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Fiduciary Duty in the Gray Zone: What Boards Must Know About Converging Geopolitical and Technology Risks

Fiduciary Duty in the Gray Zone: What Boards Must Know About Converging Geopolitical and Technology Risks

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While global leaders gather in Davos to discuss "Cooperation in a Contested World," corporate Boards face a starker reality: your fiduciary duty now extends into the gray zone. This is not the world of traditional risk management. It is a domain where Advanced Persistent Threats (APTs) exploit hardware-level vulnerabilities to exfiltrate IP, where fragmented AI policies across 50 U.S. states create compliance nightmares that competitors exploit, and where nation-state actors target your brand equity as a geopolitical weapon.

The question is no longer whether your company will be targeted, but whether your Board understands that cybersecurity, AI governance, and geopolitical risk are now inseparable from corporate strategy and shareholder value.

 

The Convergence at Davos

In his analysis of the World Economic Forum's 2026 gathering, my friend and colleague Ray Wang posed a critical question that should keep every Board member awake at night: "In 2026 and beyond, will countries and companies have to choose a side between China vs the United States?" As Ray noted, the deepening rivalry between these powers creates a mutually exclusive relationship where strategic alignment with one comes at the expense of ties with the other. For Boards, this is not an abstract geopolitical debate. It is a daily operational reality that intersects with three immediate threats I want to address directly.

First, your hardware supply chains are compromised at levels most Boards do not yet comprehend. Second, the Balkanization of AI policy across both the United States and the rest of the free world creates strategic asymmetries that favor competitors operating under unified national frameworks. Third, gray zone conflict has made your company a target whether you realize it or not, and the attacks are designed to stay below the threshold that would trigger a traditional security response or even Board awareness.

Let me be direct: if your Board's risk committee is still treating cybersecurity as an IT issue, AI as a compliance checkbox, and geopolitics as someone else's problem, you are already behind.

 

Hardware Vulnerabilities: The 15-Year Systematic Campaign You Didn't Know Was Happening

During my time first as a Senior National Intelligence Service Executive and later as Chief Information Officer at the Federal Communications Commission, I confronted a reality that most corporate Boards still have not internalized. Advanced Persistent Threats are not opportunistic hackers looking for quick wins. They are nation-state operations with 15-year time horizons, systematic discipline, and one objective: position themselves so deeply in your infrastructure that by the time you discover them, it is too late.

These actors do not just exploit software vulnerabilities. They compromise hardware at the manufacturing level, embedding backdoors in chips, routers, and firmware that your security teams will never detect with conventional tools. They target the supply chain chokepoints where a single compromised component can give them access to thousands of downstream customers. And they are patient. They will sit dormant in your systems for years, exfiltrating IP, monitoring communications, and mapping your network until the moment they need to activate.

Ray's analysis highlighted that over $6 trillion will be invested in AI infrastructure by 2030. But here is what that statistic obscures: every data center, every AI accelerator chip, every network switch in that infrastructure represents a potential entry point for APTs if Boards do not demand hardware-level security verification from their vendors.

The strategic question for Boards is this: do you know where every critical component in your infrastructure was manufactured, by whom, and under what security protocols? If the answer is no, you have a fiduciary exposure that goes far beyond traditional cyber insurance.

 

The 50-State AI Policy Fragmentation: A Gift to Your Competitors

I have spent the past two years working with policymakers and industry leaders on AI frameworks on both sides of the aisle as well as external to the United States. What I have witnessed is a policy disaster unfolding in slow motion, and it is creating competitive disadvantages that most Boards have not yet quantified.

Right now, your company must navigate conflicting AI regulations across 50 U.S. states, each with different definitions of algorithmic accountability, data privacy, and bias testing. California has one framework. Texas has another. New York is developing a third. Meanwhile, your competitors in China operate under a unified national AI strategy with clear guidelines, centralized resources, and government backing.

This is not just a compliance cost issue, though those costs are real and growing. It is a strategic speed issue. While your legal team is parsing whether your AI model meets the requirements of all 50 states, your competitors are iterating, deploying, and capturing market share under coherent national policies.

The fragmentation also creates security vulnerabilities. When compliance requirements conflict across jurisdictions, companies often default to the lowest common denominator or create patchwork solutions that leave gaps. APTs and gray zone actors exploit these gaps ruthlessly. They study your compliance posture, identify the seams between state regulations, and target the vulnerabilities that emerge from trying to satisfy everyone.

For Boards, this raises a governance question that transcends the legal department: are you building AI systems that are merely compliant, or are you building systems that are strategically resilient in a contested global environment? There is a difference, and it matters.

 

Gray Zone Conflict: Your Company Is Already a Target

Let me introduce you to a concept I have been writing about for years: gray zone conflict. This is the space between peace and conventional warfare where state and non-state actors use cyberattacks, disinformation, economic pressure, and IP theft to achieve strategic objectives while staying below the threshold that would trigger a military response or even public awareness.

Your company is operating in this zone right now, whether your Board acknowledges it or not. And the attacks are not random. They are targeted, systematic, and designed to achieve one of three objectives: financial gain through ransomware or extortion, brand damage to undermine market position or public trust, or access to your secrets and IP to accelerate a competitor's capabilities or a nation's strategic industries.

I have seen gray zone operations unfold in real time during my career in the intelligence community and as a federal CIO. The sophistication is breathtaking. Adversaries will spend months studying your organization, identifying key employees, mapping relationships, and crafting social engineering campaigns that exploit human psychology, not just technical vulnerabilities. They will compromise a mid-level employee's personal device, pivot to corporate systems, and exfiltrate terabytes of data over months while your security operations center sees nothing unusual.

The brand damage operations are equally insidious. Adversaries will seed disinformation about your products, manipulate social media to amplify customer complaints, or leak selectively edited internal communications to create reputational crises that tank your stock price. And because these operations stay below the threshold of overt attack, your crisis communications playbook is often useless.

Here is what keeps me up at night: most Boards do not have visibility into gray zone threats until after the damage is done. Your quarterly risk reports focus on traditional metrics like cyber incident response times or compliance audit results. But gray zone operations are designed to evade those metrics. They succeed precisely because they do not trigger the alarms your systems are designed to detect.

 

What Boards Must Do: From Risk Management to Strategic Foresight

I have spent my career leading what I call "near impossible missions," from modernizing legacy systems at the FCC to directing technology-enabled bioterrorism responses to 9/11, anthrax in 2001, SARS in 2003, and more. The common thread in all these experiences is that traditional risk management frameworks are insufficient when you are facing adaptive, intelligent adversaries in rapidly changing environments.

Boards need to shift from reactive risk management to proactive strategic foresight. This means three things.

First, demand hardware-level security verification. Your procurement processes must include rigorous supply chain security assessments that go beyond vendor questionnaires. You need to know the provenance of every critical component, the security protocols at every manufacturing facility, and the verification methods that ensure no tampering occurred. This is not an IT issue. It is a Board-level strategic sourcing issue that affects the integrity of your entire operation.

Second, advocate for federal AI policy leadership. The 50-state fragmentation is not sustainable, and it is not in your shareholders' interests. Boards should be vocal in calling for light-touch federal frameworks that provide clarity, consistency, and competitive parity with other nations. This is not about stifling innovation. It is about creating the conditions where American companies can compete globally without one hand tied behind their backs by conflicting state mandates.

Third, build gray zone resilience into your governance model. This means expanding your risk committee's mandate to include geopolitical threat intelligence, not just cyber metrics. It means conducting tabletop exercises that simulate gray zone scenarios like IP theft campaigns, brand sabotage operations, or supply chain compromises. And it means developing decision elasticity, which is the ability to respond rapidly to ambiguous threats without waiting for perfect information or consensus.

 

The Agency Paradox and the Future of Corporate Governance

Throughout my work, I have observed what I call the Agency Paradox. As our technological tools become exponentially more powerful, our collective sense of human agency often feels increasingly fragile. For Boards, this paradox manifests in a troubling way: the more data and AI capabilities you have, the more you may feel overwhelmed by complexity and uncertainty rather than empowered by insight.

The solution is not to retreat from technology or to hand over decision-making to algorithms. The solution is to develop what I call decision elasticity, which is the ability to use AI and data to gather intelligence at scale while maintaining the nuanced, ethical judgment that only humans can provide. In the context of gray zone threats, this means using AI to detect anomalies and surface threats, but relying on human judgment to interpret ambiguous signals, assessing strategic context, and making decisions that balance security with values like privacy, transparency, and due process.

The most forward-thinking Boards I work with are already making this shift. They recognize that cybersecurity, AI governance, and geopolitical risk are not separate silos. They are interconnected dimensions of a single strategic challenge: how do we build resilient organizations that can thrive in a contested, rapidly changing world while preserving the human agency and ethical judgment that define great companies?

In Part 2 of this series, I will explore how Boards can operationalize this strategic foresight through AI-augmented defense and human-machine partnerships that balance speed with wisdom.

 

An Invitation to Deeper Dialogue

If your Board is grappling with these converging threats, if you recognize that traditional risk frameworks are insufficient, or if you simply want to stress-test your current approach against the realities of gray zone conflict and geopolitical competition, I invite you to engage in a deeper conversation.

My work as both as a Board Member and a senior advisor is major compaies is dedicated to helping Boards develop the strategic foresight and decision elasticity needed to navigate what I call "tech tectonics," which are the seismic shifts beneath the surface of global business. This is not about selling you a technology solution or a compliance framework. It is about building the governance capacity to make wise decisions in conditions of radical uncertainty.

The stakes could not be higher. As Ray noted in his Davos analysis, we are entering an era where the nature of work, the meaning of human existence, and the future social order are all in flux. For corporate Boards, the question is whether you will shape that future proactively or react to it after your competitors, your adversaries, and the market have already moved.

I have spent my career wrestling with these challenges, and I use the word "wrestling" deliberately. Leadership in this era is not a graceful dance. It requires constant engagement, humility, and a commitment to co-creating a future where technology serves human dignity rather than constraining it.

Your fiduciary duty now extends into the gray zone. The question is whether your Board is ready to govern accordingly.

 

Dr. David Bray is both Chair of the Accelerator and a Distinguished Fellow at the non-partisan Stimson Center as well as Principal and CEO at LeadDoAdapt Ventures, Inc. He previously served as a non-partisan Senior National Intelligence Service Executive, as Chief Information Officer of the Federal Communications Commission, and IT Chief for the Bioterrorism Preparedness and Response Program. Business Insider named him one of the top “24 Americans Changing the World” and he has received both the Joint Civilian Service Commendation Award and the National Intelligence Exceptional Achievement Medal. The U.S. Congress invited him to serve as an expert witness on AI in September 2025. He also advises corporate Boards and CEOs on navigating the convergence of AI, cybersecurity, and geopolitical risk.

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Can robots do boring work, save money, never complain? (Distillation Aftershots)

Can robots do boring work, save money, never complain? (Distillation Aftershots)

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Welcome to a new edition of The Board: Distillation Aftershots (*).

 

This newsletter shares curious and interesting insights and data points distilled from enterprise technology to identify what’s notable.  If you want to subscribe to the newsletter, in your inbox every Sunday morning, please click here.

 

In this issue, we will continue our discussion of robots and robotics. Previously, I wrote about humanoid robots and their limited usefulness. Today, I’d like to talk about functional robots… the ones that don’t look like us but do a good job.

 

First, my take. 

 

Last week, I made my case against humanoid robots: they are cute, work in movies, make me laugh, but they’re not valuable to businesses (for the most part; as with anything else, use cases apply). Many reasons, mostly related to efficiency versus costs (how much it costs to make a humanoid work like a human, when there’s no need for such complexity). 

 

This week, I want to discuss the opposite: the ROI inherent in non-humanoid robots and the efficiency they deliver as they move from innovation spend to infrastructure ROI. Treat is a system, not a tool, and it locks in structural cost and competitive advantages.  It is no longer a novelty; it works.

 

As shown in the resources below, large organizations have turned to robots to save time and money.  Whether we are talking about reducing health and life risks, speeding up processes, reducing costs, operating 24/7, automating manual tasks efficiently, or a combination of all these, we have proven that they work.  What started in the 1980s and 1990s in manufacturing plants became more impressive for smaller outfits as robotics improved (and costs decreased, naturally).

 

Whether it’s Amazon running a dark warehouse where Roomba-like robots move merchandise 24/7 to speed up shipping, or UPS loading and unloading trucks to balance labor and health issues, these scenarios should spark the imagination of any other organization.  Are there any repetitive tasks in your processes that could benefit from non-stop or safer operations?  Are there menial tasks that are taking valuable time from team members who could be working on higher-revenue tasks?  That’s the starting point.

 

More and more, robotics startups are moving to create highly-specialized, industry- and job-specific robots that return ROI in a short time (as low as six months, mostly fully repaid within two years).  And the merger (or, better said, integration) of AI and robotics, which we are experiencing now, will further change these equations. Imagine mixing the autonomy of thought of an agent, with the mechanized operations of a robot – no longer a single, static operation but a dynamic, “thinking” one.  

Imagine the possibilities – everyone else is, in 2026 and beyond.

 

Here are some reading resources:

 

  1. UPS deploys truck-unloading robots to optimize logistics, expanding a pilot program that showed great potential.  They demonstrated ROI by addressing labor availability, injury risk, and peak-volume volatility.  Published December 2025. ?
  2. Danfoss deployed autonomous mobile robots (AMRs) integrated with compact storage to manage growth without expanding the warehouse footprint. Published 2025?
  3. Walmart continues to roll out robots across its regional distribution centers, using fleets of high-speed mobile robots for picking, sorting, and palletizing. Because they were deployed as a supply-chain platform, the ROI extends beyond a single function to all interdependent functions. Published 2025.
  4. Starship’s sidewalk delivery robots crossed 9+ million autonomous deliveries for Uber Eats, operating at urban and campus scale.  One of the few robotics cases with multi-million transactions validating economics over novelty. Published 2025.
  5. Here’s a functional example, not tied to any one company: airports, hospitals, and others have adopted professional cleaning robots to address labor shortages and rising operating costs. Published 2025.
  6. A study reviewing numerous use cases and case studies found impressive results for organizations deploying functional robots: an average 18–24% ROI over 5 years; up to 30% reduction in labor costs; throughput gains of up to 40%; and space utilization improvements of up to 25%. Published September 2025.

What’s your take? We are fostering a community of executives who want to discuss these issues in depth. This newsletter is but a part of it. We welcome your feedback and look forward to engaging in these conversations.

 

If you are interested in exploring the full report, discussing the Board’s offering further, or have any additional questions, please contact me at [email protected], and I will be happy to connect with you.

 

(*) A normal distillation process produces byproducts: primary, simple ones called foreshots, and secondary, more complex and nuanced ones called aftershots. This newsletter highlights remnants from the distillation process, the “cutting room floor” elements, and shares insights to complement the monthly report.

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Google Gemini to power Apple Intelligence

Google Gemini to power Apple Intelligence

Apple Intelligence as well as a new Siri will be based on Google's Gemini models.

The news, initially reported in August by Bloomberg, is a big win for Google. Apple has a partnership with OpenAI and has embedded ChatGPT into Apple devices.

Here's the joint statement:

"Apple and Google have entered into a multi-year collaboration under which the next generation of Apple Foundation Models will be based on Google's Gemini models and cloud technology. These models will help power future Apple Intelligence features, including a more personalized Siri coming this year.

After careful evaluation, Apple determined that Google's Al technology provides the most capable foundation for Apple Foundation Models and is excited about the innovative new experiences it will unlock for Apple users. Apple Intelligence will continue to run on Apple devices and Private Cloud Compute, while maintaining Apple's industry-leading privacy standards."

A few takeaways:

  • Google Gemini gets the win over OpenAI, which increasingly wants to compete with Apple devices.
  • This partnership between Google and Apple was the result of a favorable antitrust ruling in September.
  • Apple's use of Google's Private Cloud Compute makes for a nice customer reference.
  • If Apple can get its AI game together--no matter what it is paying Google--without the capital expenditures others in tech have spent the company is going to be a 2026 winner.
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Google launches agentic commerce tools, Universal Commerce Protocol, Gemini Enterprise for Customer Experience

Google launches agentic commerce tools, Universal Commerce Protocol, Gemini Enterprise for Customer Experience

Google is making its play to lead agentic AI commerce across its units as it combines AI Mode commerce features and agents with a new end-to-end commerce protocol and Google Cloud's Gemini Enterprise for Customer Experience.

Google launched Universal Commerce Protocol (UCP), a standard for agentic AI commerce, checkout directly through AI mode along with direct offers and interactions to connect brands and shoppers. Google Cloud also unveiled Gemini Enterprise for Customer Experience, a suite that includes a shopping agent, customer experience agent studio, Vertex AI powered search and food ordering agent.

The announcements, timed for the National Retail Federations 2026 conference, add up to Google providing various tools to enable agentic commerce in multiple forms with a unified platform.

In a nutshell, Google is looking to offer tools to enable shopping across the customer journey. Carrie Tharp, VP of Global Solutions and Industries at Google Cloud, said AI is moving from a passive tool to one that's more active and autonomous.

"Agents can execute complex, multi-step, prescriptive actions across every consumer and operational touch point, and every retailer now has the opportunity to bring their value proposition to life in fundamentally new ways through these agentic experiences," said Tharp. "Most retailers are still in the early days of evolving discovery and the modern customer journey is very fragmented with shoppers jumping between apps, search and physical aisles because legacy systems don't talk to each other. This is simple. AI isn't retailers' competition. It should be their superpower. We believe AI must serve retailers and shoppers alike."

Although Google faces plenty of commerce competition from OpenAI, Microsoft, Amazon and a bevy of others, the company does occupy a unique position given that it touches nearly every part of the retail budget from marketing and demand to back-end functions via Google Cloud. Google CEO Sundar Pichai was a headliner at NRF 2026.

Here's a breakdown of what Google announced at NRF 2026.

Universal Commerce Protocol (UCP)

UCP is designed to be an open standards for agentic commerce that works across the entire shopping journey from discovery and buying to post purchase support.

Vidhya Srinivasan, VP and GM of Ads and Commerce at Google, said UCP "sits between agentic experiences with consumer services on one hand and the business back-end on the other." She added that UCP is built to walk across industries and is compatible with Model Context Protocol, Agent2Agent and Agentic Payments Protocol.

UCP is supported by 20 retail and commerce players including Shopify, Etsy, Wayfair, Target, Best Buy, PayPal, Visa, Stripe and American Express.

Srinivasan said UCP will power a new checkout feature in Google's AI Mode and in search, but the company will add more partners and capabilities including discovering related products and applying loyalty rewards.

Shopify's Vanessa Lee, VP of Product, said UCP is designed to address more seamless checkouts.

"Checkouts are simple from a consumer perspective and we put a lot of energy and work as retailers and platforms to make that checkout experience seamless," she said. "But one of the things that we did with UCP was we wanted to acknowledge that there's actually a lot of work that goes on behind the scenes to make that checkout as seamless as possible. One thing that we learned over the last two decades was that every single checkout is unique, and we want agentic shopping to not just be for a subset of checkouts. We wanted it to be for ubiquitous across all of shopping."

UCP sets up a series of new agentic shopping features from Google.

Business Agent, Direct Offers

Google is launching Brand Agent as a headliner of a set of agentic commerce features and tools.

The company launched Business Agent, which chats and answers questions about retailers within a search. "One way to think about it is think it's think of it as a virtual sales associate that can just answer product questions in the brand's voice during those critical shopping moments, so that the retailers can just help drive sales," said Srinivasan.

Business Agent is live with anchor retailers including Lowe's, Poshmark and Reebok. In the months ahead, retailers will be able to train agents with their own data and insights and enable direct purchases in AI Mode.

Google also announced multiple new attributes in its Merchant Center to improve discovery in AI Mode and Gemini. "These new attributes complement retailers' existing data feeds, and they go beyond the traditional keywords to include things like answers to common product questions, things like compatible accessories or even substitutes. We'll be rolling these out to a set a group of retailers soon, with plans to expand in the coming months," said Srinivasan.

The company is also launching a new ads pilot called Direct Offers, which moves beyond traditional ads and targets people shopping in AI Mode. Free shipping, bundles and special deals would be included in Direct Offers. Shopify merchants, Rugs USA, e.l.f. Cosmetics, Petco and Samsonite are piloting Direct Offers.

Checkout in AI Mode is available through Google Pay.

Google Cloud Gemini Enterprise for Customer Experience

The company said the company's launch of Google Cloud Gemini Enterprise for Customer Experience is designed to give retailers the ability to build agents for retailers that maintain brand voice at every interaction across channels.

"These agents are not just for answering questions. They can inform the customer about inventory availability, guide them through order processing, suggest products they love, and handle a return seamlessly. Every touch point becomes an opportunity to delight and drive more business," said Darshan Kantak, VP of Product, Applied AI at Google Cloud.

The shopping agents in Gemini Enterprise for Customer Experience can carry out complex reasoning, multimodal interactions and execute actions. Papa John's Kevin Vasconi, Chief Digital and Technology Officer, said 85% of the company's orders are digital and the goal is to remove friction from the purchase.

Vasconi quipped that buying pizza isn't considered to be stressful unless you're ordering for your child's travel soccer team and navigating preferences and dietary restrictions.

"We're always thinking about how we turn a transaction into a personal experience. Not everybody has a personal shopper and we think this is a beautiful application of multimodal AI. We're trying to figure out how do we take the friction out of the experience. As good as it is, there's still a lot of friction in the experience," said Vasconi.

Google Cloud Gemini Enterprise for Customer Experience follows an emerging Google Cloud playbook as Gemini Enterprise is being rolled out to multiple verticals.

"Think of it as an ecosystem of smart, interconnected agents that are orchestrated to understand reason and to take action. It enables businesses to drive that high touch premium service from initial product discovery to post purchase resolution, while maintaining continuous context across each of the touch points," said Kantak. "When a retailer uses this technology, the AI experience belongs to them. It's built for their brand in their persona."

Gemini Enterprise for Customer Experience is also multimodal to handle images, video and voice as well as text. Kantak said Kroger and Lowe's are launch customers.

Kroger's Yael Cosset, SVP and Chief Digital Officer, said the grocer has a rich data set that can leverage agentic AI to tailor offers, make recommendation and give customers time back. "Consumers want to eat more at home, but have lack of time for the complexity and how overwhelming it can be to plan and ultimately shop for their groceries," he said. "The shopping companion Google is going to allow us to develop and roll out features that will alleviate that complexity. Agentic commerce is going to be a huge unlock to accelerate that emotional connection with the customer."

The company also launched a Customer Experience Agent Studio where customers can upload transcripts, products and product information and an agent builds another one and evaluates quality. Monitoring is also built in.

Kantak added that Google Cloud is also looking to connect human agents and AI agents with two new customer service tools--AI Coach and AI Trainer. AI Coach provides real-time guidance to human reps and AI Trainer speeds up onboarding.

Other additions include:

  • Discovery Engine, which uncovers service trends with natural language queries.
  • Quality AI, which is a system that understands every conversation happening, creates insights and scorecards.
  • Food Agent, which is part of the Google Cloud Gemini Enterprise for Customer Experience suite, and can enable voice ordering across kiosks, drive throughs and car dashboards. The Food Agent will also have the ability to upsell and automate processes. Papa John's is a launch customer.
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Why enterprise AI leaders need to bank on open-source LLMs

Why enterprise AI leaders need to bank on open-source LLMs

Nvidia, which is quickly becoming the champion of AI open-source models in the US, argues that open AI models are roughly six months behind more expensive proprietary frontier models. If that's the case, CxOs should base nearly all of their AI plans around open-source models.

In Nvidia CEO Jensen Huang's CES 2026 keynote, there were a lot of talk about agentic AI systems, physical AI and robotics, but his open-source comments stuck with me. He said the following (emphasis added):

"We now know that AI is going to proliferate everywhere with open-source and open innovation across every single company and every industry around the world is activated at the same time. We have open model systems all over the world of all different kinds and they have also reached the frontier. Open-source models are solidly six months behind the frontier models, but these models are getting smarter and smarter."

Nvidia backed up its open model case with releases of new Nvidia Nemotron models (speech, RAG, Safety) for agentic AI, Cosmos models for physical AI, Alpamayo for autonomous vehicles, GR00T for robotics and Clara for biomedical.

The number of companies using Nvidia's open-source models are very familiar including ServiceNow, Cadence, CrowdStrike, Caterpillar and a bevy of others. "Not only do we open,source the models, but we also open source the data we used to train those models. Only in that way can you truly trust how those models came to be," Huang said. “That's something Meta never did with its Llama model.”

Huang said 80% of startups are building on open models, and that a quarter of OpenRouter tokens are generated by open models.

With the gap between open-source AI models and proprietary models closing why would an enterprise bet on a frontier model that will only have a lead of 6 months?

What's in it for Huang? Nvidia will obviously have the GPU and AI stack for the training and inference. Nvidia's software stack also dominates for AI. Simply put, Nvidia doesn't need a model for its business model. In other words, commodity LLMs are fine for most use cases--including yours.

Good enough and cheap enough

Huang's comments aren't that surprising given that enterprises are tweaking commodity models with their proprietary data. One of the bigger announcements out of AWS re:Invent revolved around easy customization of its Nova models. Nvidia’s software and models are being integrated into Palantir, ServiceNow and Siemens. ServiceNow used Nvidia Nemotron for its Apriel Nemotron 15B reasoning model for lower cost and latency agentic AI. Siemens expanded its Nvidia partnership that includes integration of Nemotron models.

“We built on Nvidia Nemotron for the next generation of our platform, which enables customers to do extraordinary things with large language model power at a fraction of the big model cost, zero latency, total security, no hallucination and a cost-effective ROI,” said ServiceNow CEO Bill McDermott, on the company’s third quarter earnings call.

Although software vendors are leveraging Nvidia’s Nemotron models, making most CxOs users by default, there are signs enterprises are going Nvidia and open models. Caterpillar outlined its AI plans with a dose of Nvidia Nemotron and Qwen3 models as did PepsiCo with digital twin efforts via Siemens. Hyundai said it was leveraging Nvidia Nemotron models last year.

Salesforce CEO Marc Benioff also noted that LLMs are commoditizing. He said in December: "We use all of the large language models. They're all great. We love all of them. We love all of our children, but they're also all just commodities, and we can have the choice of choosing whatever one we want, whether it's OpenAI or Gemini or Anthropic or there's other open-source ones. They're all very good at this point. So, we can swap them in and out. The lowest cost one is the best one for us, making us basically the top user of these foundation models."

Benioff’s mantra applies to enterprises too: The lower cost one is the best one.

What's in it for you?

I'd argue that there will be few if any enterprise use cases that will require a bleeding edge LLM. And if you can wait six months for an open-source option to catch up (likely from Nvidia at this point) why would you blow your cost curve on a high-end model?

You can use a series of open models to form an agentic system. The whole is greater than the parts and the parts need to be cheaper.

You'll obviously have to evaluate open-source options, commoditized LLMs and cheaper models and gauge ease of customizing with your data, but there should be a high bar to go proprietary where you just might be locked in.

It's unclear what this will mean for the likes of OpenAI, Anthropic or Google and Gemini, but that's not your problem. Your job is to drive AI returns and that'll increasingly mean open-source and commoditized models.

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Leadership in the Age of AI-Driven Cyber Threats | DisrupTV Ep. 423

Leadership in the Age of AI-Driven Cyber Threats | DisrupTV Ep. 423

Leadership in the Age of AI-Driven Cyber Threats

What Boards, CEOs, and General Counsel Must Do Now

As organizations head into 2026, leadership is being tested by a perfect storm of AI acceleration, escalating cyber threats, geopolitical uncertainty, and regulatory complexity. In DisrupTV Episode 423, hosts R "Ray" Wang and Vala Afshar sat down with Ken Banta, Andre Pienaar, and Dr. David Bray to unpack what modern leaders—and boards—must do to stay ahead in an era of converged risk.

The message was clear: cybersecurity, AI strategy, and leadership capability can no longer be treated as separate conversations. They are deeply intertwined—and failing to address them holistically puts enterprises, governments, and societies at risk.

Why Cybersecurity and AI Budgets Must Rise Together

Andre Pienaar, CEO and founder of C5 Capital, opened with a stark reality: cyberattacks are becoming more sophisticated, faster, and increasingly AI-driven. Threat actors are no longer operating with manual tools; they are deploying automation, machine learning, and increasingly autonomous systems to exploit vulnerabilities at scale.

For boards and executives, this means a fundamental shift in investment strategy.

  • You cannot increase AI adoption without simultaneously increasing cybersecurity investment.

Andre emphasized that AI expands the attack surface just as much as it enhances productivity. Organizations deploying AI without upgrading security architectures are effectively widening the door for adversaries.

Key priorities include:

  • AI-enabled threat detection and response
  • Continuous monitoring of anomalous behavior
  • Security-by-design in all AI initiatives
  • Preparing now for post-quantum cryptography

AI-Augmented Defense: Humans and Machines, Together

Dr. David Bray, Distinguished Chair at the Stimson Center and CEO of LDA Ventures, reinforced that AI alone is not the solution—but neither are humans operating without it.

Cybersecurity success depends on augmented intelligence, where:

  • AI detects patterns of life and anomalies at machine speed
  • Humans provide context, judgment, and ethical oversight
  • Systems continuously learn from both human and machine input

David highlighted a sobering trend: ransom demands are increasing sharply, and AI-enabled attacks are lowering the cost and effort for bad actors. Defenders must respond with equal sophistication.

  • The future of cybersecurity is not human vs. machine—it’s human with machine.

Quantum Computing, Geopolitics, and the New Security Landscape

The discussion also explored the geopolitical implications of AI and quantum computing. Andre and David both stressed that quantum breakthroughs will eventually render today’s encryption obsolete, making post-quantum cryptography a near-term planning requirement—not a distant concern.

At the same time, AI policy and regulation are fragmenting globally. David argued that:

  • Cities and governments must collaborate to harmonize AI governance
  • Organizations need to compartmentalize AI experimentation while maintaining oversight
  • Leaders must understand which geopolitical “technology matrix” they are operating within

AI strategy is now inseparable from national security, economic competitiveness, and global alignment.

Leadership Under Converged Uncertainty

Ken Banta brought the conversation back to leadership fundamentals—at a time when uncertainty is no longer episodic, but constant.

He emphasized that self-awareness is now a core leadership capability, not a soft skill. Leaders must understand:

  • How their words and actions are interpreted
  • When to slow down versus accelerate decisions
  • How to build trust through consistency and transparency

Ken shared a powerful reminder: people don’t just follow strategy—they follow signals. In high-risk environments, leaders set the tone for ethical behavior, risk tolerance, and psychological safety.

The Critical Role of General Counsel in AI and Cyber Risk

One of the most compelling insights centered on the evolving role of the General Counsel (GC). Ken described GCs as:

  • The conscience of the organization
  • Key advisors on AI governance and cyber risk
  • Central to decision-making under uncertainty

As AI systems influence decisions at scale, GCs are increasingly responsible for ensuring:

  • Regulatory compliance
  • Ethical use of data and algorithms
  • Alignment between risk, innovation, and corporate values

Leadership today is no longer just about vision—it’s about judgment under pressure.

From Awareness to Action: What Leaders Should Do Next

A recurring theme throughout the episode was urgency. Talking about AI and cybersecurity is no longer enough—leaders must operationalize governance, preparedness, and accountability.

One proposed next step:

  • Create an AI checklist for boards covering cybersecurity, data handling, governance, and regulatory compliance.

This kind of structured approach helps boards move from abstract risk discussions to concrete oversight.

Final Thoughts: Leadership Is the Ultimate Security Layer

DisrupTV Episode 423 made one thing abundantly clear: technology does not fail in isolation—leadership does.

In an era defined by AI-driven threats, quantum disruption, and geopolitical tension, the most resilient organizations will be led by executives who:

  • Invest proactively in AI and cybersecurity together
  • Embrace human–machine collaboration
  • Build trust through self-awareness and transparency
  • Empower General Counsel and risk leaders as strategic partners

As Ken Banta concluded, leadership itself is the ultimate control system. And in a world of converged uncertainty, how leaders think, decide, and act will determine whether organizations merely survive—or truly endure.

Related Episodes

If you found Episode 423 valuable, here are a few others that align in theme or extend similar conversations:

 

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NRF 2026: Agentic AI commerce, frontline workers, customer experiences

NRF 2026: Agentic AI commerce, frontline workers, customer experiences

Retailers are busy trying to figure out agentic AI driven commerce and keep frontline workers engaged so they can drive customer experience.

Those are the key themes from the National Retail Federation Big Show in New York City. Like previous years, the show features a parade of tech vendors pitching retailers on how to leverage the latest innovation. In 2026, that innovation is all about shopping agents, AI and employee experience.

Tech vendors, consumers and retailers are aligned on the idea that AI will drive buying journeys and experiences. According to IBM's Institute for Business Value in collaboration with the National Retail Federation (NRF), 72% of surveyed customers still shop in stores and 45% turn to AI for help.

Meanwhile, there's a renewed focus on frontline workers from the likes of Workday and UKG.

Here's a look at the news to know.

Agentic AI

Microsoft outlined its Copilot Checkout, which is designed to convert conversations into sales, and Brand Agents, which provide guidance to shoppers on a retailer's site.

The Microsoft effort is aimed at a shopping use case where a consumer is looking to compare products, make decisions and purchase within one window. Google, Microsoft, OpenAI and a bevy of others including Shopify and Paypal are looking to do something similar.

Ultimately, retailers will be embedded into the primary chat interfaces. At NRF, commerce players such as Etsy were supporting multiple efforts.

Microsoft's Brand Agents are designed to bring an in-store experience from an associate into a chat interface at this point. Brand Agents offer guidance but can also upsell and cross-sell. Brand Agents are built within Microsoft Clarity, which is an analytics tool to help merchants understand shopper behavior.

Accenture said it invested in Profitmind, which offers an agentic AI platform to help retailers automate decisions for pricing, inventory and platform. The two companies also inked a strategic pact.

Profitmind uses a network of AI agents to surface recommendations for pricing, inventory and promotions.

Manhattan Associates rolled out updates to its Manhattan Active Omni platform including three new AI agents. The agents include:

  • Store Associate Agent.
  • Contact Center Agent.
  • OMS Configuration Agent.

Manhattan also rolled out its Manhattan Active Point of Sale, which features a display where customers can view their carts in real time, enter loyalty information and get receipts.

Blue Yonder outlined updated AI agents for merchandise and assortment planning, allocation and replenishment and inventory operations.

More retail:

Frontline worker experiences

Workday announced a series of hospitality and retail customer wins including Alterra Mountain Company, Brookshire Grocery, Hungry Jack, and Zaxby's. The company also said that it integrated recently acquired Paradox and made it available through Workday's platform.

Paradox focuses on frontline worker engagement and connects candidates and employers.

Workday also outlined Workday Frontline Agent, which handles shift swaps and hour limits. The Workday Frontline Agent will be available in the Spring of 2026.

UKG demonstrated its Workforce Operating Platform and features such as UKG Rapid Hire, which compresses the time to hire, Dynamic Labor Management to address staffing gaps, UKG Frontline Worker Network and UKG Wallet to pay employees on demand.

The company also said Jetro Restaurant Depot saved $2 million in sourcing and onboarding costs with UKG Rapid Hire.

Customer experience

Technically, AI shopping agents impact customer experience, but here are a few notable in-store efforts. 

Stratavision, a computer vision company, launched its fitting room intelligence platform to help retailers optimize fitting room utilization.

The fitting room intelligence tools are tied into Consumer IQ, which analyzes customer paths and behaviors, aligns staffing to demand, increases engagement and lowers costs.

Denso is showcasing its Indoor Positioning System (IPS) to highlight how its automotive grade micro location technology can be used in retailing. Denso's IPS system is integrated with EPAM software to highlight retail media activations and personalized interactions and wayfinding.

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OpenAI doubles down on health, targets providers and patients

OpenAI doubles down on health, targets providers and patients

OpenAI is planning on being a healthcare industry AI player with the launch of OpenAI for Healthcare, a HIPAA-compliant version of ChatGPT for clinicians, just days after debuting ChatGPT Health for consumers.

The rollout of OpenAI for Healthcare makes it clear the company is betting that health is going to be a big vertical. OpenAI is looking to make ChatGPT a key tool on both sides of the healthcare equation. Anthropic has also launched Claude for Life Sciences and has embedded its models into healthcare workflows. Both model providers will compete and partner with healthcare efforts from multiple software and cloud vendors.

ChatGPT for Healthcare is rolling out with some major customers. In a blog post, OpenAI said AdventHealth, Baylor Scott & White Health, Boston Children’s Hospital, Cedars-Sinai Medical Center, HCA Healthcare, Memorial Sloan Kettering Cancer Center, Stanford Medicine Children’s Health and University of California, San Francisco (UCSF) will use ChatGPT for Healthcare.

OpenAI said healthcare providers have been tailoring OpenAI API to be HIPAA compliant. ChatGPT for Healthcare can help with serving up medical knowledge, admin work and personalize care. OpenAI provided sample prompts and use cases for ChatGPT for Healthcare.

ChatGPT for Healthcare includes:

  • GPT-5 models specifically built for healthcare and tested by physicians and benchmarked.
  • Citations for evidence retrieval to check sources.
  • Integrations with enterprise tools so healthcare providers align ChatGPT for Healthcare with policies, document repositories and best practices.
  • Templates for workflow automation for patient instructions, discharge summaries, clinical letters and authorizations.
  • Governance and access management based on roles.
  • HIPAA compliance. Content shared with ChatGPT for Healthcare isn't used to train models.

ChatGPT for Healthcare appears to have a better footing in the enterprise with big name customers already in the fold. It remains to be seen how ChatGPT Health fares with consumers.

Launched earlier this week to a small number of customers, ChatGPT Health is a dedicated experience where consumers can share medical records, data and wellness information. ChatGPT Health promises to keep conversations encrypted and isolated.

ChatGPT Health also integrates with Apple Health, Function and MyFitnessPal and will likely expand its roster of health apps in the future. OpenAI said that ChatGPT Health conversations won't flow over to regular chats. Ultimately, OpenAI sees ChatGPT Health as an advisor to prep consumers for doctor visits, improve nutrition and craft exercise programs. The service will even digest your lab results and point out what's important.

What could go wrong? Given that health is a primary use case for ChatGPT already, I didn't expect much wariness from health savvy consumers in my circle. Instead, the answers were unanimous with some form of "hell no." Biggest concern was sharing your data with OpenAI. Now this informal poll isn't scientific, but there will be some set of consumers that won't trust OpenAI's dedicated health service without some HIPAA-like promise.

Either way, ChatGPT for Healthcare may take care of patient usage. It'll just be a question of whether patients use OpenAI directly or indirectly.

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At CES 2026, humanoid robots are everywhere, but don't expect ROI to follow

At CES 2026, humanoid robots are everywhere, but don't expect ROI to follow

At CES 2025, you really couldn’t avoid the humanoid hype. Humanoid robots were everywhere.

Nvidia CEO Jensen Huang shared the stage with a bunch of robots.

Huang said AI and robotics will go together and will advance the industry. He noted that there will be more than humanoid robots. “The next era for robotic systems is going to be robots, and these robots are going to come in all kinds of different sizes,” he said.

AMD CEO Lisa Su brought on Generative Bionics CEO Daniele Pucci. Generative Bionics is a spin-off of the Italian Institute of Technology. Humanoid robots were the ultimate in keynote crutches.

Pucci argued that AI can sense the world but robotics can enable it to experience it.

Boston Dynamics also introduced the latest version of its Atlas humanoid robot. CES 2026 was a parade of humanoids with a few even offering cleaning services. 

And now for the reality check via Constellation Research’s Chief Distiller Esteban Kolsky. In his recent newsletter designed for boards of directors, Kolsky dissed the humanoid construct for robots. Kolsky is clear that AI and robotics are going to combine and deliver enterprise value. 

But the humanoid form factor makes no sense. Kolsky said: “Let’s get the ugliest part of this out of here: humanoid robots are the worst possible path we can take. Despite Hollywood’s love of anthropomorphized animatronics, there are many deficiencies in human-shaped and look-alike robot.”

For starters, the human body isn’t efficient. If humans were starting from scratch we wouldn’t have engineered this system. Why spend billions trying to replicate (poorly in most cases) a human with a robot? In addition, humans don’t adapt well to new environments. Guess what? Humanoid robots don’t either.

Here’s a video of Kolsky riffing on humanoids.

 

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Snowflake acquires Observe, expands into telemetry data observability

Snowflake acquires Observe, expands into telemetry data observability

Snowflake said it will acquire Observe to integrate observability tools into its platform.

With the move, Snowflake can extend into IT operations management software and keep their telemetry data within its AI Data Cloud.

According to Snowflake, the plan is to integrate its data and Observe's AI Site Reliability Engineer (SRE) to proactively head off production issues. Snowflake added that it will have one architecture based on Apache Iceberg and OpenTelemetry to manage the telemetry data for AI agents and adjacent applications.

Snowflake CEO Sridhar Ramaswamy said the complexity involved with AI agents and data applications means "reliability is no longer just an IT metric. It's a business imperative."

Key points include:

  • Observe Site Reliability Engineer (SRE) uses a unified context graph that will enable Snowflake to correlate logs, metrics and traces.
  • Telemetry data will be "treated as first-class data" in the Snowflake AI Data Cloud.
  • By combining Snowflake data and Observe's platform, enterprises won't have to rely on sampling and short retention windows to manage costs.
  • Terms of the deal weren't disclosed.

Here's a look at Observe's platform that will be connected to Snowflake.

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