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Category: tech

  • Why Data Centres Will Change the World and Bankrupt Their Builders — John Hempton

    Everybody who touches it will go bust.
    Everybody who touches it will go bust.

    This talk is a wonderful explanation of the new AI build out. It’s amazing technology, but it’s going to bankrupt the people who are building the data centers.

    John says, “AI as a tech is freaking amazing.” He understands, because he works with it, that it’s going to transform human society at scale.

    John’s argument: high capital cost, low margin production, and open to competition means various people will build data centers, there will be a race to the bottom, every one gets AI, like electricity, and the initial builders will go out of business because the cost of producing the hardware (or software “tokens”) keeps going down. Think of the railroad builders, or the optic fiber build out, same process.

    “John Hempton is Australia’s most prominent short seller and the founder and chief investment officer of Bronte Capital, a Sydney-based global hedge fund.”

  • AI “Stochastic Parrots” Talk

    Sam and Dario looting investors.
    Sam and Dario looting investors.

    I saw the thumbnail and laughed and laughed. The picture is amusing and partially true, but the commentary is not balanced. I use AI everyday as an IT admin and programmer. It’s super useful, it’s not just hype. Most folks are creatures of habit and you have to really think a lot in order for AI to be useful in a job so it’s going to take a lot of time for AI to be adopted in the workplace.

    The big picture is complicated and nuanced. OpenAI is loosing a lot of money. Anthropic at least has enterprise revenue to say it’s making money. But it has $895 billion in commitments, and it’s unknown whether the commitments come due before the revenue can grow to pay the bill.

    The interview is with a lady who wrote the “Stochastic Parrots” paper.

  • Conversational AI

    It's not a bug, it's an inherent feature of LLM AI.
    It’s not a bug, it’s an inherent feature of LLM AI.
    I'm sorry--you're right. That mushroom is poisonous.
    I’m sorry–you’re right. That mushroom is poisonous.

  • Fascinating Podcast with Lex and DHH

    DHH: Future of Programming, AI, Agentic Engineering, Vibe Coding & Linux | Lex Fridman Podcast #501

    A whole lotta fun and useful programming info. DHH goes from a skeptic a year ago to a convert today, running 5 linux boxes — 16 concurrent threads is his current limit. Programming is now more orchestrating a chorus of agents rather than chiseling code one line at a time like Michaelangelo.

    DHH Lex Podcast
    DHH gifting Lex with an Omarchy laptop

    And DHH gives Lex a new Omarchy laptop on his birthday!

    Lex Podcast
    Lex gets a laptop that installs a new linux OS in less than 60 seconds

  • Interesting Reddit Post Regarding PC memory for AI

    Unified Memory Concept but for x86 computers?

    I was looking at these ‘AI Cluster’ videos where they are using 4 Mac Pro studios and making these clusters work for AI. They kept on talking about how the ‘Unified memory’ made it possible for these Macs to make it run. By the looks of Nvidia being cheapskates with VRAM on their latest GPUs, do you guys reckon the concept of ‘Unified Memory’ will trickle down to x86 computers eventually to the point that GPUs will no longer have VRAM in them but it’ll be on us if we wish to put in more or less memory in our systems depending on our needs?

    Eventual Answer:

    Takane-sama •

    It won’t be universal but might be more common (since it already exists).

    Strix Halo is the best example of a high-end x86 competitor to the Mac Studio and it highlights both the advantages and disadvantages of that arrangement. You can allocate enormous amounts of memory to the integrated GPU for rendering or AI tasks at a far lower cost than the equivalent amount of VRAM in a discrete GPU. But Strix Halo still gets demolished by a RTX Pro 6000 in GPU-based workloads.

    People are playing with clustered Mac Studios because it’s a relatively cheap way to stack tons of fairly fast RAM, which is important since AI workloads are primarily memory bottlenecked. A Mac Studio with an M3 Ultra and 256 GB of RAM runs at about 820 GB/s memory bandwidth and costs less than an RTX Pro 6000 which only provides 96 GB of VRAM. That’s a lot better than Strix Halo which runs at 256 GB/s or standard socketed DDR5 which runs at around 90 GB/s depending on speed.

    The RTX Pro 6000 though still crushes all of them at nearly 1,800 GB/s. That’s why actual AI companies are treating cards like that as entry level. Processing speeds are much faster especially since you can stack multiple GPUs using PCIe on a single board with faster interconnect speeds than TB5 on a Mac Studio (and data centers use even faster interconnects than consumer PCIe).

    from Reddit