When NVIDIA reported third-quarter revenue of $57 billion, up 62 percent, Wall Street breathed a collective sigh of relief. The AI bubble, it seemed, had been granted another reprieve. But beneath the headline-grabbing numbers lies a financial architecture that should trouble anyone who remembers the tech industry's previous monopolies—and the catastrophic collapses that eventually broke...
Category: AI
When AI agents run a startup
In August 2025, journalist Evan Ratliff cofounded a startup staffed entirely by AI agents. Five virtual employees—each with email, Slack, phone capabilities, and their own synthetic voices—collaborated to build a product, run marketing, and handle operations. Three months later, HurumoAI had shipped working software and attracted genuine VC interest. It also nearly collapsed multiple times...
The Enterprise AI Shift: How Chinese Models Are Challenging Silicon Valley’s Dominance
When Airbnb CEO Brian Chesky told Bloomberg in October that his company relies heavily on Alibaba's Qwen model for AI-powered customer service, calling it "very good, fast, and cheap," he offered a rare glimpse into a trend that's quietly reshaping enterprise AI adoption. While Silicon Valley giants battle over who can build the most powerful—and...
The AI That Pauses to Think: How Interleaved Reasoning Is Reshaping Autonomous Agents
When Moonshot AI demonstrated its Kimi K2 model tackling a PhD-level mathematics problem in hyperbolic geometry, according to examples published in their technical documentation, the AI didn't just compute an answer. It embarked on a 23-step journey: searching academic literature, running calculations, reconsidering its approach based on results, querying databases again, and iterating until it...
Moonshot AI’s Kimi K2 Challenges Western Leaders—With a Licensing Twist
Chinese artificial intelligence startup Moonshot AI has released Kimi K2 Thinking, a massive language model that the company claims outperforms leading American AI systems on several key benchmarks. If the claims hold up under independent testing, the release would mark another milestone in China's accelerating push to close the AI performance gap with Western labs—and...
The LLM Whisperers: How Cloudflare and Anthropic Cracked the Code on AI Agent Efficiency
There's a delicious irony at the heart of modern AI development. We've spent years training large language models on every scrap of code humanity has ever written—Stack Overflow answers, GitHub repositories, programming textbooks, documentation—teaching them to become fluent in Python, JavaScript, TypeScript, and dozens of other languages. Then, when it comes time to actually use...
AI Hallucinations: Why They Happen and How We’re Tackling Them
AI hallucinations refer to instances where a model generates a confident response that sounds plausible but is factually incorrect or entirely fabricated . For example, an AI chatbot might cite a nonexistent legal case or invent a scientific-sounding explanation out of thin air. These aren’t intentional lies – they result from the way generative AI...
The Complete Guide to Running LLMs Locally: Hardware, Software, and Performance Essentials
For years, the language model arms race seemed to belong exclusively to cloud providers and their API keys. But something remarkable has happened in the past eighteen months: open-weight models have matured to the point where sophisticated, capable AI can now run entirely on consumer hardware sitting under your desk. The implications are profound. Your...
The AI slop bucket overflow: “Workslop” is the hidden productivity drain no one’s measuring
There's a new term making the rounds in corporate America, and it perfectly captures a frustration that's been building since ChatGPT entered the workplace: workslop. It's the AI-generated equivalent of that colleague who forwards you a 47-slide PowerPoint deck that somehow says nothing at all, except now it's happening at machine speed, in every department,...
Claude’s Modular Mind: How Anthropic’s Agent Skills Redefine Context in AI Systems
If you've been building with large language models, you've hit this wall: every API call requires re-explaining your entire workflow. Financial reports need 500 tokens of formatting rules. Code generation needs another 300 tokens for style guides. Multiply this across thousands of requests, and you're paying twice—once in API costs, once in context window exhaustion....
