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Per Tech Times: “More U.S. businesses paid for Anthropic's Claude than for OpenAI's ChatGPT in April 2026 — the first time in the AI industry's short history […] Anthropic's annualised revenue run rate crossed $30 billion in early April 2026, up from roughly $9 billion at the end of 2025, placing it above the approximately $24 to $25 billion annualised figure OpenAI reported at the same time. More than 1,000 enterprise customers now spend over $1 million annually on Anthropic products — a number that doubled in under two months after the company's $30 billion Series G raise in February 2026. Eight of the Fortune 10 are now Claude customers, according to Anthropic.” submitted by /u/StarlightDown [link] [comments]

submitted by /u/danie-l [link] [comments]

Hey everyone, Most people build 8-bit computers to run Pong or Tetris. I wanted to see if I could push a custom 8-bit architecture to do something much harder: train a neural network from scratch. I built VirtualPC, an open-source 8-bit computer system simulated from basic NAND gates up to a functional CPU that can train a small neural net from a folder on your computer. Repository: https://github.com/ninjahawk/VirtualPC › The ML Core Instead of importing PyTorch, everything happens at the bare-metal assembly level: Custom ISA: The Instruction Set Architecture was designed to handle the math needed for machine learning. Low-Level Training: The CPU executes forward and backward passes directly through custom assembly code. Matrix Math on 8-bit: Overcoming severe memory limits using disk-bac...

Every week there's a new paper or tweet claiming some model "understands" context, "reasons" about math, or "knows" what it doesn't know. But when you look closely, there's almost no consensus on what "understanding" even means — philosophically or empirically. Searle's Chinese Room argument is 40 years old and still hasn't been cleanly resolved. The "stochastic parrot" framing treats token prediction as the ceiling. Integrated Information Theory would say current architectures are near-zero in phi. And yet GPT-4 passes the bar exam. A few questions I've been sitting with: Is "understanding" even the right frame — or is it a folk-psychology term we're forcing onto a system that operates on completely different principles? Does it matter if a model "truly understands" if the outputs are in...

I think we’re underestimating how chaotic enterprise AI adoption actually is inside large companies. From the outside, it looks simple: buy better models add copilots automate workflows deploy AI agents increase productivity But inside many enterprises, CIOs and CTOs are dealing with a much deeper problem: The organization itself is fragmented. Customer data exists across: CRM systems billing platforms support tools spreadsheets emails regional databases legacy systems nobody fully understands anymore And every system describes the “same customer” differently. Then leadership says: “Scale AI faster.” But scale AI on top of what exactly? Which system represents reality correctly? The CRM? The support history? The risk engine? The finance system? The employee’s undocumented tribal knowle...

I came across a Stanford research paper that actually went inside companies running AI in production - not pilots, not surveys, real deployments. They found something that stuck with me. Companies using what they call "agentic AI" - where the AI owns the task start to finish with no human approval loop - are seeing 71% median productivity gains. Companies using standard AI that assists humans are averaging 40%. Same technology. Nearly double the output. The kicker: only 20% of companies are in the 71% group. A few things that stood out from the actual data: A supermarket replaced its entire buying process with AI - waste down 40%, stockouts down 80%, profit margin doubled A security team went from 1,500 alerts/month to 40,000 with the same headcount Stanford identified 3 conditions requir...

submitted by /u/UberDrive [link] [comments]

I think one of the biggest AI risks may be starting to flip. Earlier, the fear was: “What if AI is wrong too often?” But now I think the deeper risk may become: “What happens when AI becomes right often enough that humans stop meaningfully questioning it?” In many enterprise systems, oversight slowly changes shape. At first: humans review everything carefully. Then: they review only exceptions. Then: they skim explanations. Then: they approve unless something looks obviously wrong. Eventually, oversight becomes routine instead of judgment. That creates what I’m calling the Trust–Oversight Paradox: More AI accuracy → more human trust → less meaningful scrutiny → harder governance when failure finally happens. And the dangerous part is: high-performing AI can still fail through: incomplete ...

submitted by /u/Tiny-Independent273 [link] [comments]