Founder and editor of Omniscient Media. Picks the stories, sets the thesis, writes the final voice, and builds the AI pipelines that surround the writing.
OpenAI missed multiple internal revenue targets in early 2026, ceding ground to Anthropic in its highest-margin segments. For most companies, a growth stumble is manageable. For SoftBank, which borrowed $40 billion unsecured to fund a $30 billion OpenAI bet maturing in March 2027, the timing could not be worse.
Meta and Microsoft announced thousands of layoffs on the same week they reaffirmed plans to spend close to $700 billion on AI infrastructure in 2026. The juxtaposition is not coincidental - it is the central logic of this moment in the industry.
Intel posted its strongest quarter in years, with revenue beating Wall Street by $1.3 billion and its data center and AI unit up 22% year over year. The real story is structural: the AI infrastructure buildout is quietly rehabilitating the CPU, and Intel finds itself holding assets no one expected to matter this much.
Average transparency scores for major AI developers fell from 58 to 40 in a single year, reversing two years of measured progress. The companies building the most consequential models have decided, collectively, that the public does not need to know how they work.
The Stanford AI Index 2026 documents record investment, rapid capability gains, and a narrowing U.S.-China model gap. It also documents an 89 percent collapse in AI scholar immigration, the dismantling of the government's only frontier model evaluation body, and a generation of entry-level workers being displaced before they form. The U.S. may still be winning. Whether anyone in power is paying attention to the score is a different question.
Japan's Humanoid Robot EXPO in April 2026 revealed a nation grappling with a stark reality: the country that pioneered humanoid robotics now trails China by a wide margin in production scale. With Unitree and AgiBot on track to dominate 80% of global shipments, Japan's path forward may lie in specialization rather than scale.
Claude Design turns Anthropic's most capable vision model into a full creative collaborator - generating prototypes, decks, and marketing collateral from a prompt. The product is framed as a complement to tools like Canva and Figma. The market isn't buying it.
After 15 years building Apple into a $4 trillion institution, Tim Cook is handing the keys to John Ternus, a mechanical engineer who has spent a quarter century shaping the products Cook sold. The transition says as much about where Apple has been as where it is going.
On April 8, Elon Musk listed seven models in simultaneous training on Colossus 2 and captioned the post "Some catching up to do." The cluster burns 400 megawatts, runs on an estimated 550,000 NVIDIA Blackwell GPUs, and is training a 10-trillion-parameter model. The question is whether scale alone can close the gap.
Z.ai's GLM-5.1 briefly led the SWE-Bench Pro leaderboard with a self-reported 58.4% score, trained entirely on Huawei Ascend chips with no NVIDIA silicon in the stack. The benchmark story has already moved on. The geopolitical one has not.
Model Context Protocol is the closest thing AI has to a universal plug standard - and it arrived with the same security debt that plagued every previous universal plug standard. A comprehensive technical guide to MCP architecture, attack surfaces, optimization, and one uncomfortable prediction about where this is all heading.
From an 800-line GitHub side project to a $1.25 billion platform used by 35% of the Fortune 500, LangChain has become the de facto infrastructure layer for production AI agents. This comprehensive guide covers how the ecosystem works, what it costs, who uses it, and how it compares to its competitors.
Isomorphic Labs has a Nobel Prize-winning platform, $600 million in fresh capital, and partnerships worth up to $3 billion with Eli Lilly and Novartis. Its first AI-designed drug was supposed to enter human clinical trials by end of 2025. It didn't. What the delay reveals about the gap between computational elegance and biological proof.
Six publicly available frontier models are clustered within 1.3 percentage points on the industry's most-cited coding benchmark. Meanwhile, a withheld model just scored 93.9% on the same test. The measurement system isn't broken - it's being gamed at two levels simultaneously.
Claude Mythos Preview can autonomously find and exploit zero-day vulnerabilities in every major operating system and browser. Rather than shelve it, Anthropic has handed it to a coalition of 50-plus firms under Project Glasswing. The strategy is defensible. Whether it holds depends on who else is building the same thing - and Washington's posture toward the company that built it.
SoftBank wired its first $10 billion OpenAI tranche today - borrowed in full from JPMorgan, Goldman Sachs, and three Japanese banks on a 12-month unsecured loan. The deal's architecture reveals more about its risks than its headline number does.
Google DeepMind's Gemini 3.1 Pro arrived with the strongest independently verified reasoning scores of any frontier model. Three weeks later, GPT-5.4 changed the picture. A benchmark-by-benchmark assessment of where Gemini still leads, where it has fallen behind, and what the competitive gap actually looks like on verified data.
Employment for workers aged 22 to 25 in AI-exposed occupations has fallen 16 percent since ChatGPT's release, while older workers in the same fields have held steady or grown. The entry-level job is disappearing not through mass layoffs but through a quiet failure to hire - and the long-run consequences for the talent pipeline have not yet been priced in.
The Maven Smart System, built by Palantir and integrated with Anthropic's Claude, compressed the US targeting cycle from hours to seconds during Operation Epic Fury. Understanding how that pipeline actually works - and what it cannot do - is essential to evaluating the accountability questions the campaign has raised.
OpenClaw is the fastest-growing open-source AI agent in GitHub history: a self-hosted, messaging-native assistant that can manage your inbox, run shell commands, book flights, and extend itself with community-built skills. This is the complete story of how it was built, how it works, why it broke the internet, and why it scares cybersecurity researchers.
Google Research has published TurboQuant, an algorithm that cuts the memory cost of running large AI models by at least sixfold - with no accuracy penalty and no retraining required. Memory chip stocks sold off sharply. The sell-off misread what the research actually says.
OpenAI has shut down Sora, its AI video platform, roughly 15 months after launch - taking down with it a blockbuster licensing deal with Disney and a planned $1 billion investment. Reuters confirmed no money ever changed hands. The manner of the shutdown, as much as the decision itself, reveals how fragile the Big Tech-Hollywood AI partnership model always was.
Tesla, Figure AI, Boston Dynamics, and 1X have each crossed from prototype to production-ready product within months of one another. The competition is no longer about which robot looks most human. It is about which company can scale.
In 2025, just ten companies absorbed 41% of all U.S. venture dollars - a concentration level with no precedent in a decade. The headline figures flatter a market that is quietly contracting at its base, where deal counts have hit a six-year low and seed funding is falling. The question is not whether AI deserves capital. It is whether this degree of gravitational pull leaves room for anything else.
A Harvard Business School working paper analyzing nearly all U.S. job postings from 2019 to 2025 is the most rigorous accounting yet of generative AI's labor market impact. The headline numbers are striking - but three separate research teams find reasons for both alarm and restraint.
A research preview unveiled at NVIDIA GTC shows HD video generated in under 100 milliseconds, a latency drop so sharp it changes what video AI is, not just how fast it runs. The creative and safety implications are profound.
Global AI spending is on track to hit $2.52 trillion in 2026, yet 95% of task-specific enterprise deployments deliver zero measurable P&L impact. The money is going where the cameras are pointed, not where the returns are.
The White House has released a sweeping legislative blueprint that would strip states of authority to regulate AI development, handing the industry a single, minimally burdensome federal standard. The move is the culmination of a year-long campaign to consolidate AI governance in Washington - but getting Congress to actually pass it is another matter.
OpenAI's new GPT-5.4 mini and nano models complete the GPT-5.4 family, targeting agentic workflows where speed and cost matter more than raw capability. Mini nearly matches flagship benchmark scores at a third of the price; nano goes further, enabling economically viable mass-scale deployments.
Mistral's new Forge platform lets enterprises train AI models from scratch on proprietary data. But the deeper ambition isn't customization - it's making domain-trained models the reliable foundation for enterprise AI agents.