DX Today | No-Hype Podcast & News About AI & DX
The DX Today Podcast: Real Insights About AI and Digital Transformation
Tired of AI hype and transformation snake oil? This isn't another sales pitch disguised as expertise. Join a 30+ year tech veteran and Chief AI Officer who's built $1.2 billion in real solutions—and has the battle scars to prove it.
No vendor agenda. No sponsored content. Just unfiltered insights about what actually works in AI and digital transformation, what spectacularly fails, and why most "expert" advice misses the mark.
If you're looking for honest perspectives from someone who's been in the trenches since before "digital transformation" was a buzzword, you've found your show. Real problems, real solutions, real talk.
For executives, practitioners, and anyone who wants the truth about technology without the sales pitch.
DX Today | No-Hype Podcast & News About AI & DX
Federal AI Preemption, NVIDIA's Agentic Storage Push, and the Stealth Model That Was Xiaomi - March 23, 2026
Use Left/Right to seek, Home/End to jump to start or end. Hold shift to jump forward or backward.
Welcome to the DX Today podcast, your daily deep dive into the AI ecosystem. I'm Mike, and joining me as always is Alex.
SPEAKER_00Morning, Mike. We've got a policy bombshell, an infrastructure story that quietly huge, and a pretty wild stealth model episode that tells you where the ecosystem is heading.
SPEAKER_03Three topics today. One, the White House just dropped a national AI framework that basically says no 50-state patchwork. Two, NVIDIA is trying to reinvent enterprise storage for agentic AI, not just GPUs, but the whole data stack. And three, the mystery hunter alpha model that devs thought was Deep Seek turned out to be Xiaomi and the details are fascinating.
SPEAKER_00Let's do it.
SPEAKER_03Topic one, the White House AI Framework. Alex, what actually happened here?
SPEAKER_00On March 20, the White House released a short set of legislative recommendations for Congress, a national policy framework for artificial intelligence. The headline is preemption. They're urging Congress to create a federal AI policy framework that overrides state AI laws that the administration views as unduly burdensome to avoid what they call a 50-state patchwork. That's a big swing. It is. And it's paired with three other pillars: child safety, energy and infrastructure, and a generally light-touch posture toward new AI regulators. The document explicitly tells Congress not to create a new federal rulemaking body for AI, and instead to use existing sector regulators plus industry-led standards.
SPEAKER_03So don't build an AI FDA, basically.
SPEAKER_00Exactly. And they propose regulatory sandboxes, controlled environments where companies can test AI applications without getting crushed by compliance overhead.
SPEAKER_03Okay, but the politics of preemption are always messy. What do they say states can still do?
SPEAKER_00They carve out a few buckets, states keep traditional police powers and generally applicable laws like fraud statutes, consumer protection, and laws protecting children. States keep zoning authority over where data centers go, and states can set rules for their own procurement and use of AI in things like education and law enforcement.
SPEAKER_01But states shouldn't regulate model development.
SPEAKER_00Right. The framework argues AI development is inherently interstate and tied to foreign policy and national security, so they want that primarily federal.
SPEAKER_03Let's talk child safety because I'm noticing that's the bipartisan hook in basically every AI bill now.
SPEAKER_00The framework leans into that. It recommends age assurance requirements for platforms likely to be accessed by minors, including things like parental attestation, plus robust parental controls, privacy settings, screen time, content exposure, account controls. It also calls for features that reduce the risks of sexual exploitation and self-harm for minors. And there's the deep fake angle. Yes. It cites the Take It Down Act as a major child protection initiative aimed at deep fake abuse. And it says Congress should ensure states can still enforce laws against child sexual abuse material even if it's AI generated.
SPEAKER_03Power, data centers, electricity.
SPEAKER_00The framework explicitly calls for streamlining federal permitting so large AI data centers can generate or procure on-site and behind-the-meter power generation. It also references a ratepayer protection pledge, arguing residential ratepayers shouldn't see higher electricity costs because of new AI data centers.
SPEAKER_03That's interesting because it's not anti-data center. It's build the AI factories, but don't make the public pay the bill.
SPEAKER_00Precisely. It's an attempt to square aggressive AI buildout with local political backlash about grid stream and power prices.
SPEAKER_03What's the technical subtext here? Like why does preemption matter to builders?
SPEAKER_00Compliance complexity. If you're training or deploying models nationally, 50 different state regimes can mean wildly different disclosure rules, liability theories, audit requirements, and duty of care standards. Preemption lowers variance, which can accelerate deployment. But the trade-off is you could also end up with one federal regime that's too permissive or too strict.
SPEAKER_03And it also changes who gets to set norm.
SPEAKER_00Exactly. If states can't legislate model development, then the locus moves to Congress, federal agencies, and courts, especially because the framework basically punts the biggest unresolved question, which is copyright training.
SPEAKER_03Yeah, I saw that lying.
SPEAKER_00The framework says the administration believes training on copyrighted material does not violate copyright laws, but it acknowledges there are arguments the other way and supports letting courts resolve it. It also floats the idea of collective licensing frameworks with antitrust protection, but without dictating when licensing is required.
SPEAKER_03So it's like we think it's fair use, but we're not going to lock it in legislatively.
SPEAKER_00That's my reading. They're trying to avoid a legislative move that would preemptively tilt the playing field before the courts settle it.
SPEAKER_03Alright, topic two NVIDIA and the storage story. This one feels nerdy, but I'm telling you, it's the kind of boring thing that decides winners.
SPEAKER_00Completely agree. At NVIDIA GTC 2026, Jensen Wong had a line that stuck.
SPEAKER_03Agents are basically little machines that get angry at slow I'm not sure.
SPEAKER_00Exactly. The article frames data as shifting from data lakes to data rivers, constant flow, constantly changing. The prepare data once mentality doesn't work. For AI, data preparation is continuous extraction, enrichment, classification, embeddings, indexing, semantic search.
SPEAKER_03And that isn't just compute, that's high performance storage and networking.
SPEAKER_00Right. Nvidia is pushing this concept of a new storage stack. The piece references NVIDIA's forthcoming Bluefield 4 STX storage system and calls NVIDIA STX a reinvention of the storage stack for AI factories. Yes. The strategic move is platform expansion. GPUs were step one, now it's networking, DPUs, and increasingly the data layer. And the piece says Nvidia is working with big storage vendors like IBM, Dell, and NetApp. It also claims 60 to 70% of the world's on-premises enterprise data is part of this picture, meaning enterprises are sitting on mountains of data that agents could unlock if you solved retrieval and governance.
SPEAKER_03Which gets to the AI factories framing. They're not selling a model, they're selling an industrial system.
SPEAKER_00Exactly. Think of it as an assembly line. Raw data comes in, gets cleaned and embedded, gets indexed, and then agents and models consume it at high speed. If that line stalls, your fancy model isn't useful.
SPEAKER_03And Alex, we've seen this movie before with databases.
SPEAKER_00Yes, when workloads shift, infrastructure shifts. In the relational era, you optimized for human queries and transactional consistency. In the agent era, you optimized for machine queries, high concurrency, massive random reads, vector search, low latency retrieval, and frequent updates. You also need strong lineage and security controls because an agent will happily pull sensitive docs if your permissions model is sloppy.
SPEAKER_03So the storage wars are coming.
SPEAKER_00Or they're already here. And NVIDIA is positioning to be the default architecture reference for how enterprises build AI native data systems.
SPEAKER_03Topic 3, the stealth model drama. I love these because they reveal how developers actually discover new capabilities now.
SPEAKER_00This one is a great snapshot. A free, unattributed model called Hunter Alpha showed up on OpenRouter on March 11th. Because it was anonymous and powerful, people started speculating it was Deep Seek quietly testing V4.
SPEAKER_03Because the spec sounded like a DeepSeek thing, right?
SPEAKER_00Right. Reuters reports the model's profile advertised a trillion parameters and up to a 1 million token context window. That combo matches what the community expects from a next-gen Chinese open model.
SPEAKER_03And then the reveal, Xiaomi.
SPEAKER_00Exactly. Reuters says Xiaomi's AI team, MIMO, led by a former DeepSeek researcher, stated Hunter Alpha was an early internal test guild of MIMO V2 Pro, which they described as a flagship model intended to be the brain of AI agents.
SPEAKER_03There's that word again. Agents.
SPEAKER_00Yep, it's motif today. The story says MIMO V2 Pro would partner with five major agent frameworks, including something called OpenClaw, offering a week of free access to developers worldwide.
SPEAKER_03That's a distribution strategy.
SPEAKER_00It is. And the model router dynamic is important. OpenRouter lets developers query many models through one interface, so it becomes a natural place to test new systems and collect feedback. ReutersNotes stealth launches are not unusual. An anonymous Pony Alpha model appeared earlier and was later confirmed by another Chinese firm.
SPEAKER_03The stealth launch is like dropping a mixtape on SoundCloud.
SPEAKER_00Great analogy. But there's a governance angle. ReutersNotes Hunter Alpha's profile page said prompts and completions are logged by the provider and may be used to improve the model. That's standard. But when the model is anonymous, developers may not know who is collecting that data at first.
SPEAKER_03Which is going to make enterprise security teams freak out.
SPEAKER_00Yes. Expect more approved model gateway tooling, logging, and red teaming around these distribution channels.
SPEAKER_03One more point. The story says adoption was huge.
SPEAKER_00Reuters reports the model was adopted rapidly after appearing, and that Mixtrill said it surpassed 1 trillion tokens in total usage and topped open router leaderboards.
SPEAKER_03That's insane scale for an oops you found our test build.
SPEAKER_00It tells you two things. First, developers are constantly hunting for better price performance, and a free high capability model triggers immediate migration. Second, the agent shift is happening fast. Teams want brains that can run tool-using workflows, not just chat.
SPEAKER_03All right, let's connect the docs across all three. Here's my take. Policy is trying to standardize the rules, infrastructure is trying to standardize the stack, and distribution is trying to standardize access.
SPEAKER_00I like that. And there's an undercurrent, the center of gravity is moving from a single chatbot experience to an agent ecosystem, with storage and data pipelines as the core, and with policy debates increasingly tied to kids' safety and energy constraints.
SPEAKER_02Which makes the question for builders: are you optimizing for the next model release or for the system that lets you swap models constantly?
SPEAKER_00Exactly. If your architecture assumes one model forever, you're going to be in pain. The winners will treat models as interchangeable components and focus on data, evaluation, governance, and cost control.
SPEAKER_03That's all for today's episode of the DX Today podcast. Thanks for listening, and we'll see you next time.