Colorado's AI Act takes effect June 30, requiring companies to actively guard against AI making discriminatory decisions. It is not alone. A draft federal bill, a White House fight with the states, and an EU labeling mandate are all moving at once. The companies treating AI compliance as next year's problem are about to discover the regulators moved faster than they did.

A new Publicis Sapient survey of 1,550 decision makers finds 73 percent of large enterprises use AI across most of their work, but only 10 percent say it is core to operations. Nearly half say the AI is already capable while their organization is not built to capture what it can do. The bottleneck has moved from the technology to the org chart, and most leaders are still trying to fix the wrong one.

xAI shipped Grok Imagine Video 1.5 this week, and it now tops the video generation leaderboard while costing roughly 86 percent less than the premium option. Everyone is watching capability. The story that will actually rearrange budgets is the collapsing price of generated media. This is the token economy arriving on schedule.

Tech layoffs are running at 1,115 a day in 2026, and almost every announcement blames AI. A new Gartner study of 350 companies found the firms cutting the most got no better financial returns than the firms cutting the least. The layoffs are real. The justification often is not. Here is how to tell the difference inside your own company.

Anthropic has passed OpenAI in US business adoption for the first time and launched a partner program to lock in the companies building on Claude. The headline is the leaderboard. The real story is that the company you standardized on three years ago is no longer obviously the right bet, and most enterprises built no way to change their mind.

The EU AI Act was set to bring tough obligations for high-risk AI systems online in August 2026. Those obligations have been postponed to December 2027, a delay of about 16 months, and the requirement for member states to build regulatory sandboxes slipped a year. The Commission still published a code of practice on labeling AI-generated content on June 10. The lesson for leaders: even the world's most aggressive AI regulator decided the rules were arriving faster than compan

Anthropic released Claude Fable 5 on June 9. It scored around 80 percent on a hard real-world software engineering benchmark, well ahead of every rival, and Stripe reported it compressed five months of engineering into days and finished a migration in a 50-million-line codebase in a single day. The headline is not the benchmark. It is long-horizon autonomy: AI that works for hours on one specification. Here is what that does to how you scope projects and what your people shou

AI got cheaper per token and more expensive overall at the same time. This week the industry hit a full cost panic: Uber blew through its 2026 AI coding budget by April, Microsoft pulled some Claude Code licenses, JPMorgan warned that token costs are eating internet profits, and a large share of AI agent startups are projected to run out of cash by late 2026. Cheaper unit cost does not mean cheaper bills. Here is how to keep AI on a budget instead of letting it eat your margi

Salesforce hired zero new software engineers in its last fiscal year while planning to spend around $300 million on Anthropic AI tokens in 2026, much of it on coding. Sales headcount grew 20 percent. This is the token economy made literal: computation is replacing labor hours as the basic unit of production, and the budget is moving from payroll to compute. Here is what it means for how you plan, hire, and measure.

Meta laid off roughly 8,000 people and moved another 7,000 into AI workflow teams this spring, touching nearly a fifth of its workforce. On June 12, Mark Zuckerberg admitted in an internal memo that the company made mistakes in how it restructured. The lesson for every leader: cutting a role and automating its work are two different things, and confusing them is how you lose capacity while calling it strategy.

Microsoft just shipped a reasoning model built from scratch and a new layer for developers to build agents on company data, reducing its dependence on OpenAI. The lesson for every business is not to train your own model. It is to own the one asset that off the shelf AI cannot copy.

This month Washington moved to keep federal AI rules light while Colorado's strict AI Act counts down to a June 30 start date. The result is a widening gap between a permissive federal stance and a patchwork of demanding state laws. If your company operates across state lines, that gap is yours to manage.

Salesforce confirmed it added no new engineers in its entire last fiscal year while growing revenue, after AI agents made existing developers dramatically more productive. The real story is not that work vanished. It is where the work went, and what that means for how you staff.

More than 180,000 tech workers have been cut in 2026, and most of the companies doing it are profitable. The cuts are not about distress. They are about moving capital from payroll into AI infrastructure. Understanding that reframe tells you exactly how to protect your career and your company.

Anthropic passed OpenAI this month on both private valuation and revenue run rate. For business leaders, the lesson is not which model to pick. It is that betting your company on any single model is now the riskiest move you can make.

Executive summary Global electricity demand is undergoing its most violent inflection in three decades, and the company best positioned to capture it is the one that owns the picks and shovels of AI's physical buildout. After 15 years of near-flat growth, global power consumption surged 4.4% in 2024 and is now compounding at ~3.6% annually through 2030 per the IEA, equivalent to adding a Japan to the world every year. Data centers alone will consume 945 TWh by 2030 in the IEA

There's "China speed," the pace at which platforms get built, scaled, and exported across the Pacific. And then there's "Shenzhen speed," which is fast enough to surprise the Chinese themselves. I just spent a week there at a Prosus tech conference. Beyond participating as one of the keynotes, the bigger takeaway was listening to the Chinese AI landscape unfold with founders speaking on building robots, AR glasses, and frontier models. The programming was very thoroughly put

Sometimes we read about sweeping headlines on AI, and it makes us lose hope in humans. AI gets better exponentially at everything and our confidence in ourselves starts to wane. But the other day, I came across a test where the humans scored 100% and AI around 1%. Whenever a new model update is rolled out, it is measured against industry benchmarks. (These benchmarks include tests such as MMLU, which measures broad knowledge across many subjects, GSM8K for mathematical reaso

AI slowly eats away the modern org chart Did you know that the modern org chart is actually modelled after how soldiers reported to generals during war times in the Roman period? Just like how schools are modelled after the Germanic assembly line (aka outdated), org chart make-ups isn’t that innovative either. And it’s controlled us for about 2000 years. Let that sink in. So apparently, back in the day, 8 soldiers would form a contubernium (pictured below) Which really was ju

Tokens are the new form of labor, a new way to calculate the opex of a company? The other day, I was using Perplexity Computer, a new product feature that was rolled out. Basically, it’s Perplexity’s version of an AI agent, where it can draft and send emails, run commands in parallel, create sub agents, and execute on tasks. Each task that was finished, my credits started to dwindle, and it made me think of something, which is that labor, in the future, is simply tokens.