Eye On A.I.

The same tools that slowed the U.S. military down in Afghanistan (PowerPoint, Excel, email, and Word) are now slowing American businesses down in the AI race. Drew Cukor spent 30 years as a Marine intelligence officer, helped build Project Maven into a battlefield command and control system, served as Chief Data Officer at JP Morgan, and is now leading AI transformation at TWG AI. In this episode, he joins Craig Smith to make a case that most enterprise AI strategies are fundamentally broken, not because the technology isn't there, but because companies are storing their data in Microsoft file folders where it becomes inaccessible to AI, appointing AI officers who block progress rather than enable it, and mistaking chatbot deployments for transformation.
Cukor's prescription is specific: take a company's core workflows apart, how it acquires customers, delivers services, handles back office operations, and rebuild them from scratch with AI embedded throughout, protected inside Palantir Foundry, delivered within 36 months, with the CEO owning the outcome rather than delegating it to a CTO or a made-up AI officer role. The stakes, he argues, are not abstract: China is going AI-native from the start without the legacy infrastructure that's slowing American enterprise, token spend is approaching the cost of a human salary making poorly designed AI workflows as expensive as bad hiring decisions, and the window for acting is closing. The most important video he recommends any business leader watch isn't one where the AI wins, it's the footage of Lee Sedol losing to AlphaGo and realizing mid-game that he no longer understands how the game works. That moment, Cukor says, is coming for every legacy business that doesn't move now.
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Hundreds of billions of dollars are flowing into AI data centers right now, and Paolo Ardoino, CEO of Tether - the company behind the world's most widely used stablecoin with 573 million users - thinks that investment is going to age very badly. In this episode, he joins Craig Smith to explain QVAC, Tether's open-source platform for running AI on smartphones, laptops, and edge devices, and to make a case that within five years, 90% of what ordinary people use AI for will run entirely on consumer hardware, without touching a data center. The evidence is already there: Tether's team built a 4-billion-parameter medical AI model that outperforms Google's 27-billion-parameter MedGemma, running on a good smartphone, and a 1.7-billion-parameter version that runs on the average $80 smartphone available in Africa.
The deeper argument in this conversation is philosophical as much as technical. Ardoino applies the same disintermediation logic that made sending dollars to the world's unbanked free - zero transaction fees, revenue from treasury bill interest - to AI: "not your AI, not your intelligence." If you don't control how your AI runs and your data never leaves your device, the AI is genuinely yours. If it does, someone else is getting smarter with your information. He also makes a pointed economic argument: the AI companies currently charging $200 for subscriptions that cost $1,000 to $5,000 to deliver are subsidizing growth while private, and when they go public, retail investors will absorb the gap. His prescription isn't to stop building, it's to build differently, toward millions of small interacting models rather than trillion-parameter monoliths, toward devices that think locally rather than systems that route everything through Ireland and back.
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The AI chip market looks monolithic from the outside - NVIDIA dominates, and everyone else is fighting for scraps. But d-Matrix's CEO Sid Sheth argues that the market is quietly splitting into two distinct tiers, and the one that's exploding right now is the one NVIDIA's architecture isn't built for. In this episode, Sid joins Craig Smith to explain the "premium token economy": a new class of AI inference where interactivity is the product, users pay ten times more per million tokens for instant responses, and the memory bandwidth limits of GPU-based systems create a structural ceiling that purpose-built architectures don't have.
The conversation is unusually candid about what AI actually looks like at the executive level: Sid describes using Claude as a sounding board for M&A strategy, producing full integration plans in 15 minutes that used to require entire banking advisory teams, and watching AI shift from a tool that echoed his ideas back at him to one that genuinely disagrees, flags what he missed, and pushes back with enough confidence to be useful. He also makes the case that we're at the beginning of a shift from individual agents to what he calls "organizational AI" - teams of agents running entire company functions at a high level of abstraction - and that the infrastructure bet d-Matrix is making positions them directly in the path of that wave.
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Most enterprise IT teams spend the majority of their time fighting the same fires repeatedly. BMC Helix is building the AI system that handles those fires automatically, detecting anomalies, tracing root cause through millions of asset relationships, generating remediation plans, and learning from every incident it resolves.
Craig Smith sits down with Erhan Giral, VP of AI Strategy and Innovation at BMC Helix, and Ryan Manning, Chief Product Officer at BMC Helix, to explain how agentic AI is transforming IT service management from a reactive, human-driven process into something closer to a self-healing system, and why doing that at enterprise scale requires a fundamentally different architecture than most AI deployments attempt.
The most technically interesting part of this conversation is where BMC Helix is headed: building "gyms", synthetic data center environments where AI agents deliberately break things and learn to fix them overnight, 24 hours a day, generating the bespoke operational training data that text-based foundation models can no longer provide.
Erhan describes an architecture of specialized sub-agents, anomaly detection, log analysis, root cause analysis, remediation planning, that work in a hierarchy, passing hypotheses between each other until they converge on an answer, fine-tuned to reason the way a specific enterprise's best IT engineer would rather than the way a generic documentation page reads. For customers, the results are measurable: 25 to 50% cost reduction, fewer recurring outages, and IT staff who can finally go home at a predictable time rather than spending their nights firefighting problems that could have been prevented.
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Most companies think they're transforming with AI. They're not, and the gap between what they believe and what's actually happening on the ground is costing them far more than they realize.
In this episode, Craig Smith sits down with Chris Blackburn, founder and CEO of Liatrio, a consultancy that has spent a decade embedding directly inside large enterprises to help them actually change how they work, not just what tools they use.
The conversation opens with a striking data point: the average enterprise Blackburn works with operates at just 5 to 6% efficiency, meaning employees spend only three to three-and-a-half hours per week on work that genuinely creates value, compared to Toyota's benchmark of 70%. The core argument is that AI is being applied to the wrong part of the problem: individual productivity gains don't flow through to the bottom line if the organizational system around the individual - the approvals, handoffs, bureaucracy, and middle management layers - stays exactly the same.
Blackburn introduces a concept he calls "strangling the enterprise": rather than trying to transform a 5,500-person organization all at once, build a small, low-bureaucracy unit inside it that operates with radical autonomy, proves the model works, and expands outward. 
The episode closes with a frank conversation about what real transformation actually costs: roughly half of total compensation spend across the organization, sustained for two years, a number Blackburn describes as "absolutely insane" and one he believes most CFOs aren't yet prepared to confront.
Key Topics Covered:
●     Why the average enterprise operates at 5-6% efficiency, and what Toyota's 70% benchmark reveals about the scale of the opportunity AI could unlock
●     The critical distinction between individual productivity gains and system-level improvement, and why saving an hour doesn't automatically improve the bottom line
●     "Strangle the enterprise": how to build a small, autonomous AI-native unit inside a large organization rather than trying to transform the whole thing at once
●     Why most CEOs are dangerously disconnected from the actual work being done, and what McKinsey says about how much time they should be spending on transformation
●     What AI transformation actually costs: roughly half of total compensation spend, sustained over two years, and why most CFOs aren't ready for that number
●     Why AI isn't just changing jobs but changing life - from shorter work weeks to longer health spans - and what the farming analogy reveals about how slowly societies absorb new productivity
As enterprises pour money into AI tools while reporting little bottom-line impact, this conversation offers the most operationally honest account available of why that gap exists, and what organizations that actually want to close it need to be willing to do differently.
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Connect with Chris Blackburn
LinkedIn: https://www.linkedin.com/in/chrisblackburn