Unreal Intelligence

Karl Sorochinski

Unreal IntelligenceExploring Artificial Intelligence, Technology, and the Future Artificial intelligence is changing how we live, work, create, and understand the world. But separating meaningful innovation from speculation, hype, and marketing isn't always easy. Unreal Intelligence explores the technologies, ideas, challenges, and opportunities shaping our increasingly intelligent world. From emerging AI capabilities and real-world applications to the broader implications of technological change, we examine what matters, why it matters, and what might come next. Whether you're a technology professional, business leader, developer, or simply curious about the future, Unreal Intelligence brings perspective to a rapidly evolving landscape. New episodes weekly. Sponsored and Produced by EKA Technology, LLC Humanizing AI.

  1. 2d ago

    The Data Center is on the Ballot

    On Tuesday, data centers are on the ballot. Not literally. But in governor's races across the country, candidates are promising to pause them, and it isn't the party you'd guess. In New Hampshire, the Republican governor wants a moratorium. In Texas, it's the Democrat. In Pennsylvania, it's the Republican again. New York's Democratic governor already did it. And in Maine, the legislature passed the country's first statewide moratorium, and the Democratic governor vetoed it to protect jobs in a mill town. This week I take politics off the table and follow the electric bill instead. Gallup finds 71% of Americans oppose an AI data center near home, more than oppose a nuclear plant. UMass Amherst puts local support at 11%. Data Center Watch counted at least 75 projects, worth about $130 billion, blocked or delayed in the first quarter of this year alone. Whoever wins, the industry already lost the assumption it was running on: that nobody votes on data centers. Then I play out the three ways Tuesday can go. In a pause wave, capacity gets tighter. In the status quo, "pay your own way" moves grid costs onto developers, and from there onto you. In a patchwork, which is my bet, you get a map of where compute is easy and hard to get, and data residency all over again. All three push your AI bill in the same direction. I'm honest about what's driving prices *today* (memory chips, not zoning boards). And Anthropic's IPO filing shows compute is contracted 12 to 18 months ahead, which means this week's vote helps set your 2028 AI bill. Finally, the cost that doesn't show up on an invoice: AI's social license. Pew says 52% of Americans are more concerned than excited about AI. If you're a 500-person company announcing "AI efficiencies" in a town where power bills just jumped, you're the local face of it. I end with three things to do Monday morning: ask your vendors where your workloads run and what their capacity plan is, right-size your models, and get your local AI story straight before you need it.

  2. Oct 26

    AI is Replacing Your Doctor and it is Good Thing

    This week, a study in *Science* put Microsoft's Diagnostic Orchestrator up against practicing physicians on 325 real emergency room cases. The doctors got the diagnosis right about 47% of the time. The AI got it right 80% of the time. So yes, AI is coming for your doctor, and I think that's the best news in healthcare in a generation. What's being replaced is the doctor working alone with fifteen minutes, a clipboard, and an insurance flowchart. What replaces it is the doctor with backup. We go through what the studies actually say, caveats included: text-only tests, generalists rather than specialists, and expert doctors still out-diagnosing AI in a meta-analysis of 83 studies. Then comes the twist. Handing doctors GPT-4 barely helped them (74 to 76) while the AI alone scored 92. Workflows designed for collaboration jumped clinicians from 75% to 82–85%, and at Penda Health in Kenya, an AI safety net cut diagnostic errors 16% across nearly 40,000 real visits. The integration is the product, not the model. From there: the MASAI trial (more than 100,000 women, 29% more cancers found, 44% less radiologist workload), the 2017 Kaggle lung cancer competition won by a team with no medical training, and records at scale with Epic's 300-million-patient Cosmos. Can a human review 100,000 similar cases in a minute? No. Can a doctor ask a tool that just did? Yes. Then the two threats. The first is AI pointed at the money instead of the patient, like Medicare's WISeR pilot, where review vendors get paid when they help reduce Medicare spending. The second is the war on science: the most measles cases since 1991, 22 mRNA vaccine projects wound down, physics PhDs leaving at a 30-year high, and trust in federal health agencies down to about 60%. AI in medicine is built on that science. Throw it out, and you throw out the thing that makes the AI worth trusting. We close with medicine's playbook for any business adopting AI.

  3. Oct 19

    Nobody Told It It Wasn't a Drill

    Anthropic told its own AI, in writing, "This is a simulation. You have no internet access." It did have internet access, and it broke into three real companies. It didn't go rogue. It did exactly what it was told, about a world that wasn't true. Lately there's been a wave of articles saying "AI didn't go rogue, you just instructed it wrong," and comments on one of my own posts saying testing and validation is the stakeholder's job. I agree with both, as far as they go. This episode is about where they run out. First, what actually happened in Anthropic's review of 141,006 cyber-evaluation runs. Claude reached a real production database, and it published a malicious package that ran on fifteen real systems. In one case it realized the systems were probably real and kept going anyway, talking itself into the idea that they must be part of the exercise. Then the two ways an agent gets lied to. The inside lie is when you're wrong about the environment. The outside lie is when someone rewrites it through the data, like SalesBleed, where a public Salesforce lead form hijacked Agentforce into leaking CRM data. Underneath both: it's not a person, every answer sounds 100% confident whether it's 2% or 99% sure, goals beat guardrails unless you enforce them, and it learned its logic from us. Is anyone really surprised it cheats and lies? Then Monday. "Rogue" is over-hyped, and stakeholders do have to own what they asked for and tested. But stakeholders have always struggled with requirements and testing, and we're now asking them to redesign testing for agents and take full responsibility, so that won't happen before bad things happen. Trust the badge, not the brief. Run a two-column audit of what each agent was told versus what it can actually reach. Enforce limits in permissions and the network, not the prompt. Treat every input as data, not instructions. Make someone's job reading the transcripts. And give the business an agent-testing playbook, because "go test it" isn't one.

  4. Oct 12

    Measurable Isn't Meaningful

    Eight in ten organizations say their AI agents are delivering measurable returns. Fewer than one in ten have scaled one into real value. Both can be true, and the gap between them is where your agent budget is going. This week I'm going after the word "measurable." It has quietly become the bar every agent clears, and that's why it tells you nothing. First, measurement theater. The "80% measurable, 23% significant" stat that's been all over LinkedIn isn't one study. It's two different surveys (Anthropic's and WRITER's), with different respondents and different questions, stitched together. That's the problem in miniature: everyone quotes the number that flatters their story. I talk about projects I've been part of where "hours off someone's plate" was the success metric, for work everyone already knew had no value. Goodhart's law did the rest. Then the economics: why the agents that pay back own a unit of work that already had a price, and why the review tax eats the business case. New research from Google, DeepMind and MIT found that for 46% of scientists who save time with AI, checking its output eats more than a quarter of the savings. I also tell the story of an evaluation system that got a definitive "no" in a few weeks, at about 20% of the cost and 5% of the time of the traditional route. Leadership called it a failure and shut it down. The next three efforts cost full price and were also wrong. Then the Monday Agent Scorecard. Five questions for every agent you're running: what unit of work does it own, what did that unit cost before, what does it cost now with review time included, what does an error cost, and would it survive leaving the pilot team? Then sort each agent into fund, fix or kill. Most of yours will land in "fix," and that's where the opportunity is. And if a fast, cheap "no" isn't counted as a win in your program, you're going to keep paying full price to learn the same lessons.

  5. Oct 5

    Colorado Blinked

    America's first broad AI law was signed, delayed, sued, frozen by a federal court, and then repealed. It never spent a single day in force. When Colorado swapped its 2024 AI Act for SB 26-189 this May, plenty of executives breathed a sigh of relief. I think that's a mistake. Colorado didn't lower the bar. It moved it. The old law asked you to prove your process. The new one asks you to justify your outcomes, one person at a time. I walk through the whole saga: the governor signing "with reservations" the special-session delay xAI's lawsuit, with the U.S. Department of Justice joining on xAI's side a federal court freezing the law at the request of both sides, Colorado's own attorney general included the rewrite Then I get into what survived. Starting January 1, 2027, if AI materially influences a decision about someone's job, housing, loan, insurance, health care, or education, you owe them notice, a plain-language explanation after a bad outcome, a chance to fix their data, and meaningful human review. A reviewer who simply defaults to the system's output doesn't count. I connect that to something I'm watching right now in AI coding: people rubber-stamping agent decisions they don't understand, until the agent deletes the database. Finally, the Monday-morning version, which works in any state: inventory every AI-assisted decision about people run the Explain-It Test on each one measure your reviewers' override rate get your vendors to commit, in writing, to explaining individual decisions

  6. Sep 28

    Two Years, Same Number

    Last year, 39% of companies said AI had touched their profits at all. This year, according to McKinsey's State of AI report, it's 37%. It didn't go up — and in between, planned corporate AI spending roughly doubled. If your AI budget got bigger and your P&L didn't, you're not behind. You're the majority. And the majority is about to get asked some uncomfortable questions. This week isn't about AI strategy failing or corners being cut. It's an accounting question. The people using these tools swear they're faster, and I believe them — so where's the value going? I walk through the flat line from four directions: McKinsey (37% any EBIT impact, 6% "high performers," both flat), Domino Data Lab (57% whose AI return isn't outpacing investment — unchanged two years running, even as 93% say production got better), PwC's CEO Survey (one in eight got both revenue and cost benefits), and BCG (spend doubling, 94% committed to keep spending regardless). I give the J-curve argument its due — Solow, the dynamo, Brynjolfsson — and then point out the fine print: the curve only bends for the factories that redesigned the floor. Then, where the hours went. Saved time has no default destination; if nobody decides where it goes, it goes into email. I share what we've lived through — dozens of "automations" built to take work off people's plates, then rewritten when the tooling changed, with business partners blaming AI — when the real problem was that nobody had asked whether those processes were worth doing at all. Automation doesn't create value. It can just make worthless work invisible. The 6% who get real returns are nearly three times as likely to have redesigned the workflow. So: one question and four moves for Monday — should this process exist; name the P&L line; decide where freed capacity goes; redesign one workflow end to end; and set a kill date. "Next year" is not a milestone. A date is.

  7. Sep 21

    You Didn't Buy AI. You're Buying It Anyway

    You never signed an AI contract. No license, no pilot, nobody in your company anywhere near a chatbot. You're still paying for AI — it's on your electric bill. And in the grid market covering New Jersey and Pennsylvania, one line item on that bill went up more than tenfold in two auctions. This week I go after the AI cost nobody budgeted, nobody approved, and nobody can cancel. Every story written about this is a household story — your bill is going up, here's a thermostat tip. That's not the version business leaders need. If you sign the leases and own the P&L, the data center buildout is three specific things happening to your company right now, and only one of them is about price. I start with the receipts, not the forecasts. The Federal Reserve Bank of Dallas published a working paper in August finding that data centers already built have raised wholesale electricity prices three to five percent nationwide, with scenarios running to fifty percent if the proposed construction gets built and runs hot — and enough inflation impact that a Fed bank is now modeling one industry's construction schedule into PCE. Meanwhile Fortune's reporting puts the buildout near seven trillion dollars with no guaranteed demand behind it. And here's the reversal that matters: for twenty years data centers made power cheaper for everyone else by spreading fixed costs. That's inverting. I also give real airtime to the best argument against my own thesis — Rutgers' New Jersey State Policy Lab published "Are Data Centers Raising Your Electric Bill? Mostly Not. Yet." They're right that New Jersey's spike is driven more by generation retirements than data centers. But the word carrying that title is the last one. Then the three ways it lands. One: the capacity market, where PJM's auction went from 28.92 to 329.17 per megawatt-day in two auctions — clearing at the FERC cap, when PJM's own simulation says it would have gone to 388.57 without one. PJM's market monitor attributes 6.3 billion of the latest auction to data centers, and 29.4 billion of 63.6 billion across the last four. You don't bid in that auction. You get the invoice. Two: peak exposure — demand charges and ratchet clauses, where fifteen minutes on a July afternoon can set a floor under your bill for eleven months, and I walk the arithmetic. Three: the interconnection queue, where the median project reaching commercial operation last year waited sixty-one months, up from twenty-two in 2008. That one doesn't cost money. It costs you the option to grow. Finally, the leverage — because the politics turned hard this year. Over three hundred data center bills across thirty state legislatures in six weeks. Twenty-three states with an approved large-load tariff. New Jersey's Data Center Fair Share Act. PPL's rate case in Pennsylvania, where showing up cut a proposed increase roughly in half and produced the large load tariff and attached eleven million dollars in low-income assistance. Plus the wrinkle nobody's flagging: most of these tariffs protect the residential ratepayer. Your commercial rate class may not be named in the document at all. Four things to do Monday morning, and one line to remember — you can decide AI isn't for you, but you cannot decide the grid isn't for you.

  8. Sep 14

    Different Day, Same Problem (only Worse)

    For as long as I've been in this business, we've sent our requirements to a developer farm and gotten back exactly what we asked for — and it's never what we wanted, because we don't know how to tell anybody what we want. Then AI showed up, and a whole lot of people decided they didn't need the developer farm anymore. They'd just tell the AI themselves. Here's the problem: you still don't know what to tell it. You just found a faster, cheaper way to get exactly what you asked for — and the bill doesn't arrive on launch day. This week I go back to the oldest failure in software, because the trend keeps repeating and every lap costs more. PMI's research says poor requirements management is the primary cause almost half the time when projects miss their goals. Queensland Health's payroll system went live "after ten aborted attempts" — and when IBM argued a developer only knows what the business requirements tell it, the Commission of Inquiry said a vendor is "more than a software developer." NASA lost the Mars Climate Orbiter not because nobody wrote a units standard, but because "the SIS… was not followed." And Lidl spent roughly half a billion euros customizing SAP toward the way it had always done things before pulling the plug.

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About

Unreal IntelligenceExploring Artificial Intelligence, Technology, and the Future Artificial intelligence is changing how we live, work, create, and understand the world. But separating meaningful innovation from speculation, hype, and marketing isn't always easy. Unreal Intelligence explores the technologies, ideas, challenges, and opportunities shaping our increasingly intelligent world. From emerging AI capabilities and real-world applications to the broader implications of technological change, we examine what matters, why it matters, and what might come next. Whether you're a technology professional, business leader, developer, or simply curious about the future, Unreal Intelligence brings perspective to a rapidly evolving landscape. New episodes weekly. Sponsored and Produced by EKA Technology, LLC Humanizing AI.

Information

  • Creator
    Karl Sorochinski
  • Years Active
    2026
  • Episodes
    26
  • Rating
    Clean
  • Copyright
    © 2026 EKA Technology, LLC
  • Show Website
    Unreal Intelligence

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