Intropy Raises $11M to Revolutionize Parts Industry with AI

𝗜'𝘃𝗲 𝘀𝗮𝘁 𝗼𝗻 𝗯𝗼𝘁𝗵 𝘀𝗶𝗱𝗲𝘀 𝗼𝗳 𝘁𝗵𝗲 𝗜𝗧-𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝘁𝗮𝗯𝗹𝗲. 𝗬𝗲𝗮𝗿𝘀 𝗶𝗻𝘀𝗶𝗱𝗲 𝗮 𝗯𝗶𝗴 𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗳𝗶𝗿𝗺. 𝗧𝗵𝗲𝗻 𝘆𝗲𝗮𝗿𝘀 𝗮𝘀 𝘁𝗵𝗲 𝗰𝗹𝗶𝗲𝗻𝘁 𝗯𝘂𝘆𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝗳𝗶𝗿𝗺𝘀 𝗹𝗶𝗸𝗲 𝗶𝘁. So when AI hit, I knew exactly whose margin was about to quietly grow. The whole model sells one thing: human hours. Arbitrage. A pyramid of juniors. Billing by the man-month. Then AI made one engineer do the work of five. And here's the part nobody says out loud in the QBR: On a fixed-price contract, that saving doesn't come back to you. Same scope, far fewer hours — and the invoice doesn't move. As the buyer, I realized my leverage was highest right then. Before they repriced. So the conversation had to change. From hours to outcomes. With the AI savings written into the deal — not pocketed quietly. If your provider got way more efficient and your bill didn't budge? That's not a coincidence. It's a renegotiation waiting to happen. #CIO #ITServices #Procurement #AI

The fastest way to waste money on AI is to buy it because the demo felt smart. If you want a clean filter, ask the vendor for one thing: Show me the before-and-after math on one decision you actually make. Not “efficiency.” Not “automation.” A decision. Examples that count: - Interconnection: does this tool change the timeline, or just repackage the uncertainty? - Design: does it remove rework, or just make the first draft faster? - O&M: does it reduce truck rolls or downtime, and by how much, at your system size? - Commercial ops: does it change a bid, a pricing assumption, or a performance guarantee? If the vendor cannot name the decision, the owner, and the number that moves, you do not have ROI. You have software. The standard: - Decision: what changes? - Owner: who uses it? - Metric: what moves? - Threshold: what result makes it worth it? That is procurement, not hype.

A Frontier Is Not a Moat Michael Porter wrote your AI business case in 1996. Then he explained why it fails. The paper is "What Is Strategy?" The argument, in five words: operational effectiveness is not strategy. Porter drew a curve he called the productivity frontier. It represents the best cost-to-quality anyone can achieve with the available technology. A new tool pushes the curve outward. Everyone runs to the new curve. Everyone improves in absolute terms. Nobody gains an inch on anyone. *** He called the result competitive convergence. Firms become indistinguishable. And the productivity gains do not stay with the firms. They land with customers and suppliers. *** He had receipts. R.R. Donnelley was the leader in commercial printing, holding margins above 7% through the 1980s. It did everything right operationally. By 1995, its margin was under 4.6%. Now look at what we are doing with GenAI. Same models. Same vendors. Same contracts. Same quarter. This is the fastest-diffusing best practice in business history. Porter was worried that rivals would need a few years to copy you. We have compressed that to a procurement cycle. Every carrier and every bank is sprinting to the same point on the same curve, calling it transformation, and will arrive together with nothing to hand a shareholder. Porter's answer was not to stop improving. It was to stop mistaking the race to the frontier for a strategy. Which is the whole problem with scale. You cannot out-scale anyone on something you rent. Scale lives in what your vendor cannot sell to your competitor: your data, your feedback loops, your ability to deploy inside a regulated process. Build the pipeline. Rent the model. Unless you want to stand in a long line to pay for the taco, and then eat just the shell! #EnterpriseAI #FinancialServices #CompetitiveAdvantage

Your AI can reason. It just can't remember what your company already  learned.     So we solve the same problem twice. And we can't explain how we got the  answer.  Here's the gap: your systems store what. Nothing stores why.     Enterprise systems are the best in the world at recording what happened  and what you signed. But the why — the leverage, the rationale, the  "never again" — lives in one person's head. When they leave, it leaves  with them.  Company memory is the layer between the agent and the system of record.  It stores the cited why: leverage for procurement, an audit basis for  finance. Append-only. Timestamped. And it refuses to answer what it  can't cite.  The moat was never the model. A competitor can license the same one  tomorrow. The moat is everything your company has already learned —  remembered, and ready to use again.  One question I keep coming back to 👇 when the agent's memory and the  user's memory disagree on why a deal was won — whose version goes on  the record?  —     This is part 1 of 3. Next: what it looks like when the memory gets  written — and then used, cited weeks later.  #EnterpriseAI #AIAgents #KnowledgeGraph #Procurement #Finance #companymemory #secondbrain #institutionalmemory #memorysystems

$559 million in operating profit on ~$10.9 billion of quarterly revenue — Anthropic just ran the first profitable quarter in frontier-AI history. The scale of the turn is the story. Q2 2026 revenue came in around $10.9B (preliminary figures later topped $11.5B), up from $787M a year earlier. A year ago, the company told investors it did not expect operating profit before 2028. Compute cost per revenue dollar fell from $0.71 to $0.56 in a single quarter. The milestone is narrower than the headline. This is adjusted operating profit at roughly a 5% margin, and model training remains an enormous cash cost funded by investors. But the direction matters: at scale, serving frontier models can now generate margin. That effectively splits a frontier lab into two businesses — a profitable inference and API operation, and a capital-intensive training operation still running on raised capital. The industry question shifts from "can these labs ever earn?" to "which half of the model business are you exposed to?" For enterprise buyers, a supplier that can partly self-fund lowers vendor-viability risk and reshapes procurement leverage. But adjusted profit is not free cash flow, and training-driven capital calls continue. Read the margin definition, not the headline. Two years ago the question was whether frontier AI could earn. This quarter answered it — on the inference side, at least. If the profit sits in serving models and the losses sit in training them, which side is your AI vendor actually betting on? #MarketIntelligence #EnterpriseAI #AIStrategy #FrontierAI #BoardroomStrategy

This is a "Do Not Miss" event. Pam Krank knows what it takes to ensure on-time customer payments. This will be an action oriented discussion

This is a "Do Not Miss" event. Pam Krank knows what it takes to ensure on-time customer payments. This will be an action oriented discussion

Quoting happens in one tool. Approvals happen in another. Tracking lives somewhere else entirely, and documents get buried in a shared drive nobody fully trusts. None of these tools are broken on their own. The problem is that a single shipment has to pass through all five of them, and every handoff between systems is a place where something gets delayed, duplicated, or missed. We built Zavin around a different idea. One shipment should live in one place, with quoting, approvals, tracking, docs and invoicing connected on a single spine instead of scattered across five logins. If your team is still stitching five systems together by hand for every shipment, that is not a staffing gap. It is an architecture problem. How many tools does a single shipment touch on your team right now? zavin.ai #FreightForwarding #Logistics #SupplyChain #AI

Read an article this week about how the biggest winners of AI aren't going to be the tech companies, but the businesses with the thinnest margins. The argument was that a <1% reduction in coordination overhead costs for a 3% margin business creates a >25% profit increase. For manufacturers and supply chain teams, that math should stop you in your tracks. You're not sitting on 3% margins because your people aren't working hard enough. You're sitting there because coordination is expensive: emails chasing a PO status, phone calls confirming a shipment, spreadsheets reconciling what three systems already "know." The businesses that win the next decade in manufacturing and logistics won't be the ones with the flashiest tech stack. They'll be the ones that figured out how to strip friction out of the thousand small handoffs between people, systems, and partners that never made it into a KPI. Article: https://lnkd.in/gcEENN6h