Jeremy Levy

🧭 2) DeSantis Emerging as a Political AI Skeptic Recent news : including from Politico, frames Florida Gov. Ron DeSantis not just as a critic of big tech, but as a growing skeptic of AI and its infrastructure needs: • A headline from Politico states: “We have to reject that with every fiber of our being”: DeSantis emerges as a chief AI skeptic. This reflects a broader political narrative, not just grid concerns: skepticism about AI’s societal impacts, which too often gets tethered to infrastructure debates like data centers.

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Brian Lamb and his team at Promissory are building a modern platform for stacking trusts and maximizing QSBS. He answers all of the critical questions around how these trusts can be used, and who they're best suited for, on this week's episode of "QSBS, Solved". Listen: https://lnkd.in/eVjXnEKC

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Venture funding isn't success of course, but it's nearly impossible to win big markets without it. Unlike the AI hype, Ando is very, very real. And unlike others, Ando is here to make lives genuinely better for everyone. (*) #proudinvestor #micdrop #oversubscribed Big thx to Bradford Oberwager and James Kairos, Ben Metcalfe, Andy Mutz, Joseph Chittenden-Veal, Scott Sanders, Massy Ghausi, John Lyman, Thomas Wesley, John Holder… and of course Phoenix Toews and Paul Wellons (to name a few) — (*) Yeah yeah, some pencil-pusher will use AI to over-optimize profits over people, but as American Airlines is learning the hard way (WSJ: https://lnkd.in/ePz_QNzt ), net of competition, "people businesses" don't work that way. You HAVE to care for people or you lose. Ando plays a beautiful collaborative role in that dialogue, helping profit-focused management more clearly see the effects of a motivated labor force. That's why I'm truly #proudinvestor --- Posts co-authored with my LinkedIn secret weapon Dennis Buckley

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One of the most interesting funding announcements in the AI tooling space lately: 🎙️ Wispr Flow has now raised $81M (with a fresh $25M led by Notable Capital, plus Steven Bartlett’s Flight Fund) to build a voice-native OS for work. 💸 Backers include investors behind Airbnb, Slack, TikTok, Anthropic, and more — a strong signal that voice interfaces are the next wave of computing. 👉 If you’re building with AI daily (agents, copilots, workflows), it’s worth experiencing what a serious Voice OS feels like: 🔗 https://lnkd.in/ga7tzxZu

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I sat down to build an agent orchestrator. Soon later I was learning tmux integration, Claude Code hooks, and all the things that can go wrong. Seth Godin called it yak shaving. Twenty-one years later, the yaks have learned to code. AI makes building faster. It doesn't make the dependency chains shorter. The bottleneck moved from "write the code" to "build everything the code depends on." New post on the otageLabs blog. https://lnkd.in/g5ZHXcFA #AI #SoftwareDevelopment #AIEngineering

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My two cents on the Claude Squeeze Application layer AI companies being built right now will not survive if they incorporate human in the loop as a core part of how work gets done. If your agent requires a human to approve the output, you’re building a better UI for Claude. If your agent helps an accountant do more accounting or a lawyer do more lawyer-ing, you might have a business, but companies won’t pay a lot for it. It’s not that Claude is going to steal your customers. It’s that the delta between what you’re offering and what someone gets from just using Claude directly is getting smaller every month. The complexity you’re solving for, better UX, smarter prompts, cleaner folder structures, is real, but it’s not worth a big check. And buyers are figuring that out. I’m already seeing it. A real estate brokerage looking at AI underwriting tools realized they can dump a rent roll straight into Claude and get 80% of the output for free. They’ll pay a little more for a better interface, but not a lot more. If you’re not doing the job end-to-end, your value is the productivity layer. And Claude is coming for the productivity layer. Hot take: I don’t think Harvey would get funded in 2026. They were funded when they were because the models weren’t advanced enough to reveal how thoroughly they’d eat the application layer. Harvey may survive, they’ve built real brand and enterprise relationships. But with that exact pitch today I think you’d struggle. Here’s what I think survives: Agents that own the outcome. They have to clear every one of these bars: 1. Fully autonomous. No human in the loop. The agent does the job, not assists with it. 2. Accountable for outcomes. Not activity. Results, and the agent is measured on that basis. 3. Mission-critical metrics. The outcomes it owns show up in the Monday morning operating review. The numbers the CEO reports. Going back to Harvey, if they said we’ll just do your legal work for you, end-to-end, accountable to outcomes, that’d be a defensible business in 2026. But notice what it is: outsourced labor with AI as the delivery mechanism. Not a productivity tool. A law firm that doesn’t charge law firm rates. Most AI companies won’t make that leap because it means owning outcomes. It’s hard. That’s what we’re building at Uniti. If you can autonomously run the entire leasing funnel for a property manager and own their rentals-per-month and conversion rate, you’re not a software vendor. Then do the same for all their workflows. You’re outsourced labor. Completely different moat. The mistake is selling the tool when they should be doing the job. If there’s a human in the loop, the customer is paying for the human’s time and your software. As models get better, the human becomes the cost center and your product becomes the overhead. The comparison isn’t your tool vs. Claude. It’s your agent vs. a full-time hire. That’s a different conversation, and a new category.

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Ontologies remain one of the most effective ways to ground AI memory, keeping extracted knowledge anchored to existing structures and adaptable through feedback. As memory systems evolve, their role remains essential, and a core focus at Cognee.

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More proof assets do not equal credibility. When proof is thin, don't make loud claims. Use a repeatable way to prove what you’re saying. The failure mode is simple. Startups makes claims without evidence and buyers can see the guess work As a PMM fix it with stacked proof One layer per core claim and standardizes it- claim → proof → measurement. Level 1: Mechanism proof Share screen flow, workflow diagram, demo steps that customers can relate to Level 2: Workflow proof Show steps removed, steps added, handoffs changed in the customer's process Level 3: Early signal proof Activation, time-to-first-value, usage frequency, retention trend, pilot completion. Level 4: Outcome proof Show potential win rate, conversion, cycle time, costs saved, revenue impact. Only if you can attribute it. Operating rule: Do not publish a claim above your highest proof level. If you don’t have outcomes, you still have options. Run a pilot to validate what is sticking and that can be the proof point Define the pilot user and workflow. Define one success metric tied to the workflow. Define one adoption metric tied to usage. Define the time window. Show what “good” looks like. At Awvent when we had no customer we applied this process with countless potential customer..refined it with every feedback...eventually landing our first customer ( always a sweet feeling) As early stages startup this is one way you stay credible while the product is still earning outcomes.

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