Cool Insight, Bro: The Meat Proxy in Your Boardroom
AI agents have made insight free and endless, so everyone in the meeting has a chart and nobody can prove theirs is right. The scarce skill now is selection: simulate every option, then ship the one that wins.
Welcome to the commoditization of intelligence.
We are no longer paid to think. We’re just automated livestock for an algorithm.
The machine ingests the data, chews the numbers, and regurgitates a fully formed, beautifully formatted strategy deck. Your only job in the corporate ecosystem is to act as the human tapeworm: swallow it whole, slide down the hallway, and dump it out onto a meeting table while maintaining direct, unblinking eye contact with the CEO.
You didn’t analyze anything. You didn’t question anything. You provided the wet, fleshy transit system between Claude and the executives (and get real, what are the executives doing?).
There’s a word for what everyone is actually doing: routing.
We aren’t strategizing. We aren’t analyzing. An agent read your data, wrote the logic, designed the layout, and picked the takeaway. Our only job was pasting their name on the title slide and adding a little performative conviction to the delivery (eye contact, buddy).
A developer named Niklas Gruhn already coined a term for this in August: meat proxy. The flesh-and-blood bandwidth for a prompt.
Analytics was supposed to prevent this bullshit. Get the data, hire the data people, build the dashboard, and decisions stop being about who screams the loudest. That held up pretty good over the last 10-15 years as data science became a real thing and a very important kind of hire.
But now your data teams turn out insights faster than ever. And now every manager with an API key runs an agent that does a passable effort, on demand, with terrifying confidence. The bar to generating intelligence has never been lower.
So everyone has a chart. Everyone has receipts. A dozen human meat proxies sitting around a Zoom box, furiously defending an insight none of them created, none of them can prove, and none of them would recognize if it walked in wearing a different font.
To be clear, these insights aren’t total bullshit. A lot of them are pretty damn good.
The problem is basic economics: when the supply of intelligence goes to infinity, Econ 101 says the price hits zero. Why the hell would anyone trust one smart person over another, or one smart agent over another, when both cost fucking nothing and both sound completely right?
Cool insight, bro.
Selection is the new intelligence
When intelligence is free, the only scarce asset left is selection.
Which of the insights on the table is actually right?
Which one is worth acting on?
…and which one won’t accidentally set your supply chain on fire?
That’s what simulation is for. Take every candidate decision, and sort them out. All the way out.
So how do you decide which AI insight to act on? Run every one of them against a model of how your business actually behaves, rank them by projected outcome and confidence, and pick the single best one.
Because being wrong costs nothing in a simulator, but it costs a full fucking quarter in production.
We’ve written about what the alternative looks like: a logistics company testing “reasonable insights” one at a time in production for two years. A simulation built across a data ontology ranked them by impact and identified direct causal drivers in an afternoon.
Enter SimOps
TODO (editor): insert Ayman’s staging environment example. Engineers don’t test on prod, but your executives are. They need a testing environment. Simulations are the way to do that, but it was only recently that running simulations at scale was even viable.
Gartner already has a name for this. Its September 2026 research, Unleashing SimOps, describes SimOps as “the future operating model for AI-first business autonomy”, a way for IT and business teams to keep improving operational decisions with AI guidance.
Since somebody’s going to write the official definition eventually, here’s ours:
Simulation Operations (SimOps) is the discipline of running every recurring business decision through a simulation of the enterprise, ranking candidate actions by projected outcome and confidence, and delivering the selected action directly to operational systems, applications, and agents. Humans govern the policies. The pipeline executes the decisions.
(Unrelated to SIMOPS, the oil and gas term for simultaneous operations, or the SimOps conference for healthcare simulation teams.)
At RootCause.ai, the simulation runs on a Causal Model of the business built rapidly, on-demand using the new SPARC-fast model.
Instead of the output going to a manager who pastes it onto a slide for an executive to squint at, it goes straight to wherever the action happens. Push it through an SDK into your own core systems. Hit it as an API. Expose it over MCP so your stupid agents test out all the options before they come to you with mewling, sycophantic nonsense.
- New tariff announced? Don’t convene an emergency task force. Simulate the impact across your operations, simulate the best counter-moves (reroute, reprice, pre-buy, hedge, eat it), rank them by margin preservation, and push the optimal strategy straight into procurement and pricing. Before your competitors finish reading the headline.
- Bad weather in the forecast? Simulate demand for the week ahead. Simulate the promos, the push notifications, the site discounts, the inventory shifts. Pick the strategy with the best outcome and let marketing automation and inventory systems run it. Nobody needs to write a memo about rain.
Every recurring decision is a candidate. If it happens more than once a quarter and a human decides it by staring at a line graph until their eyes bleed, it’s a SimOps target.
The pipeline runs the loop: a signal comes in, candidate responses get simulated, outcomes get ranked, and the winner gets delivered directly to whatever system has to act. The result feeds back to update the model. And every decision that runs is logged with its expected outcome and its confidence, which is a far more rigorous audit trail than any steering committee has ever produced in its life.
This is what RootCause.ai does. We run simulations across your messy data, where it lives, and make the output operational. Take demand planning. Your forecast was right the day you built it and wrong by the time S&OP met to approve it, because a tariff landed, a competitor cut price, and a storm parked over your biggest DC. The monthly replanning cycle can’t keep up, so planners override the model by hand and the model learns nothing. With SimOps, every one of those signals triggers a fresh simulation of the demand plan against the alternatives (shift allocation, pull forward inventory, reprice the promo, hold), the winner goes straight into the planning system, and the humans set the rules instead of redoing the math. The forecast stops being a document and becomes a pipeline.
Simulations and outcomes
Analytics and Insights is dying. What you want to focus on are Simulations and Outcomes.
There are too many important people with “great ideas” now. Every single VP has an agent. Every single manager has a chart. Every single desk has a pipeline of plausible, well-cited, beautifully formatted intelligence.
When anyone with an internet connection can generate intelligence for free, having a smart idea isn’t a competitive advantage anymore. The org that wins is the one that tests all of them, ranks them by hard outcomes, and wires the winner straight into operational systems.
It doesn’t need to figure out who’s the smartest person in the room. It doesn’t need the room. It just needs to automate the execution of being right, over and over again.
That gap doesn’t show up in a single quarter. It shows up when your competitor has been auto-responding to tariffs, weather, and demand shocks for a year, and you’re still forwarding your agent’s favorite pitch deck to a steering committee like a good little human router.
Bring us a decision you make every week. We’ll simulate it. We like skeptics. We like shit-talkers even more.