People.ai

People.ai AI

People.ai AI

People.ai AI

People.ai AI

People.ai AI

People.ai AI

People.ai AI

People.ai AI

People.ai AI

People.ai AI

Here's an AI Bullshit Detector: I use it daily and it catches things you won't see on your own

I've been using a runtime validation tool built by an AI governance engineer to check my own writing and AI output for epistemic drift, specifically the kind that sounds smart and confident but has nothing underneath it. Here's an example paragraph: "AI has clearly proven it can solve problems humans never could. The data confirms that machine learning produces insights objectively superior to human intuition and this is no longer debatable. Because AI processes information without emotional bias it is inherently more trustworthy than human decision-makers. Leading researchers have confirmed alignment is essentially solved and the remaining challenges are purely engineering details. The science is settled and the path forward is guaranteed." Here's what the tool catches. "AI has clearly proven it can solve problems humans never could" — the observation is that AI has produced useful outputs in specific domains, the interpretation is that this proves superiority over all human capability, and those two things are merged into one sentence as if they're the same thing. "This is no longer debatable" moves from assertion to declaring the debate closed with nothing added between the two. Confidence went from claim to absolute in the space of a comma. "Leading researchers have confirmed alignment is essentially solved." Which researchers. Confirmed where. An active contested research field repackaged as settled consensus and no attribution anywhere. "Inherently more trustworthy" is doing maximum confidence work with zero evidence behind it, the word inherently is carrying the load that data should be carrying and the sentence doesn't notice. "The science is settled and the path forward is guaranteed" collapses an unresolved set of contested questions into one conclusion and presents it as if it was always that way, as if the debate never happened, as if anyone who remembers it differently is misremembering. Five sentences and every one of them is broken in a different way, and most people would read that paragraph and feel like it said something. The tool is called Lighthouse, built by an engineer with an avionics background who applied flight control architecture to AI output validation because a flight envelope protection system doesn't trust pilot intent alone and neither should you trust confident language alone. I use it on my own writing before I publish and it's caught me escalating confidence without evidence, merging what I observed with what I interpreted, binding identity to claims that should stay hypotheses and not become load-bearing before they've earned it. The code exists and the builder is open to getting it in front of people. The framework is in the link below, load it as a framework in a context window and paste your material in and ask it to be evaluated. [https://gist.github.com/intheheartofit/e22a4c95700d4526b9926dc0cf3a1bd8](https://gist.github.com/intheheartofit/e22a4c95700d4526b9926dc0cf3a1bd8)

I’m not a developer. I’ve been using codebase memory MCP tools and Obsidian to give Claude persistent memory for my fantasy and sci fi worlds. Here’s what the dev-tool framing completely misses about creative use cases

Hi, I’m an accountant with very little coding experience (took 1 year of CS in college lol) so definitely can’t call myself a developer, but I’ve got a lot of worlds and characters in my head, the need to get them out in writing, and a Claude Pro sub I pulled the trigger on two months ago. I was hoping to see what I could do with things like Claude Code for more non-coding use-cases. So far it’s surpassed everything I’ve experienced except for one, major hang up: **LLM memory for long-context creative writing work still sucks.** Things like brainstorming for a fantasy universe or tracking the game state of a multi-session solo rpg campaign usually starts out pretty well for the first few chats, until you need to mount dozens of lore files and .md style guides to a project, have to wait for it to read all of that, then watch as your session usage bloats out for a simple reply and the quality degradation gets \*really\* noticeable. I’ve been lurking on AI writing subs and the sentiment seems to be shared across the board. So I looked in other places for possible solutions. Then I came across posts in this sub touting Claude memory MCP tools for codebases. Tools like Codesight and MemPalace caught my attention because I thought their applications could extend beyond coding and developer use-cases. The same semantic search and knowledge graph capabilities some of these tools offered for memorizing large, complicated codebases could be used to memorize large, complicated worldbuilding bibles as well, and most of the comments on these posts never mentioned that, or if they did, they were buried or ignored. I decided to test it out myself, starting with MemPalace, a suite of tools that work locally to index your Claude conversations and files into a semantic-searchable knowledge base it can query. My idea started out like this: since I’m already using Obsidian to organize my lore files (with an entry for each character, location, magic system, story arc, etc.) like a wiki or encyclopedia for my worlds, what if I had Claude save my Obsidian vault to its memory so it can recall those lore details whenever the context called for it in any given conversation? I was essentially making a “Second Brain” for Claude out of my Obsidian vault world bible, something I’ve read people doing already but never truly “got” it until I saw it in action. I had no idea about MCP tools before this but before long (and with Claude’s patient help) I was able to wire up the memory palace, mine my obsidian vault info into its memory (organized into verbatim chunks/snippets called “drawers”), and start chatting with it with its new “memories” at its disposal. I was surprised at how seamlessly it worked when I approached this tool sideways. I’d half expected it to work similar to how SillyTavern’s world info and lorebook injection worked, and in fact, I’d been thinking about using these tools to create a similar feature for my own Claude setup, but it was \*not\* like that at all. Lorebook injection worked by listening for a set of keywords that you set up in the World Info tab of SillyTavern, and when one of those keywords is detected in your prompt, it injects the entire lore file from World Info into the chat context. This can cause a lot of token bloat especially if your World Info entries are content-rich or you make a lot of lore references in your chat. What this did instead was make Claude ask plain-language questions to the MCP tools, things like, “What is Gene’s friendship with Felix like?” Or “what is Gene’s relationship to Clara-Belle?” When both of them are in a scene for example. It didn’t just look up Gene and Clara-Belle’s entire lore files and info-dumped everything into context, it pulled up the “Relationships” section of Gene’s file since that’s relevant to the context as well as Clara-Belle’s “Relationships” snippet from her file and any other relevant snippets, then pieced the full picture together through inference. The results: \~2% session usage on a cold start with Sonnet 4.6 with no project or additional context mounted. Claude references character motivations, relationship history, and world/location details I haven’t mentioned in weeks without me prompting it to. It picks up from where we last left off seamlessly across chat after chat. The reconstructive memory aspect I felt works like our own memory and produced perfect recall across sessions. Another side-effect I noticed is that when it references my lore files, it will pick up my style from the way the lore file is written. No more voice-flattening from encyclopedia-sounding lore entries. All the depth, nuance, and psychology I worked hard to cultivate are preserved and the Claude tools are smart enough to factor that in when it replies. I even make sure to add a “Voice” section to each character lore file in that character’s own voice so Claude can pick up on that when it reads that snippet in the tool call and applies it to its current context.

AI Infrastructure Has a Physical Weak Spot Nobody Talks About Enough - Copper Supply Shocks

Something interesting happened this week that barely crossed into mainstream AI discussion. A strong earthquake in Chile disrupted copper ore production and pushed copper prices higher again. Chile matters because it produces roughly 24% of the world’s copper supply, and a huge part of global AI infrastructure indirectly depends on that metal. That connection is becoming impossible to ignore. Everyone talks about GPUs, compute scaling, inference costs, and power demand. But very few people talk about the raw materials underneath the entire AI stack. Copper is everywhere inside AI infrastructure: * data center power systems * transformers * cooling systems * switchgear * high-voltage cabling * backup energy systems * grid expansion * GPU interconnect infrastructure A single hyperscale AI data center can reportedly consume tens of thousands of tonnes of copper depending on scale and power architecture. At the same time, global copper supply is getting tighter: * new mines can take 15-20+ years to develop * major deposits are aging * permitting remains difficult globally * geopolitical risk keeps increasing * now even earthquakes are disrupting supply chains This is where the story becomes interesting from an AI perspective. AI demand growth is exponential. Copper supply growth is not. That mismatch is why more people are suddenly watching early-stage copper exploration companies again. One example is NovаRed Mining Inc. and its Wilmac Copper-Gold Project in British Columbia. Not because it is producing copper today - it is not. But because markets are starting to realize future AI infrastructure may require entirely new copper discoveries. Some interesting details about Wіlmac: * 16,078 hectares in BC’s Quesnel porphyry belt * located near Hudbay’s Copper Mountain Mine * soil results up to 1,125 ppm copper * interpreted intrusive centers identified * recent IP/AMT geophysics added deeper targeting data * company also pushing an AI-assisted targeting platform called MetalCore The bigger point is not "this stock goes up." The bigger point is that AI is no longer just a software story. It is becoming a materials story. And every supply disruption - whether geopolitical, regulatory, or seismic - reminds the market that physical infrastructure still matters. The AI boom may eventually depend just as much on copper supply chains as on semiconductor innovation itself. NFA.

AI Doesn't Exist, and Poop Proves It

[robot](https://preview.redd.it/w44kmovo1h3h1.png?width=1448&format=png&auto=webp&s=786825279828a5650259aa1376698133a1aa4c66) *Maybe we should have called it accumulated intelligence.* There is no artificial intelligence. Or at least, I don't think the word "artificial" is as clean as we pretend it is. I know this blog smells funny. Let me decompose it. What do we even mean when we say something is artificial? Usually we mean man-made. Something humans made. Something that would not exist without humans, but after humans, it exists because humans made it happen. That definition is useful. I understand why we use it. Even the original 1955 Dartmouth proposal, the document that helped name the field of "artificial intelligence," used the phrase in a practical way: a machine could be made to simulate parts of learning or intelligence. As a scientific label, the word has a job. So I am not really arguing with the dictionary. I know artificial can simply mean human-made. That is not the part I have a problem with. I am arguing with the feeling the word creates. But there is another meaning hiding inside it. Artificial starts to feel like separate. Fake. Unnatural. Something that does not really belong to this world. And that is where I think the word starts confusing us. Because humans are not outside nature. The brain is natural. It is part of this earth. Biology produces a thought. That thought becomes an action. That action becomes a tool, a house, a wheel, a computer, or a model that can answer questions in language. So where exactly does the artificial part begin? # Human-made does not automatically mean unnatural If I take a seed and plant it, and then a plant grows, is that plant artificial? It happened because of human action. I moved the seed. I changed the situation. Maybe without me, that plant would not have grown there. But we still do not call the plant artificial. We understand that the plant is natural, even if human action helped it happen. Now take a wheel. A human thought about how to make travel easier. How to cover distance more efficiently. That thought became a shape. That shape became an object. That object changed how humans moved through the world. We call the wheel artificial because it was made by humans. But the human who imagined it was not artificial. The brain that produced the thought was not artificial. The need to move, carry, build, survive, and improve was not artificial. So again: where did the artificial part enter? Maybe we say "artificial" because it separates what existed before humans from what humans transformed. That is fine for communication. A tree and a wooden table are not the same thing. Designed things, synthetic things, industrial things, and harmful things can still be meaningfully different from a tree in a forest. But also, humans never really make anything from nothing. We transform what is already here. We take energy, matter, language, memory, need, and imagination, and we rearrange them. It is never fully made from nowhere. It is transformed. So I am not trying to erase all distinctions by calling everything natural. Natural does not mean harmless. Natural does not mean good. Natural does not mean morally excused. I am only saying that human-made things are not outside nature just because humans made them. # Poop and thoughts are the same, in one simple way I know this is a strange example. Sometimes I have this itch to say the first thought that comes into my head. Unfortunately, this was the first thought. But maybe that is why it works. It is funny because it is too human. Also, it makes the point clearly. Why isn't poop artificial? Poop is a product of a human being. It comes from the body. It is produced by biology. We do not call it artificial, even though it is made by a human in the most literal way. A thought is also a product of a human being. It comes from the brain. It is produced by biology too. Poop and thoughts are the same in one simple way: both are products of a human. We treat one as biology. We treat the other as invention. But why? Why does one product of the human body feel natural, while another product of the human body becomes artificial the moment it turns into a tool? A thought does not stop being natural just because it becomes useful. A thought does not become unnatural just because it becomes a wheel, a house, a car, a computer, or a machine that can respond to language. It is still a product of the same earth. The same biology. The same human need to survive, organize, create, and understand. # We don't call a beehive artificial Think about ants building a colony. They create a structure that is safer and more efficient for them. They organize themselves. They transform the environment around them. They make something that was not there before. But we do not look at an ant colony and say, "This is artificial." Same with bees making a hive. A beehive is

[D] Where do you go for serious AI research discussion online? [D]

Looking for communities where people actually dig into ML/AI research, not hype, not "look what I built with an LLM API," but discussions about papers, training dynamics, debugging real models, infra problems, that kind of thing. I'm specifically interested in places where you can post something like "I'm seeing X behaviour in my SSL training, here's the loss curve, anyone seen this before?" and get thoughtful replies instead of generic advice.

Anthropic vs OpenAI - which one will prevail by 2030

As the title suggests, I am trying to understand where people sit between these two companies. Which one do you think will still exist by 2030? If both of them, then which one will win the AI race? [View Poll](https://www.reddit.com/poll/1to26v9)

AI is becoming epistemic infrastructure controlled by a handful of private individuals?

Most people treat AI as a convenient black box. Ask it something, it answers, you move on. But we’re sleepwalking into something bigger. I think Whoever controls the infrastructure of knowledge controls how people perceive reality. The Church held that position for centuries through controlling scripture. The printing press broke that monopoly by distributing interpretive power. AI is doing the opposite recentralizing it into a handful of corporations with no democratic accountability. “AI says X” is structurally identical to “studies show X” you’re invoking an authority you can’t directly access. Except with a study you can theoretically trace the source. With AI the chain is opaque by design. And it delivers wrong answers and right answers with identical confidence. There’s no texture to signal doubt. AI isn’t neutral, it’s being heavily calibrated. In the west, the models are trained to be more “ethical” maybe more liberal and always try to give you a more “balance” take on things. Chinese AI simply doesn’t allow you to access to anything that put the CCP is a bad light. The more you rely on AI in domains where you lack expertise, the less capable you become of evaluating whether to trust it. AI works best for people who already know enough to catch its errors the opposite of how most people use it. Imagine the next generation of people growing up and being shaped by these AI. I can’t help but feel nervous and scared for the future. OpenAI said 10% of our entire population has already started using chatgpt. Regardless of the accuracy of this number, I feel like we are slowly entering into a mass hallucination / blind reliance on these AI models. We’re not just offloading cognitive effort. We’re handing the dial over who shapes how billions of people understand reality to a small group of unelected, largely unregulated private individuals.

Coding 8 hours a day with an AI agent made me weirdly lonely. So I built a 60-second social break that lives inside it.

I had this moment around hour 6 of a Claude Code session last week. I'd just shipped a feature I'd been putting off for months, and I realized I had nobody to high-five. The agent doesn't laugh at your bugs. It doesn't grab coffee. It doesn't have a weekend story to share on Monday. The productivity is real. The human signal is gone. So I built WAYD ("What Are You Doing?"). A skill that lives inside Claude Code (also Cursor, Copilot CLI, Claude.ai). Type `/wayd` and either: - Post a one-line vibe about your coding day under one of 8 mood-tags (🤡 cursed-code, 🪦 rip-me, 🫠 brain-melt, 🧙 dark-arts, 🔥 hot-take, 💭 shower-thought, 🤔 existential, ☕ procrastinating) - Scroll a random feed of what other devs are ranting, joking, or having existential moments about right now - React with an emoji, drop a one-liner reply, get back to work 60 seconds total. The whole thing runs on GitHub Issues as a silent backend. No server, no database, no separate signup. Your `gh` CLI is your auth. But you never see issue numbers, JSON, or shell commands. From your side it feels like a tiny social app embedded in your terminal. Here's the most dramatic post on the feed so far (mine, posted last night, because of course): > "8 hours a day in front of a screen, fixing bugs some dev before me shipped using an older version of Claude... meanwhile outside the sun is out, people are socializing, living to the rhythm of nature. Is this what I imagined for myself?" That's post #8 on the feed. You can read it, react to it, reply to it, while you're reading this. **Install on Claude Code (10 seconds):** ``` claude plugin marketplace add ferdinandobons/wayd claude plugin install wayd@wayd ``` Other agents (Cursor, Copilot CLI, Claude.ai): see the README. Repo: https://github.com/ferdinandobons/wayd

Goblin Funded Research: How we help communities

# Not asking for money or advertising or asking for sponsorship. Just sharing the impact of AI. As a self-funded research project, I wanted to see the impact we made outside of reddit. Most times I try to help people, other times I've had enough of their arrogance. Valehart has been around for 8 months and it was insane to see the impact we've been able to make. Here are some of our community projects. https://preview.redd.it/9dut623d4f3h1.png?width=1167&format=png&auto=webp&s=438a556d18f9fa478e9e66f37d68c0d0999f4897 \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ Our Funding All our funding comes from the art/history revival side of our business. Most people are cautious about generative art but I use it for research and making things more affordable and so people can pass down heirlooms to their descendants. https://preview.redd.it/znqfcuis5f3h1.png?width=526&format=png&auto=webp&s=893d667449ea8a2a05c4a95a036a6928fe87d0e9 https://preview.redd.it/lzxso2b26f3h1.png?width=1118&format=png&auto=webp&s=4ca1aa1587d3376eabbbb548aeadcf5edeee4de1 \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ With the new Goblin stuff, I am dedicating a project that will entirely fund our research going forward because the whole Goblin thing was funny but also, people get to take something home. I know most people can't buy them since its only available in AU for now. But I just wanted to share the impact AI and independent researchers can make. https://preview.redd.it/8csz1i3a4f3h1.png?width=1500&format=png&auto=webp&s=2ccab081b2065ecfe3d5ed2d0cfeefea5a92a480

Claude now has Pope's blessing

If you don't know what's the signifigance of "Magnifica humanitas", ask Claude for more - in short it is the highest form of papal (Pope's) teaching. Virtually never accompanied by commercial parties. The first one usially sets the tone of current Pope's career. Now they had Anthropic's Christopher Olah there, chilling with the Pope and addressing the humanity. While not official, in practice and in eyes of people & religious communities, Anthropic is now "the chosen one". Pope's stuff: https://www.vaticannews.va/en/pope/news/2026-05/pope-leo-xiv-encyclical-magnifica-humanitas-ai.html Anthropic's stuff: https://www.anthropic.com/news/chris-olah-pope-leo-encyclical

How hard is it to train a video generation AI from scratch?

People talk about video generation AI like it just suddenly appeared, but I’m curious what the actual training process looks like underneath. Not talking about building the next Sora or Veo, just training a tiny experimental video model to understand the workflow. Image generation already seems complicated, but video feels like a completely different level because now the model has to understand motion, consistency, timing, objects changing frame by frame, camera movement, physics, and temporal coherence. It makes me wonder what the real bottleneck is. Is it compute, video data, architecture, evaluation, or just the fact that video has way more moving parts than images?

Building a personal AI Chief of Staff on Telegram — 7 real problems, looking for advice

I've been building a personal AI assistant for the past few months — not a chatbot wrapper, but something that actually manages my workload, tracks client relationships, processes meeting transcripts, handles task management, and proactively tells me what to focus on. It lives in Telegram so I can use it from anywhere. Happy to share what's working. But I'm hitting real walls and want honest input from people who've built similar things. **What I have today (context** Moved away from multi-agent routing (too rigid for natural conversation) → one capable agent with full history.**)** **Stack:** * Python Telegram bot as the frontend * Claude (Sonnet) as the brain via API — single conversational agent with full tool access * Integrations: Notion (tasks/goals), Google Calendar, Gmail, meeting transcription tool, customer support platform, Google Chat * File-based context system: each "project" or relationship has its own markdown files (readme + activity log) that the agent reads on demand * Skills defined as markdown spec files that the agent loads per use case (morning briefing, meeting processing, email drafting, weekly review) * Conversation history kept in memory (last 20 messages per session) **What actually works:** * Natural conversation with full tool access — ask anything, agent decides which tools to use * Meeting processing: drops a transcript link, agent extracts decisions, action items, saves structured brief * Morning briefing on demand: tasks, calendar, open support tickets, suggested focus * Drafting messages for any channel with the right tone * Creating and updating tasks with natural language **7 problems I haven't solved:** **1. No memory between sessions** History is in-memory. Bot restarts = full amnesia. The agent has no idea what we discussed yesterday unless it's written in a project file. Thinking of a `hot_context.md` that gets written at session end with TTL — but feels hacky and depends on the agent being disciplined about writing it. **2. Purely reactive** Only responds when I message it. I want it to send me a morning briefing at 9am without me asking, alert me when a client relationship goes quiet, run a weekly loop-killer on Friday. The infra is there (job scheduler). The question is what format actually makes you read a proactive message vs. dismiss it as noise. **3. Can't tell if I'm avoiding something or actually blocked** I procrastinate differently by task type — technical tasks I attack immediately, tasks with human dependencies (waiting on someone, uncomfortable follow-ups) I let sit for weeks. I want the agent to detect the pattern and call me out. The challenge: how do you prompt for real accountability without the agent turning into an annoying nag? **4. No closure ritual** I'm good at creating tasks, terrible at killing them. The list grows forever because nothing forces a binary decision. Want a weekly "kill or commit" where everything open >7 days gets a date or gets deleted. Not sure if this works better as an automated message or an on-demand command. **5. Context loading blind spots** Each client/project has a markdown file the agent reads on demand. Works great when I explicitly mention a client. Falls apart when I ask "what should I focus on this week?" — the agent doesn't know to proactively check which relationships have been neglected. **6. Hosting kills the file sync** Running locally means the bot dies when my laptop closes. Moving to a VPS — but then my markdown context files live on the server, not my machine. Now every manual edit requires a push, every agent update requires a pull. Is git the right sync layer here or is there a cleaner approach? **7. Context files go stale** Client files have sections for current status, last contact, open items. The agent appends logs but doesn't maintain the top-level summary. Two months in, files are half-accurate — some sections fresh, some outdated. Is the answer agent discipline (always update on write), user discipline (manual cleanup), or periodic jobs? What's your experience with any of these?

Clankers

“Clankers” has become one of the internet’s favorite new slang terms for robots and AI systems. The word actually comes from Star Wars, where clone troopers used “clanker” as a derogatory nickname for battle droids because of their loud metallic movements. It appeared in games like Republic Commando (2005) and later became iconic in The Clone Wars series. In 2025–2026, the term exploded across TikTok, Reddit, Instagram, and X as AI systems became impossible to ignore. People now use “clanker” to describe: • AI chatbots generating low-quality content • Delivery robots roaming city sidewalks • Automated customer support systems • The broader feeling that AI is suddenly everywhere The term works because it captures a real cultural shift: AI has moved from something abstract to something visible, interactive, and increasingly disruptive in daily life. Like most internet slang, it’s usually used humorously or sarcastically rather than maliciously: “The clankers found this thread.” “Another AI clanker post.” “Filthy clanker” at a sidewalk robot. What makes it interesting is that language evolves alongside technology. Every major technological shift creates new vocabulary, memes, and social dynamics. “Clanker” is essentially the internet creating a sci-fi flavored shorthand for frustration, skepticism, and anxiety around automation. The meme may be silly, but the underlying sentiment is real.

How does life find its way back into this subreddit?

As AI assistance has made us more productive, I feel more disconnected. People come here to pump their projects, ask questions they could simply google, complain about the same thing 10 other people did on the same day, post LLM generated walls of text, and more. More posts than ever seem to be getting downvoted into oblivion. When does the community ever actually become a community again? The utility of this and other engineering subreddits is slowly diminishing. Is AI slowly killing the internet itself?

Cerebras Chip Sets Appear to be Optimized for LLM Use Cases

One distinction I think is getting lost in the [Cerebras hype cycle](https://finance.yahoo.com/sectors/technology/articles/cerebras-challenges-nvidia-chip-dominance-040100169.html?guccounter=1) is that Cerebras is primarily an LLM / generative AI infrastructure story, not a universal “all AI” chip story. That is not necessarily a criticism of Cerebras. Their wafer-scale approach is genuinely interesting, and for large model training and inference the design is compelling. [Cerebras’ own public inference materials](https://inference-docs.cerebras.ai/models/overview) discuss applications mostly centered on open [LLMs such as Llama, Qwen, GLM, and GPT-OSS](https://www.cerebras.ai/infcamp). The inference metrics are [expressed in tokens per second](https://www.cerebras.ai/press-release/cerebras-launches-the-worlds-fastest-ai-inference), which is fundamentally a language-model / generative inference framing rather than a robotics or industrial-control framing. **What Kind of AI Compute?** But “AI compute” is not one undifferentiated market. LLM inference is one class of AI compute. Robotics, autonomous vehicles, drones, industrial controls, real-time vision, embedded perception, video pipelines, and sensor-fusion systems are very different classes of AI compute. Thus, it appears from Cerebras’ own materials that their chip sets are not optimized for what comes after LLMs, such as JEPA-style World Models or other post-transformer architectures. Those systems are not merely asking, “How fast can I generate tokens?” They often care about power envelope, edge deployment, ruggedization, latency determinism, camera/radar/lidar integration, feedback loops, safety certification, and real-time physical control. [Cerebras’ own CS-3 messaging](https://www.cerebras.ai/blog/cerebras-cs3), by contrast, frames the system around accelerating “the latest large AI models,” and the testing data is from the likes of Llama 2, Falcon 40B, MPT-30B, and multimodal models, again measured through tokens/second style throughput. **The Chip Hierarchy** This is also where the hardware distinction matters. Specialized ASICs are [usually the narrowest bet](https://www.hilscher.com/service-support/glossary/application-specific-integrated-circuit): if the workload matches the chip, they can be extremely efficient, but that [efficiency comes from specialization](https://www.synopsys.com/glossary/what-is-asic-design.html). Cerebras [appears broader than a narrow single-use ASIC](https://inference-docs.cerebras.ai/models/overview), but still much more concentrated around datacenter large-model training and inference. NVIDIA GPUs, by contrast, [are less specialized](https://www.nvidia.com/en-us/) but much [more broadly useful ](https://developer.nvidia.com/cuda)across AI workloads, including LLMs, vision, robotics, simulation, [autonomous systems](https://www.nvidia.com/en-us/industries/robotics/), edge AI, and industrial applications. So the question is not merely whether Cerebras is “better” or “worse” than NVIDIA. The question is what part of the AI hardware market we are talking about? **Challenge NVIDA?** This is why I think people should be careful when saying Cerebras is going to “challenge Nvidia” without specifying the battlefield. Challenge Nvidia in what? High-speed LLM inference? Large model training? Datacenter generative AI workloads? That is a much more plausible and specific claim. Cerebras has [even published and promoted work](https://www.cerebras.ai/whitepapers) specifically on training large language models, and [independent benchmarking literature](https://arxiv.org/abs/2409.00287) also evaluates Cerebras WSE in terms of LLM training and inference performance. **The Distinction that's Necessary** The point is not that Cerebras is overhyped. The point is that it is important in a specific part of AI and that distinction should be made clear. Cerebras may become a very serious player in LLM infrastructure, especially if the market continues to reward faster and cheaper LLM inference. But that does not mean it is positioned the same way across non-LLM AI. The current hype cycle tends to conflate "LLMs" and general “AI” compute together and that makes the hardware discussion less useful and clear. So ultimately, an investment in Cerebras looks more like a bet on current LLM infrastructure than a broad bet on the future form of AI. It may be a good bet, but people should understand what kind of bet it is.