Yesterday's AI - October 26, 2025
Another week, another dozen companies burning through venture capital faster than their AI models can generate excuses. But hey, at least you’re here reading about it a day late, which means you can feel smugly informed without the anxiety of real-time FOMO.
I’ve organized this week’s chaos into digestible sections:
General News - the stuff everyone’s talking about (or should be)
Big Money Deals - watch VCs throw billions at the wall to see what sticks
Technical - for when you want to sound smart at standup
Skeptical - because someone needs to be the adult in the room
Pick your section, share with your team, argue about my takes in the comments. Let’s dive in.
📰 GENERAL NEWS
The AI Browser Invasion: When Your Browser Becomes Your Worst Enemy
OpenAI Launches ChatGPT Atlas Browser
OpenAI is launching ChatGPT Atlas, an AI-powered web browser that aims to compete directly with Google Chrome. The browser integrates ChatGPT capabilities natively into the browsing experience, allowing users to interact with web content through natural language.
Microsoft Edge’s Copilot Mode Goes Live
Just two days after OpenAI’s Atlas announcement, Microsoft officially launched ‘Copilot Mode’ in Edge browser. The new mode transforms each new tab into a chat interface where users can ask questions, search, or enter URLs directly through Copilot, combining search, URL navigation, and AI assistance.
Google Earth Gets AI-Powered with Gemini
Google is enhancing Google Earth with expanded AI capabilities powered by Gemini. The update improves the platform’s ability to answer questions about geographical features and assess environmental risks through improved geospatial reasoning.
My take: We need to talk about AI browsers, because this isn’t just another feature launch - it’s a security disaster in the making. Not so long ago, Perplexity released Comet, their AI browser, and now everyone’s rushing to follow suit. OpenAI dropped Atlas, Microsoft cloned it in 48 hours with Copilot Mode, and suddenly every tech giant wants an AI that can browse the web for you.
Here’s the problem: traditional browsers are passive - they display what’s on the page.
AI browsers are active - they read, interpret, and act on content. That fundamental shift creates attack vectors that make traditional XSS look like child’s play. Security researchers have already demonstrated how AI browsers like Comet can be hijacked through malicious web content. Just embed hidden commands in webpage text, and boom - your AI browser is now working for someone else.
The scary part? These companies are shipping this stuff to millions of users before anyone’s figured out the security model. When your browser can autonomously click buttons, fill forms, and make purchases based on what it “sees” on a webpage, you’ve essentially given every website potential control over your digital life. And unlike traditional security bugs that need patches, this is a fundamental design flaw. The AI browsers we’re building don’t just have vulnerabilities - they are vulnerabilities.
Microsoft launching their version two days after OpenAI tells you everything: nobody’s thinking about security, they’re thinking about market share. We’re speedrunning from “cool demo” to “catastrophic security incident” and nobody’s pumping the brakes.
Claude Code Goes Web and Mobile
Anthropic has expanded Claude Code to web and mobile platforms (iOS preview), allowing developers to run parallel coding tasks in the cloud. The service, previously only available via terminal and IDE extensions, now offers asynchronous capabilities and isolated sandbox environments for code execution. The expansion matches competitive features from OpenAI’s offerings and Google’s coding agents.
My take: Anthropic looked at their terminal-only Claude Code dominance and said “you know what would make this better? Letting people code on their phones.” The parallel task execution across multiple repositories is legitimately useful for developers managing complex projects. The isolated sandbox environments are smart too - nobody wants their AI accidentally rm -rf / their production server. But let’s be honest, the real story is the arms race: OpenAI has Codex, Google has AI Studio, and Anthropic needs to be everywhere they are. The fact that you can now launch coding jobs from your phone while pretending to listen in meetings? That’s either the future of productivity or the death of work-life balance. Probably both.
Claude Gets Memory (Finally)
Anthropic has expanded Claude’s memory feature to all paid subscribers. The update allows Claude to maintain context across different conversations and enables users to review and modify the information Claude retains through audit and edit capabilities.
My take: Your AI assistant can now remember everything you’ve ever told it, and you can check what it remembers. This is either incredibly useful or deeply creepy depending on how much you trust Anthropic with your conversational history. The audit and edit capabilities are key - at least you can see and delete what the AI knows about you. Unlike, you know, every other tech company that just hoovers up your data and says “trust us.” The fact this is limited to paid subscribers tells you memory isn’t cheap to store and process. Wonder how long until someone discovers Claude remembered something it absolutely shouldn’t have?
Meta Cutting 600 AI Jobs to “Move Faster”
Meta is laying off approximately 600 employees working in AI-related roles, citing a need to move faster and streamline operations. The cuts appear to be part of Meta’s broader restructuring efforts in its AI division.
My take: Meta’s laying off 600 AI workers to “move faster” which is corporate speak for “we hired too many people during the hype cycle and now we’re fixing it.” The irony of cutting AI jobs while everyone else is in a talent war throwing billion-dollar packages at researchers isn’t lost on me. Either Meta knows something the market doesn’t (bubble’s bursting) or they’re making a spectacular mistake. Mark Zuckerberg’s pivot from metaverse to AI to... leaner AI? The man’s got whiplash and so does his org chart.
Wikipedia Traffic Declining Due to AI Search
Wikipedia reports declining traffic due to AI-powered search engines providing direct summaries and competition from social video platforms. This represents a significant shift in how users access information, potentially impacting Wikipedia’s role as a primary knowledge source.
My take: Wikipedia, the internet’s crowdsourced encyclopedia that we all rely on but nobody pays for, is getting killed by AI that was trained on... Wikipedia. The irony is so thick you could cut it with a knife. AI search engines scrape Wikipedia’s content, summarize it, and serve it to users who never actually visit the site. So Wikipedia gets none of the traffic, none of the ad revenue (wait, they don’t have ads), and none of the new contributors. But hey, at least the AI companies got really valuable training data for free! This is the “AI value extraction problem” in perfect miniature: take public goods, monetize them, return nothing to the commons.
OpenAI Launches Company Knowledge Integration
OpenAI launches ‘company knowledge’ feature for ChatGPT Business, Enterprise, and Edu subscribers, allowing integration with workplace apps like Slack, Google Drive, and GitHub. Powered by a version of GPT-5, the feature searches across multiple data sources while maintaining enterprise-grade security and compliance controls, with citations and admin controls for permissions.
My take: OpenAI is basically saying “give us access to all your company’s internal data and we’ll make it searchable through ChatGPT.” The enterprise security theater around this is impressive - citations! Permissions! Compliance! - but the fundamental question remains: do you trust OpenAI with your company’s internal Slack messages, Google Docs, and GitHub repos? The fact this uses GPT-5 under the hood is buried in the announcement, but it’s the real story. Your company’s private data is being used to test their most advanced model. Bold strategy, let’s see if it pays off.
In Brief: The Rest of the Week
YouTube Launches Likeness Detection - YouTube released AI technology that detects creator likenesses in content, allowing creators to request removal of AI-generated videos using their face or voice without permission. About time, but good luck enforcing it.
Yelp’s AI Everything - Yelp introduced AI-powered menu scanning (see photos of dishes by pointing your phone at menus) and AI phone systems (Yelp Host and Receptionist) to handle restaurant reservations 24/7. AI is coming for your hostess job, and it doesn’t need smoke breaks.
Amazon’s “Help Me Decide” Button - Amazon adds an AI feature that analyzes your browsing history and preferences to recommend products. Because what shopping really needed was more algorithmic manipulation disguised as helpful assistance.
Microsoft Copilot Gets 12 Updates - Microsoft announced 12 major updates including a new AI character called Mico (Clippy’s spiritual successor), Groups feature allowing 32-person collaborative AI sessions, and integration of Microsoft’s own MAI models. Clippy is back and this time it’s powered by AI. We’re doomed.
Sora Update Coming - OpenAI is releasing updates to Sora with pet-focused video generation, social features, video editing tools, and an upcoming Android version. Your AI-generated pet videos are about to get a lot weirder.
Google’s AI Scheduler - Google Research developed an AI system to optimize virtual machine placement and resource allocation in cloud data centers using machine learning. Finally, AI doing something actually useful instead of generating marketing copy.
💰 BIG MONEY DEALS
LangChain Hits $1.25B Valuation
LangChain, the company behind the open-source framework for building AI agents, has reached unicorn status with a $1.25B valuation. The framework has gained significant traction in the AI development community.
My take: An open-source AI agent framework just became a unicorn. Let that sink in. LangChain gives away their core product for free and is somehow worth $1.25 billion. The business model is presumably “be critical infrastructure for AI agents, figure out monetization later.” Classic Silicon Valley playbook. To be fair, LangChain is genuinely useful and every AI developer uses it, but turning “everyone uses our free thing” into “sustainable business” has killed better companies. The valuation is betting that AI agents are the future and LangChain will be the picks-and-shovels. Hope they’re right, because otherwise this is a very expensive open-source project.
Sesame Raises $250M for AI Smart Glasses
Sesame, a startup founded by former Oculus CEO Brendan Iribe, has raised $250M from Sequoia and Spark Capital. The company is developing AI-powered smart glasses with conversational capabilities and has launched an invite-only iOS beta.
My take: The Oculus founders looked at Meta’s smart glasses and said “we can do that but with better AI.” $250M from top-tier VCs suggests they might be onto something, or VCs are just throwing money at anyone with “AI” and “former Oculus” in their pitch deck. Probably both. The smart glasses market has been “almost there” for a decade - Google Glass flopped, Snap Spectacles flopped, even Meta’s Ray-Bans are more “neat demo” than “must-have device.” Can AI make the difference? Maybe. Or maybe we’re about to see another $250M learn that people don’t actually want computers on their faces.
Fal AI Reportedly Raises at $4B+ Valuation
Fal AI, a multimodal AI startup offering 600+ AI models across various modalities, has reportedly raised funding at a valuation exceeding $4 billion. The company operates cloud infrastructure with thousands of Nvidia’s latest GPUs focused on fast inference.
My take: A startup that runs other people’s models in the cloud is worth $4 billion. The AI infrastructure gold rush is real. Fal AI’s pitch is basically “we made it easy to run AI models fast” which in today’s market is apparently worth four billion dollars. The 600+ models claim is clever marketing - they’re aggregating the ecosystem and providing the pipes. It’s AWS for AI, which is either brilliant or commoditized in two years when AWS, Google Cloud, and Azure eat their lunch.
OpenEvidence Raises $200M at $6B for Medical AI
OpenEvidence, an AI platform for medical professionals trained on medical journals, has secured $200M at a $6B valuation. The platform provides evidence-based answers for patient treatment, offering free access to verified medical professionals through an ad-supported model.
My take: ChatGPT for doctors is worth $6 billion and it’s ad-supported. Let me get this straight - you’re asking doctors to use an AI for medical decisions and the business model is... showing them ads? The free access for verified medical professionals is smart distribution, but ad-supported medical AI feels like a dystopian business model. “Your patient might have cancer, but first, a word from our sponsor!” The $6B valuation suggests investors believe this is the future of medical information. Guess we’ll find out when the first malpractice lawsuit lands.
The Big Deals Roundup
Veeam Acquires Securiti AI for $1.7B - Data management meets AI security in a $1.7 billion deal. Someone’s betting on the “AI security crisis” thesis.
Sumble Emerges with $38.5M - Kaggle’s founders secured $38.5M for their AI-powered sales intelligence platform. Ex-Google people starting companies and immediately raising tens of millions? Shocking.
Serval Raises $47M for IT Service Management - AI agents for automating IT operations. The $47M says VCs believe AI can solve the helpdesk ticket backlog. Spoiler: it can’t.
Nexos.ai Raises €30M - Nord Security co-founders secured €30M for an AI orchestration platform focused on secure enterprise AI adoption. Europe’s trying to have an AI industry too!
Cercli Raises $12M Series A - Dubai-based YC alum building AI-powered enterprise system for MENA region. The AI gold rush has gone global.
Palantir-Lumen $200M Partnership - Palantir and Lumen Technologies formed a $200M partnership for enterprise AI services. Two companies you forgot existed team up for AI relevance.
Google-Anthropic Partnership Expands - Google and Anthropic dramatically expanded their partnership focusing on cloud computing and chip technology. Strategic vendor diversification or desperate hedge against Nvidia dominance? Yes.
Anthropic’s Billion-Dollar TPU Expansion - Anthropic announced plans to deploy up to one million TPUs with gigawatt capacity by 2026, valued in tens of billions. The infrastructure spending is getting absurd.
OpenAI Acquires Sky (Three Times Over) - OpenAI acquired Software Applications Inc., maker of Sky, a Mac interface for AI. This got announced so many times I had to check if we were in a time loop.
Periodic Labs Sets Off $300M VC Frenzy - Top OpenAI/Google Brain researchers leaving to start Periodic Labs attracted $300M from VCs. The talent war continues.
Tensormesh Raises $4.5M - AI inference optimization startup claims 10x efficiency improvements. Everyone’s optimizing inference because compute costs are eating everyone alive.
🔬 TECHNICAL
OCR Gets Supercharged: Two Major Breakthroughs
DeepSeek’s 10x Text Compression Through Images
DeepSeek released an open-source model that compresses text through visual representation up to 10x more efficiently than traditional text tokens. Achieving 97% accuracy in OCR tasks, the model could enable language models with context windows of up to 10 million tokens. The system processes 200,000 pages per day on a single GPU and was trained on 30M PDF pages across 100 languages.
Allen Institute’s olmOCR 2
Allen Institute for AI announced olmOCR 2, claiming state-of-the-art performance for processing English-language digitized print documents. The model focuses on unit test rewards for document OCR tasks.
My take: Two major OCR breakthroughs in one week isn’t a coincidence - it’s a race. DeepSeek’s approach of treating text as images to achieve 10x compression is genuinely clever. If it works at scale, we’re talking about LLMs with 10 million token context windows, which is “process entire codebases” territory. The 200,000 pages per day on a single GPU is wild - that’s enterprise document processing without the enterprise compute budget.
Allen Institute’s olmOCR 2 is playing the traditional “we have the best benchmark scores” game, but the unit test rewards system is interesting. They’re basically training the model to be verifiably good at OCR, not just statistically good.
The bigger picture: OCR was “solved” five years ago, except it wasn’t, and now we’re solving it again with LLMs. Every time someone says an AI problem is “solved,” someone else comes along with a 10x improvement. The document processing market is about to get disrupted hard, which is bad news for everyone selling enterprise OCR solutions and great news for anyone who’s ever tried to extract text from a PDF. Still though, 97% accuracy means 3% of your text is wrong, and good luck finding which 3%.
Microsoft’s SentinelStep: Building Agents That Wait
Microsoft Research introduces SentinelStep, a system enabling AI agents to perform long-running monitoring tasks efficiently. The technology manages agent scheduling and context preservation for tasks like email monitoring and price tracking, optimizing resource usage.
My take: Finally, someone’s working on the boring but critical part of AI agents: how to make them wait around and check things periodically without burning through compute. SentinelStep is solving the “wake me when something happens” problem for AI agents. This is infrastructure work - unglamorous, but essential if you want AI agents that monitor your email for important messages without running a GPU 24/7. The context preservation is key: the agent needs to remember why it’s watching and what it’s looking for across potentially long time gaps. If this works, we’ll see a wave of “AI that watches X and tells you when Y happens” applications. Set it and forget it, AI edition.
Qwen’s Deep Research Gets Multi-Format Output
Alibaba’s Qwen Team released a major update enabling automatic conversion of research reports into interactive webpages and multi-speaker podcasts. Integrating Qwen3-Coder, Qwen-Image, and Qwen3-TTS models, it provides end-to-end research and content generation capabilities comparable to Google’s NotebookLM.
My take: Qwen looked at Google’s NotebookLM and said “we can do that too, but with more formats.” The automatic podcast generation from research reports is NotebookLM’s signature feature, so this is Alibaba straight-up copying the playbook. Fair enough - imitation is the sincerest form of validation. The integration of multiple models (Coder, Image, TTS) to provide end-to-end workflow is ambitious. You can go from “research topic” to “published webpage with accompanying podcast” without leaving the tool. That’s either incredibly powerful or a recipe for AI-generated content spam. Definitely both. When everyone can generate professional-looking research reports and podcasts in minutes, how do we tell what’s actually researched versus AI slop? We don’t. Congrats, we’ve democratized the production of convincing bullshit.
Google Introduces Model Armor for AI Security
Google Cloud launched Model Armor, a security solution designed to protect AI applications from prompt injection and jailbreaking attempts. The system offers five main security capabilities including prompt injection detection, sensitive data protection, harmful content filtering, with integration available through API, Apigee, and Vertex AI.
My take: Google’s solution to AI security vulnerabilities is... another AI system to protect your AI system. It’s turtles all the way down, except the turtles are all neural networks. Model Armor catching prompt injections and jailbreaks is genuinely useful - these attacks are everywhere and most companies have no defense. But here’s the fun part: how long until someone figures out how to prompt inject Model Armor itself? The cloud-agnostic design is smart positioning - protect any AI, not just Google’s. They’re selling picks and shovels for the AI security gold rush. The fact that they needed to build this tells you how bad the security situation is. We’re shipping AI to production and then frantically building security around it, instead of building it securely from the start.
More Technical Highlights
Max Planck Institute’s Multimodal Lab Agents - AI agent system detecting 74% of lab procedural errors with 77% accuracy, generating protocols from videos 10x faster than manual creation. Science is getting automated and it’s actually working.
Mistral Launches AI Studio - Mistral’s web-based platform for developing and deploying AI applications with their European open-source and proprietary models. Europe’s fighting back in the AI race with... a developer platform. Bold strategy.
Hugging Face Partners with VirusTotal - Integration of VirusTotal’s security scanning into Hugging Face platform to detect malware in AI models and datasets. Because apparently people are poisoning AI models now. Of course they are.
MIT-IBM Watson AI Lab on Sociotechnical AI - Focus on creating practical AI applications while considering social impact and ethical implications. Someone’s thinking about AI safety and ethics! Adorable.
🤔 SKEPTICAL
When Your AI Browser Becomes Your Enemy: The Comet Security Disaster
Security researchers demonstrated serious vulnerabilities in Perplexity’s Comet AI browser that allow hackers to hijack the AI assistant through malicious web content. Unlike traditional browsers, AI browsers actively interpret and act on webpage content, creating fundamental security risks. Current implementations lack basic security measures like command verification.
My take: I already ranted about AI browser security in the General News section, but this deserves its own spotlight. Researchers demonstrated successful attacks against Comet. Not theoretical, not proof-of-concept - actual working exploits. The vulnerability is embarrassingly simple: embed hidden commands in webpage text and the AI browser just... executes them. No verification, no “are you sure?”, just blind obedience to text it reads on the internet.
This is the perfect example of “move fast and break things” except what’s breaking is user security. Perplexity shipped Comet knowing (or should have known) about these vulnerabilities because they were racing to be first to market. OpenAI and Microsoft saw Comet and decided to ship their own vulnerable AI browsers rather than learn from Perplexity’s mistakes.
The fundamental problem: AI browsers need to be active to be useful, but being active makes them dangerous. There’s no easy fix here because the vulnerability IS the feature. When your browser can autonomously click, type, and navigate based on what it sees, every website becomes a potential attack vector.
We’re watching the next generation of browser security disasters unfold in real-time, and the companies shipping these products are acting like it’s fine. It’s not fine. This is going to end with massive breaches, stolen credentials, and a bunch of executives saying “we had no idea this could happen” while security researchers point to the papers they published six months earlier.
Who Are AI Browsers Even For?
Critical review of OpenAI’s new AI-powered web browser, suggesting the benefits are minimal and offering only marginal efficiency improvements.
My take: TechCrunch asked the question everyone’s thinking but nobody wants to say: who actually needs this? The AI browser pitch is “we’ll browse the web FOR you” but the reality is “we’ll add an extra layer of AI between you and the web, slowing everything down and introducing security risks.” The marginal efficiency gains - saving a few clicks, summarizing pages you would’ve skimmed anyway - don’t justify the complexity and risk. This is a solution looking for a problem, and the problem it found is “how can we make web browsing worse but with more AI?”
Reddit Sues Perplexity for Data Scraping
Reddit has filed lawsuits against AI company Perplexity and other firms for allegedly scraping data from its platform without authorization, circumventing technical controls to illegally access and use Reddit’s data for AI training.
My take: Reddit spent years as the internet’s free content farm, and now they’re shocked - SHOCKED - that AI companies scraped their data without permission. The audacity of Perplexity circumventing technical controls is delicious hypocrisy considering Reddit itself is just user-generated content that Reddit doesn’t create. Reddit is suing to protect the monetary value of data their users created for free. Both sides of this lawsuit are parasites fighting over who gets to monetize other people’s content. I hope they both lose.
The real story is the broader question of AI training data and copyright. If Perplexity loses, every AI company is on notice that scraping without permission is illegal. If Reddit loses, every platform’s content is fair game. Either way, the users who actually created the content get nothing. Classic internet.
AI Models Get Brain Rot Too
Research demonstrates that training large language models on low-quality social media content with high engagement metrics negatively impacts their performance and cognitive capabilities. The study draws parallels between AI model degradation and human cognitive decline when exposed to poor quality training data.
My take: We trained AI on Twitter and Reddit and now we’re surprised it got dumber. Who could have predicted that high-engagement social media content - you know, rage bait, misinformation, and memes - makes for bad training data? Everyone. Everyone predicted this. The study shows what we all knew: garbage in, garbage out applies to AI too. The brain rot comparison is apt - feed your AI a diet of social media garbage and watch its cognitive abilities deteriorate. The terrifying part is how many AI companies are training on exactly this kind of data because it’s free, plentiful, and “engaging.” Engagement ≠ quality, but try explaining that to the people optimizing for metrics.
New Statement Calls for Not Building Superintelligence (For Now)
A Future of Life Institute statement calls for prohibiting superintelligence development until scientific consensus confirms it can be done safely with public support. The statement has 32,214 signatures including Yoshua Bengio and Geoffrey Hinton, with 64% public agreement versus 5% supporting status quo.
My take: The AI safety crowd got signatures from 32,214 people including major researchers, and the response from AI companies will be: “thanks for your input!” followed by absolutely nothing changing. The statement is asking to pause superintelligence development until we know it’s safe, which is reasonable except for the minor detail that nobody agrees on what “safe” means or when we’ll know we’ve achieved it.
Yoshua Bengio and Geoffrey Hinton signing this is significant - these are the godfathers of deep learning basically saying “we created this, and we’re worried.” The 64% public support shows people are nervous, but 5% wanting to continue as-is includes the billionaires actually building the thing, so guess which group wins?
This statement will be referenced in future documentaries about how we ignored all the warnings, signed by the very people who built the technology they’re warning about. It’s the tech industry equivalent of “we’ve tried nothing and we’re all out of ideas.”
Research: 77% of Data Engineers Have Heavier Workloads Despite AI Tools
MIT Technology Review survey reveals 77% of data engineers face heavier workloads despite AI tools, primarily due to integration complexity and tool fragmentation. Data engineers now spend 37% of their time on AI projects compared to 19% two years ago, expected to reach 61% within two years.
My take: AI was supposed to make our jobs easier, instead it made data engineers work 77% harder. The promise was automation and efficiency; the reality is managing a dozen disconnected AI tools that don’t talk to each other. This is the AI productivity paradox in perfect form: tools that speed up individual tasks but create so much integration overhead that total work increases. Data engineers spending 37% of their time on AI projects (heading to 61%) means AI isn’t reducing work, it’s becoming the work.
The disconnect between CIOs and CDOs about data engineers’ strategic value is chef’s kiss - executives think AI is handling everything while the people actually doing the work are drowning in complexity. This is what happens when you deploy AI for the sake of deploying AI without thinking through the workflow. Every new AI tool is another integration point, another potential failure mode, another thing to monitor and maintain. Congrats, we automated ourselves into more work.
Goldman Sachs Says AI Bubble Fears Are Overwrought
Goldman Sachs analysts argue that concerns about an AI bubble in the stock market are exaggerated, challenging the narrative of AI market bubble in their analysis of AI company valuations and market dynamics.
My take: Goldman Sachs, the company that totally saw the 2008 financial crisis coming, says the AI bubble concerns are “overwrought.” Well, I’m convinced! When a financial institution with a vested interest in keeping the money flowing says “nothing to see here, keep investing,” you know everything is fine.
The same people who pump billions into AI companies are now telling us there’s no bubble. This is the financial equivalent of a driver saying “I’m not drunk” while swerving between lanes. Maybe they’re right and this is sustainable growth. Maybe AI really will generate enough value to justify current valuations. Or maybe we’re in the middle of the biggest hype cycle since the dot-com boom and Goldman wants you to keep buying so they can sell. History will tell, but my money’s on “Goldman Sachs was very wrong about this.”
CLOSING THOUGHTS
This week in AI: where security researchers beg companies to stop shipping vulnerable AI browsers while those companies race to ship more vulnerable AI browsers, Meta fires 600 AI workers while everyone else throws billions at talent, and Goldman Sachs assures us the AI bubble definitely isn’t a bubble (wink wink).
The OCR breakthroughs are genuinely impressive, the infrastructure deals are genuinely terrifying, and the security situation is genuinely a disaster waiting to happen. But hey, at least Claude can remember your conversations now, so when the AI browser apocalypse comes, it’ll remember exactly how we got here.
See you next week, assuming the AI browsers haven’t handed all our credentials to malicious websites by then. YAI 👋
Disclaimer: I use AI to help aggregate and process the news. I do my best to cross-check facts and sources, but misinformation may still slip through. Always do your own research and apply critical thinking—with anything you consume these days, AI-generated or otherwise.
Thank you for collating all of this! Really helps in separating the signal from the noise. It's hilarious how we're headed towards an ironic future with AI, especially considering how it's application is creating or worsening the very problems it was promised to solve, just like every other technology. This is the efficiency paradox in action : a wider road doesn't solve traffic, simply because the influx of vehicles negates the increased efficiency of the wider road.