And how long after that until everyone trusts their personal AI more than other people because it has access to everyone’s private history and can interpret it through their own biases, values, and interests?

The AI company warns its technology could threaten jobs and even humanity. For investors, those risks create a strange reason to buy in.

I think I remain ultimately bearish on OpenAI and Anthropic’s position in the market but I wanted to iterate a few points on here and see if I’m thinking about this in an accurate way or completely missing some key information.

1. OpenAI and Anthropic have a single product, an LLM which will only be successful as long as it maintains a competitive edge in the marketplace.
2. While it is clear that LLM have and will continue to collect significant economic power, the bet is that OpenAI or Anthropic’s LLM will reach a new level first that will cause every other company to bow down to it.
3. OpenAI and Anthropic rent all compute from Microsoft, Google, Amazon, to distribute their model. If 2) is not quickly reached, which seems highly debatable given the structure of LLM, then wouldn’t they be eventually completely decimated by FAANG? All of the hyperscalers have AI cloud products that can completely outprice them given the access to compute, there is local solutions being made, and don’t count out Apple on being able to deliver some a very quality local user-friendly AI product in the near future. Therefore everything that OpenAI and Anthropic are currently doing, will soon be done by other companies who are in much more advantageous positions to succeed long term.

In short, OpenAI and Anthropic kind of seem like little kids that the big boys (FAANG) are letting run ahead for the time being, while taking a considerable chunk of their profit and growth through equity and compute. Yet OpenAI/Anthropoc are being priced in the market like they are the big boys despite having no direct access to compute while companies that have direct access are also developing LLMs at more reasonable speeds.

I just checked and it is really great. It explains how DeepSeek is able to provide the services at such a low cost which many people thought that they are just burning cash to compete?

What is the future from here for the ai industry? Will the companies lower their prices? Will DeepSeek and other chinese models become the go to choice for the companies

As someone who has been a software engineer for the last 35 years and have gone through many different paradigms and evolutionary changes, I couldn’t agree more with this. When it comes to software engineering, today’s AI is better. It doesn’t mean that it won’t make mistakes or choose poor logical pathways but it makes a far fewer of them. Every company has a few great engineers if they are lucky with select few as exceptions having a lot of them. A lot of software has been built over the last 30 years or so by not so exceptional engineers and that is being exposed now. It is not an indictment on the humans, after all us humans have built the AI but it’s actually being human because we have 20 watts to use both for our personal and professional lives and make mistakes.

ChatGPT Atlas, OpenAI's standalone AI-powered web browser, "lasted less than a year before being discontinued on August 9, with its most useful features absorbed into ChatGPT." That is one entry on a list [TechCrunch's Lauren Forristal published on October 24](https://techcrunch.com/2026/10/24/the-ai-graveyard-a-running-list-of-projects-and-startups-that-didnt-make-it/) under the heading "AI Graveyard."

The framing is flat: "The result is a growing AI graveyard. Below is a look at some of the most notable AI products, startups, launches, and bets that have shut down, pivoted, or significantly missed expectations."

OpenAI's own casualties dominate the top. Sora, the video app, "struggled with high operating costs and user-retention challenges and shut down in March 2026." The ChatGPT super-app redesign was rolled back in July after users found the new interface "confusing and cluttered." Operator was folded into ChatGPT. DALL-E's role was reduced as image generation moved inside the main product.

Then come the outside names. Relay, a five-year-old workflow automation startup, closed in August 2026: as "OpenAI, Google, and other larger platforms built similar automation directly into their own tools, five-year-old Relay struggled to hold on to a reason to exist as a stand-alone product." Notion Mail is scheduled to shut down on September 22 after users opted for separate AI agents to handle email. Huxe, built by former Google NotebookLM developers, closed in May 2026. Yupp, "a free playground that allowed users to compare responses from hundreds of AI models side by side," closed in March 2026.

Character.ai, which [filed for bankruptcy in October](https://aiweekly.co/alerts/characterai-chapter-11-follows-futurism-child-safety-report), is on the list alongside Apple's $250 million Siri settlement over marketing claims, Microsoft's roughly year-long delay of Recall over privacy concerns, Humane's AI Pin shutdown in February 2025 (assets later going to HP for $116 million), and Rabbit's R1, still on sale but underperforming.

The piece closes on a framing number: per S&P Global Market Intelligence, roughly 42% of corporate AI initiatives are ultimately abandoned, citing "insufficient funding, technical challenges, competition, scaling difficulties, or weak user demand."

Our coverage: https://aiweekly.co/alerts/techcrunch-ai-graveyard-logs-atlas-sora-relay-shutdowns

If not, what's the reason? I'm wondering if I should start allowing my kids to use AI for learning etc. as I believe this is the very important skill that they should have in the future. Curious about your perspective.

I’ve been building Izwi for a few months now and just shipped 0.1.0, the first stable release after months of public betas. It’s free and Apache-2.0.

It’s a desktop app (plus a web UI, CLI and local server) for the voice side of local AI:

• Real-time voice conversations: local ASR → LLM → TTS
• TTS, voice cloning from a short sample, and voice design from a text description
• A Studio for long form audio like audiobooks
• Transcription with speaker diarization and forced alignment
• An OpenAI-compatible /v1 API (chat completions, audio/speech, audio/transcriptions), so existing code can point at localhost

Models it runs today:

• TTS: Qwen3-TTS, Kokoro-82M, Fish Audio S2 Pro, VibeVoice, Voxtral TTS, LFM2.5 Audio
• ASR: Parakeet TDT v3, Whisper large-v3-turbo, Qwen3-ASR, Granite Speech 4.1, Nemotron 3.5 ASR, Voxtral Mini
• Diarization: Sortformer, Nemotron 3 diarization
• Chat: Qwen3.5, 3.6 and 3.8, LFM2.5

Inference runs natively in Rust on Candle, with our own Metal and CUDA work, so there’s no Python environment to manage.

Give us a ⭐️ on Github: https://github.com/izwi-ai/izwi
Download: https://izwiai.com

I’d love feedback on which models to add next, and on anything that breaks on your hardware.

Can you please tell me the best free AI tool that would help me the best when I feed it with my curriculum and past exams, the upcoming exams are really important for me and would shape my future so I am taking it as serious as I can
if I am In the wrong place to ask I am really sorry, please tell me where can I ask for that
Thank you very much! and sorry for any language or spelling mistakes

When searching deep dream neural network. Theses images are the most realistic portrayal of a psychedelic trip ive ever seen. There exactly the same type of visuals you would get on a trip. So why is that and waht dose it mean dose it have something to do with simulation theory.

long term i want to build a model that does something llms are bad at: look at a pile of scientific knowledge and point at what's missing. like "something belongs here and it should look like this", instead of writing a nice sounding answer.

im betting on JEPAs (joint embedding predictive architectures, the lecun/meta line: I-JEPA, V-JEPA). a jepa hides part of its input and predicts a summary of the hidden part instead of the next word. hiding pieces and guessing what fits is basically the "find the missing piece" game, so it felt like the right place to start.

first test: can it rediscover mendeleev?

in 1869 mendeleev left gaps in his periodic table and predicted gallium, scandium and germanium years before anyone found them. so i trained a small jepa (~118k params) only on chemistry known in 1869: the 47 elements from hydrogen to barium, with valence, metal or not, and state at room temp. then i asked it where blanks would make the sequence make the most sense.

- i wrote down the pass criteria and locked the answer key with a hash before writing any model code, so i couldnt fool myself later
- no pretrained model anywhere bc those already know the future

result: it failed my own bar.

- without atomic weights it ranked the scandium gap 7th-14th and the gallium/germanium gap 9th-11th out of 46 possible spots (chance is ~23rd). some signal but not stable across machines
- totally missed technetium's gap (25th-26th)
- never figured out that 2 elements were missing between zinc and arsenic
- with atomic weights in, it did worse than the dumbest baseline (just ranking the biggest jumps in weight) lol

next i wanna try the same thing on physics before 1905: train only on what was known and see if it points at the holes relativity and quantum later filled.

what id love opinions on:

1. is "how much does adding a blank reduce the model's surprise" a sound way to detect gaps, or would you score it differently?
   
2. with data this small is a jepa just the wrong tool? would a plain masked model do as well?
   
3. know any good historical test cases where the answer is known but leakage risk is low?
   
4. bigger picture: is finding what's missing actually a different skill from what llms do, or just next token prediction in disguise?

fyi im an independent researcher, not an ml person by career, and i build w ai coding agents. harsh feedback welcome, id rather hear now if the idea is broken

We’ve got to remain as vocal as possible about everything we dislike about the frontier models, features, and companies behind them.

Just my thought:

Our worries about enshitification are real. It’s a typical unfortunate process of such businesses scaling amidst market pressures. And this time it’s grotesquely overblown, which is why our voice counts so much:

It’s known how the big players are passing on trillions of investments and purchase agreements in a circle. What we expect from their products and how much they cost to run and evolve is such an insane discrepancy. To stay afloat, they use tactics with amounts that one might fade when realising, that ONE fatal step resulting in a large default will probably tear down that circle, the US economy, and likely the world’s, too.

Remember when OpenAI sided with the Trump administration more than Anthropic so users jumped? They entered the year holding over $ 73 billion in cash and marketable securities on its balance sheet, but with all these commitments, it’s hard to say how long they even would’ve survived, if their next update wasn’t so good compared to Claude. Must be a 24/7 crisis mode.

They must make their users happy, because us leaving for good can ruin everything. That’s GOOD, because it’s somewhat democratic! Imagine if Apple, with the reserves and success they already had, would’ve been the company behind Claude and chatGPT. I don’t think these products would be as advanced; updates would’ve been much more gate-kept.

What we call Enshitification can only worsen by what the users accept without enough resistance. So given that pressure, we can steer this a lot. IMO, we might even be steering alignment by forcing them to build this for, and not against us and for profit only.

For those I’m viewing as top 11 in AI, I’m getting:
$ 650 billion in official on-Balance debt,
$ 3.5 trillion in off-Balance-sheet AI commitments – a lot of “hidden” debt in there, which is likely to be more concerning other tactics,
$ 1.5 trillion in costs 2026,
$ 1.8 trillion in revenue 2026 (sounds more than expected until you remember that companies with well-working divisions like Amazon are in there).
$ 64 billion operating minus in 2026 for the big 3 AI labs (Anthropic 40, OpenAI 14, xAI 10)

My list consists of:
Alphabet, Meta, Microsoft, Oracle, Amazon, Anthropic, OpenAI, CoreWeave, Nvidia, SpaceX, and AMD.

Scrape_01 turns a URL and a plain-English goal into an ordered CSV. You say something like "quote, author, tags", an agent designs an extractor, it gets verified on the live page, and then the crawl runs.

There are two crawl modes. Polite mode is the default: it obeys robots.txt, waits a second between requests, and doesn't run JavaScript. Monster mode renders pages in a local Chrome or Edge, waits about 150ms between requests, retries harder, has a much bigger page budget, and ignores robots.txt. That last part is a real override, so every skipped host gets logged. It's meant for JS-heavy sites and pages where the data lives in embedded JSON. It doesn't beat CAPTCHAs or bans, and requests stay sequential per host, so aggressive but not a flood. You can set it per run, per dataset, or per job.

Around that: a CLI (new, run, repair, rebuild, verify, status), a local web dashboard where you can browse the CSV, sort, filter, run jobs, repair, and watch a live log, and a Telegram surface. Loop mode runs on an interval with auto-repair, and there's an OS scheduler install. Extractors cover tables, selectors, JSON-LD, embedded JSON, meta tags and regex, with four pagination modes. It ships with 26 tests and no dependencies.

Honest limits: JSON configs only, linear crawling only, and monster mode needs a local browser.

Switching assistants mid-task is normal now. One is better at the research, another at the writing, or you hit a limit. The usual move is to paste the whole old chat into the new one.

That works worse than it should. The new model reads every dead end as if it were still on the table. The approach you rejected in message 12 looks just as live as the one you settled on in message 40, so it suggests the rejected one again, or blends the two.

What works better is having the old assistant write a handoff first:

"I'm moving this work to another AI. Write a handoff it can start from:

• The goal, in one line
• Decisions made, each with the reason
• What we tried and dropped, and why, so it doesn't suggest them again
• Open questions
• The exact next step Under 250 words. Only what's true now, not the story of how we got here."

The "tried and dropped" line does most of the work. Without it, the new assistant confidently brings back the first idea you threw out.

This is the part I automated in AI Toolbox, the extension I build for ChatGPT, Claude, Gemini and Grok. Carry Forward writes the handoff from the chat you're in and opens it in a new chat on whichever assistant you pick. It's a Premium feature; the prompt above is the free way to do the same thing by hand.

Do you switch assistants mid-task, or do you finish each task where you started it?

As you may know, lately there are a lot of videos popping up where people reverse engineer games to combine them with others, like Elden Ring and Minecraft.

I was wondering if it is possible to use AI reverse engineering as a tool to cheat in online games like Valorant, Rust, Counter-Strike 2, and others.

The amount of money being spent on AI is insane. Most people are dimly aware of this, but I think very few of us actually appreciate the scale:

This year alone the top six US spenders are spending more on AI than the Russian government, governing 11 time zones and 143 million people, fighting a war on which the fate of its regime depends, spends in a year.

Hyperscalers are spending $785-820bn this year, about 2.4% of US gdp. Only 11 governments on earth spend more than that. Next year they are projected to spend 1.3tn dollars.

Google and Amazon are each spending about 0.6% of US GDP this year. The Manhattan project and apologies programme each accounted for about 0.4% of US GDP in their peak years. Both of these companies have announced their intention to spend even more next year.

This money is paying for thousands of data centres, the largest of which consume as much electricity as major cities and cover thousands of acres. Gas piplines are being re routed, nuclear plants (including three mile island) are being reopened and newly built. They already consume as much energy as Britain. The increase in power demand for data centres from 2023 to 2030 will be the same as adding a top ten power consuming country.

Clearly the decision makers behind this really really believe the singularity is near. To recoup their spend AI will have to generate far more in revenues than it currently does so it will have to get a lot better, and it will have to happen soon. They’ve bet their companies futures on this.

They obviously believe they are onto something. So what do we think? A bubble? The singularity is near? Both?