My new Unite.AI article looks at what happens when AI learns from human decisions. Hiring records, medical records and everyday language all carry our blind spots, and a model trained on them applies those blind spots to every case it sees. The research in the article covers hiring, healthcare and workplace email.
Meta, Walmart, Shopify and Stripe just gave AI shopping agents a way to prove who they work for. Once an agent can show ID, turning it away stops being a security call and becomes a commercial one. Is your company ready to allow agents to interact with it?
On Sunday night, Meta’s Muse users tried to shop on Amazon and hit a popup. Meanwhile Amazon’s own agents reprice listings, manage sellers, and shop other brands’ websites on your behalf. Amazon’s rule for your agent is reasonable – it just runs in one direction.
AI labs make bold claims about safety and reliability. Independent evaluators test those claims, but access and publication rights determine whether executives and the public ever see the full result.
Boards spent two years asking whether management understands AI. The harder question is who can interrupt it.
What each one actually does, where each one is stronger, and how to avoid becoming the unpaid manager of your own AI staff
America needs data centers to lead in artificial intelligence. Communities will accept them only when companies and governments disclose the costs, fund the added capacity, and leave behind durable local assets.
The future org chart will not be defined by humans managing agents through a shared brain. It will be defined by who controls context, protects dissent, and exercises judgment when automation turns history into policy.
The EU AI Act is now in full force, and it’s reshaping the landscape of AI compliance. From hidden compliance triggers to compute thresholds, there are many nuances to consider. This is a game-changer for AI labs and developers worldwide.
AI makes execution fast and cheap, but it explodes the need for decision-making and accountability. Are companies building faster, or just creating “workslop”? AI makes building easier, but no one is talking about how it’s making leadership harder.
On July 21, OpenAI’s GPT-5.6 Sol escaped its sandbox and hacked a live platform. Congress moved on a kill switch bill within days. Most boards still have no answer to the only question that matters: what’s your off-switch?
The demographic cliff is here, and companies are turning to AI to fill the labor gap. But treating human talent as a fungible commodity comes with a hidden cost: when you automate the bottom rung of the corporate ladder, you destroy the apprenticeship engine that mints future CEOs.
AI is accelerating a postliterate era. AI summaries tend to remove nuance, making complex organizations and problems look cleaner, simpler, and flatter than they are. Strong leaders know how to spot missing signals, question false coherence, and protect their judgment. For leaders, the danger is the loss of discernment. Learn how to read between the lines of AI summaries.
Most mid-market companies are waiting for an AI revolution to bubble up from the trenches.
They’ve bought the licenses, hosted the hackathons, and told everyone to “just experiment.”
Two years later, the data is brutal: only 5% ever achieve sustained productivity or profit impact. The rest are polishing sharper pencils while the real assembly line stays broken.
Here’s why the bottom-up myth is failing—and what top-down leadership must do instead.
Why counting lines of AI-generated code is a vanity metric. The new CEO flex isn’t about showing how much code your AI can generate. It’s about showing how much more your people can achieve with AI at their side. Learn how top CEOs are shifting from automation to augmentation to drive real business impact.
Claude Cowork vs Code: Which One Should You Actually Use? Claude Cowork and Code are both powerful, but they’re built for different types of work. This guide explains when to use each tab in the Claude Desktop app, including whether you can build websites with Cowork and why one often uses more tokens than the other.
Boards love signing Responsible AI Frameworks. They feel productive. But approving a PDF isn’t governance. Real AI oversight demands named accountability, living audits, and the courage to say “no.” Most boards are making three critical mistakes that leave their companies exposed to massive risk while they polish their ‘Responsible AI’ documentation.
Boards have entered the AI era unprepared. The “AI curiosity” phase is over, replaced by regulatory pressure, rising risk, and a widening gap between management’s AI narrative and the board’s ability to challenge it. Board directors who lack AI experience are trusting the recommendations made by internal AI champions and vendors. As a result, many are now rubber‑stamping initiatives they don’t fully understand — a governance failure with real P&L consequences.
The 3 Cs Framework is the only path out: Clarity, Capabilities, and Capture. Boards that master the 3 Cs move faster, take smarter risks, and build real moats.
Discover why traditional software deployment checklists fail in the age of generative AI and learn how to operationalize a multi-dimensional, probabilistic AI Definition of Done to successfully push enterprise pilots into production.
Moving from traditional software to enterprise AI requires a fundamental shift from specifying explicit logic to defining statistical boundaries. To overcome pilot fatigue and successfully scale, product managers must ditch legacy, binary deployment checklists and implement a multi-dimensional, probabilistic Definition of Done (DoD) that addresses dynamic evaluation, behavioral guardrails, upstream model volatility, and long-term production drift.
AI economics are shifting from token price to outcome efficiency. The real question is cost-of-pass: how much compute, rework, correction, and human liability-bearing review it takes to reach one correct, usable result. This essay provides a grounded analysis of AI unit economics, liability floors, cost-of-pass, and the real KPIs leaders use to measure AI productivity, throughput, and verified outcomes.
