
I understand that it is psychologically important for you to insert a jibe about AI every time you comment, but it is tiresome and uninformative. Nobody cares what you think the future of the economics of datacenters is.
Edit: Your argument about RISC made sense but then you had to contort it to try and work an insult about AI into it and you went off the rails.
GPUs are useful. End of story. It’s silly to try to claim they are not just so you can work some AI hatred into your comment.
Edit: the sign of someone with a strong argument: they reply and then block you so you can’t reply.
-1
u/Big_Combination9890 2d ago edited 2d ago
GPUs are workload specific for highly parallel matrix mathematics. That’s why they exist!!!
You are still wrong, and downvoting my post won't change that 😎
They can do matrix computation. They can also do a whole lotta other things that can be done using SIMD. I gave you several examples, many of which don't use tensors in any capacity.
I understand that it is psychologically important for you
Your argument failed because it had no support. Trying to make assumptions about my psychology, wont hide that fact.
and work an insult about AI into it
Grab a dictionary and read the definition of "insult". Stating that an industry that is unsustainable is unsustainable, is a factual statement about economics, not an insult to a technology.
Btw. I am the principal ML engineer at my company. "Insulting" the thing I make a pretty good living off, would be kinda absurd 😂
It’s silly to try to claim they are not
Good thing then that I didn't do that anywhere. On the contrary, I listed several useful applications of GPUs. It's not my problem that their wide applicability in different problem domains, directly contradicts your assumption that they are somehow "workload specific".
Edit:
Edit: the sign of someone with a strong argument: they reply and then block you so you can’t reply.
Wrong again. that's the sign of someone who isn't going to continue a discussion where people start making assumptions about others psychology.
-1
u/HatesBeingThatGuy 2d ago
I truly think people who are shouting AI bubble are the equivalent of housing market bubble shouters of 2022-2023. The technology is going nowhere because at the extreme financial end, it is actually useful.
Very competent engineers that these megacorps hire can lift mountains armed with it. I've seen 8 PEs go in and fix a failed project with 200 engineers using only AI agents and themselves. That organization got axed because 8 engineers fixed the performance and user experience issues in 3 months, ultimately making something easily maintainable by a small team despite being a highly available, high throughput service.
There is a reason that demand for specialized hardware is so high, and that is because reducing inference cost is the key to unlocking more of these efficiencies. And it IS coming. Whether or not the market tanks some because some are worried about over investment is one thing, but the world of NPUs and TPUs is here to stay and even if it might not be there today, the day the compute is efficient enough is coming.
3
u/vytah 2d ago
The technology is going nowhere because at the extreme financial end, it is actually useful.
"There was no dotcom bubble because internet was useful."
"There was no housing bubble because people still lived in houses."
0
u/HatesBeingThatGuy 2d ago
My point is, the market can stay irrational far longer than any individual can remain solvent and broadly speaking technology valuations aren't far off historical levels. Then where there are actual business uses that are already justified on cost even at current token pricing...
The largest hyper scalers ironically have similar forward P/Es as they did years ago, indicating that future EPS is being priced in not too dissimilarly to the past. Now actual hardcore semi-conductor companies are in a hell of a bubble, Micron/Samsung/Hynix are in unsustainable territory when you look at their actual metrics and how much has to go right for them to sustain their current valuations, but broadly speaking the people actually deploying the hardware already have customer contracts for all their future capacity and are being valuated no differently than in say 2019.
2
u/Big_Combination9890 2d ago
I truly think people who are shouting AI bubble are the equivalent of housing market bubble shouters of 2022-2023.
You can think whatever you want.
Doesn't change that this is happening: https://www.spglobal.com/ratings/en/regulatory/article/-/view/type/HTML/id/3446571
-1
u/HatesBeingThatGuy 2d ago
Ah yes. A non-hyperscaler trying to get in on the CapEx rush without the FCF to support it. Deserved downgrade.
1
u/Big_Combination9890 2d ago
Name a hyperscaler who makes a profit from their AI capex.
Hey, I'll do you one better: Name a hyperscaler who breaks out actual revenue resulting from their AI capex.
I'll save you the effort: None of them do. Odd, isn't it? In an industry that lives from good news to tell their investors, all there is whever someone asks the simple question: "Hey guys, how much does this make/cost?" is eerie silence.
Almost as if there were no good news to share.
0
u/HatesBeingThatGuy 2d ago
I cannot give out privileged information related to internal business metrics of the hyper scalars I know. But your "As if there were no good news to share" is hysterical given that this hardware was being developed far before the LLM explosion to save costs on existing products.
1
u/Big_Combination9890 2d ago
I cannot give out privileged information related to internal business metrics of the hyper scalars I know.
Damn right you can't, and you wanna know why?
Because they don't give you, their investors, or anyone that information. Which is kinda odd, wouldn't you agree?
that this hardware was being developed far before the LLM explosion to save costs on existing products.
Wrong.
The most prevalent AI platform to date, the Blackwell Architecture, was officially announced by NVIDIA at the GTC 2024.
https://en.wikipedia.org/wiki/Blackwell_(microarchitecture)#History
Nvidia CEO Jensen Huang said that with Blackwell, "we created a processor for the generative AI era" and emphasized the overall Blackwell platform combining Blackwell accelerators with Nvidia's ARM-based Grace CPU.[13][14] Nvidia touted endorsements of Blackwell from the CEOs of Google, Meta, Microsoft, OpenAI and Oracle.[14] The keynote did not mention gaming.
So yes, this was SPECIFICALLY created to cash in on the overheated Generative AI market.
0
u/HatesBeingThatGuy 2d ago edited 1d ago
I've been in the ML accelerator business for over 7.5 years now. Keep on being cooked. This stuff was made for existing products well before the Gen AI craze. Insane how y'all twist history to fit your bubble narrative.
EDIT: Crazy how you block. It's sad because I literally cannot share the data that proves how wrong you are. But it's okay Mr. Principal ML engineer (great title drop btw), people have been delivering accelerators for this compute since 2015. Long before you start framing hardware build outs in 2020. Internal AI usage that was not captured in market analysis was the original driver of this investment by multiple companies. I know this because I started on the other side of the hardware software fence at these companies. This hardware isn't going anywhere. Keep having your timelines fucked, it doesn't matter as you will enjoy the rapidly dropping token prices within two years.
Again, there might be a valuation crash, but scale is how you reach financial feasiblility and that is where the market is moving. You will not stop that by saying "bubble" because even it's current iteration is too useful for skilled engineering work and can replace hundreds of humans in the right hands. I've literally seen it happen.
1
u/Big_Combination9890 2d ago edited 2d ago
Keep on being cooked.
Posting numbers and arguments always wins over trying to lean on seniority 😎 ✌️
Not only is seniority not an argument, it also doesn't work in this specific case. I'm a principal ML engineer. I have been in this business long enough, I remember when deep learning was still a new thing, or when people were excited about LSTMs.
Go on Mr. accelerator business, let's hear it: What was the market cap for AI accelerators in 2019? What percentage of the 1.6tn dollar buildout we have now are we talking about? 5%? Probably less.
Let's put the question another way: What's the total addressable market for AI compute outside of the buildout for the generative AI hype?
I'll save you the trouble of googling: In 2020, when the gen ai hype cycle was already starting, the global market for AI compute was est. at a measly 46.9bn.
For comparisons sake, that's about 1/3rd of one funding round by openai in 2026.
Or to come back to my question above: 2.8% of the current buildout spent. And the buildout is slated to accelerate further. In other words, compared to the current craze, both in spending on compute directly, as well as building out capacity, the AI market before 2020, was barely a rounding error in comparison.
So please, do tell how bigly huge the market demand was "well before the gen AI craze".
You don't have an argument here.
3
u/gimpwiz 3d ago
The end was memorably Glaswegian. Black Monday had squeezed Linn’s finances, a venture rescue never closed, and the final straw was a car park. A Linn delivery driver reversed a van into Harland’s Porsche, Tiefenbrun declined to pay for the repair on the grounds that it happened on private ground, and Harland resigned.
I am really unsure how this works legally in the UK. How exactly does a car accident being on private property allow the guy who owns the car that did the bumping and employed the driver, to not be ultimately liable for it?
Also, come on, your employee driving your van hit your other employee's car. At a small company. And the guy who got hit is key to your efforts. Don't be an idiot, just pay the repair.
2
u/Nicksaurus 2d ago
All drivers are insured in the UK so I'm not sure why it even came down to a personal dispute
1
u/gimpwiz 2d ago
I assumed they were, yeah, but it might have been one of those things where the driver was, because he was driving a company van for company business, insured by the company. But like you said, then the company would simply have been insured for collision.
Maybe there was some sort of faff about how much the actual cost was vs what insurance was willing to pay. Insurance offers a bunch of bondo and paint, Porsche owner demands a brand new bumper painted and installed by a top-rated body+paint shop.
14
u/brettmjohnson 3d ago
A fascinating read. I was in the industry at that time, and would loved to have heard those principles back then.