Seekr

If someone else can switch off your AI, was it ever really yours? In June, two of the world's most capable models went dark globally. Same day, no warning, no appeal. The work built on them simply stopped. That's model dependency risk: a continuity problem and an audit problem at once. Join us August 5 to map your exposure before your regulator asks.

Join us on this free focus session TODAY to map your exposure before your regulator asks. >> Last chance to reserve your seat for this free webinar today!

>> Last chance to reserve your seat for this free webinar today! If someone else can switch off your AI, was it ever really yours? In June, two of the world's most capable models went dark globally. Same day, no warning, no appeal. The work built on them simply stopped. That's model dependency risk: a continuity problem and an audit problem at once. Join us August 5 to map your exposure before your regulator asks.

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"Did you brush your teeth?" "Yup!" Every parent knows what comes next. You check the toothbrush. Bone dry. The kid wasn't necessarily lying. Maybe they meant to. Maybe they got distracted making funny faces in the mirror. Maybe they rinsed and called it close enough. Or maybe "brushed" just means something different at that age. Either way, the answer that came out it was wrong - confidently wrong. That's how I'd characterize the relationship most individuals and organizations have with AI right now. We've come to treat AI like an infallible cognitive super calculator: type in the question, use whatever comes out the other side. A calculator gives you the same answer every time. Ask AI the same question twice and you almost always get two different answers, both delivered with total confidence. Every new wave of news headlines makes this point even clearer. Agents ignoring "stop!" commands or "going rogue." Models citing sources that don't exist. Systems doing exactly what they were optimized to do, which turns out to be nowhere near what anyone intended. Parents still ask the question. We just check the toothbrush, because confidence and accuracy are two different things, at home and at work. Tom Fishburne's latest Marketoonist cites a stat that should make every organization contemplating its AI use pause and think: 86% of enterprises have moved past the pilot stage with AI agents, yet only 34% trust what those agents are doing. Our CEO Pat Condo, quoted in the post from Tom, nailed it: "Don't ask users to trust AI. Give them the evidence to verify it." So what makes AI "trusted" exactly? It's four things: Provenance: What is the verifiable history, origin and chain of custody of the data, models and outputs? Explainability: Can you understand why it produced this result? Contestability: Can you challenge or override the result? Agent control: Did it act within its authorized boundaries? Once a model calls tools and takes actions, the question shifts from what it said to what it did. A trace records the actions. Agent control checks them against what the agent was permitted to do: which tools, which data, which approval limits. Most AI systems answer one or two of these questions at best. The usual offer is a citation, which says nothing about why the model answered the way it did. Trusted AI answers all four, for every output, in a form the person asking can follow and understand. If you want to use AI, but don't trust it, and need to be able to explain what it's actually doing, you check out: https://lnkd.in/e_xgQChr or feel free to reach out - DMs are open and I'm happy to chat.

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