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AI is making it easier for everyone to create software. The harder challenge is building the infrastructure, security and skills to turn that explosion of code into useful work. In this episode of The Deep View Conversations, we sit down with Mario Rodriguez, GitHub's chief product officer, at GitHub Universe 2026 to explore how AI agents are changing the platform, the community, and the future of software creation. Rodriguez explains why GitHub is rebuilding its foundations for machine-speed development, how its move to Azure supports that effort, and why writing code faster only helps if the software is secure and reliable. He also responds to Anthropic's decision to build Scion, its own version control system, and makes the case for Git's continued role as AI accelerates development. The conversation goes beyond the technology to a bigger question: who gets to build? As people without technical backgrounds start creating software, Rodriguez argues that lowering the barriers can bring more people into the craft and some will naturally become professional developers. His framework: lower the floor, raise the ceiling, and widen the walls. This episode explains what he means. Topics covered:• GitHub's vision for collaboration between people and AI agents• AI scanning, code review, and cybersecurity upgrades• Copilot projects, agent squads and automations that learn from their results• Keeping work moving across mobile, the web, Slack, and Teams• Why AI demand accelerated GitHub's move to Azure• GitHub's ambition to reach a billion developers• Why easier entry into coding can strengthen open source If you want to understand where professional software development, vibe coding, and open-source AI are heading, don't miss this one. Subscribe to Deep View Conversations for interviews with the leaders shaping the future of AI, business, and technology: https://tdv.transistor.fm/  And don't forget to sign up for The Deep View daily newsletter. We don’t just cover AI, we decode it. In a world flooded with hype, we deliver sharp, no-nonsense insights to keep you ahead of the curve and help you put AI to work every day: subscribe.thedeepview.com

Coding agents can drastically accelerate how fast developers build, but defaulting to the most powerful models gets expensive fast. But what if your coding assistant could route to less expensive models when they don't need high-end frontier intelligence? In this episode of The Deep View Conversations, we sit down with Kyle Daigle, GitHub’s chief operating officer, at GitHub Universe in San Francisco to explore how the company's new Copilot Fusion can route between models and reduce token costs. Daigle explains how Fusion can start with a less expensive model, move to a more capable one when needed, and bring in a second model to check the first model’s work. He says GitHub’s teams and early enterprise customers are seeing 50% to 60% lower costs on complex work, with savings reaching 77% for certain workloads. But most importantly, it's getting results that match using high-end models while doing it about 40% faster.  Other topics covered:• Why people outside traditional developer roles are building their own software• How GitHub’s marketing and design teams use Copilot to create tools for their own work• How Fusion’s Cascade and Critique approaches coordinate models within a task• Choosing between Fusion Standard, High, and Max based on the work you need done• GitHub’s plans to route some tasks to local models running on your computer• How Copilot Projects brings repositories, files, and agents together in one workflow If you’re building with AI coding agents, leading a development team, or managing your company’s AI budget, this conversation offers a way to control costs while keeping the capabilities  Subscribe to Deep View Conversations for interviews with the leaders shaping the future of AI, business, and technology: https://tdv.transistor.fm/  And don't forget to sign up for The Deep View daily newsletter. We don’t just cover AI, we decode it. In a world flooded with hype, we deliver sharp, no-nonsense insights to keep you ahead of the curve and help you put AI to work every day: subscribe.thedeepview.com

The AI agents helping employees work faster also open up new risks for a company's most sensitive data. So how do businesses keep up with the opportunities and the risks? In this episode of The Deep View Conversations, we sit down with Cristian Rodriguez, CrowdStrike's field CTO for the Americas. We talked to Rodriguez at CrowdStrike's Fal.Con 2026 event to explore what one of the world's largest cybersecurity companies is learning about how AI is rapidly changing the cybersecurity landscape. Rodriguez explains why defenders need to respond at machine speed, what the OpenAI and Hugging Face incident reveals about goal-seeking agents, and why even useful AI tools need visibility, boundaries, and accountability. Rodriguez argues that data governance remains a central challenge: companies need to understand what their agents can access, where information moves and who is responsible for each action. Topics covered:• Helping healthcare, government and other understaffed organizations respond to faster attacks • Whether automation and tool consolidation can strengthen security within existing budgets • SafeMind, red and blue cybersecurity models, and the role of the AI harness • Bring-your-own-model options and where CrowdStrike's models run • Moving from reactive incident response toward proactive security with human oversight • How Rodriguez uses Slackbot to organize his day and follow through on commitments  If you're leading an organization, putting agents to work, or trying to understand how AI changes the security and safety landscape, this conversation connects the new capabilities with the controls and judgment needed to manage them. Subscribe to Deep View Conversations for interviews with the leaders shaping the future of AI, business, and technology: https://tdv.transistor.fm/  And don't forget to sign up for The Deep View daily newsletter. We don’t just cover AI, we decode it. In a world flooded with hype, we deliver sharp, no-nonsense insights to keep you ahead of the curve and help you put AI to work every day: subscribe.thedeepview.com

Your next big AI win may come from someone your leadership team would never think to pick. In this episode of The Deep View Conversations, we sit down with Todd Earwood, CEO of Betra Works and an AI investor at HalfCourt Ventures, to explore what it takes to deploy AI for useful work inside organizations. Drawing on his work with 1,200 leaders and executives, Todd explains why the people closest to the work often see AI opportunities that executives miss. He shares how a university athletics sales rep used AI to uncover new ticket buyers, how a warehouse employee helped a manufacturer rethink a 22-year-old spreadsheet, and why a skeptical, experienced employee became an unexpected AI champion. We also discuss Todd's book, Adapt & Adopt, and his approach to teaching people how to identify useful AI tasks, evaluate opportunities, and build momentum from individual wins to broader adoption. Other topics covered: • Three paths to putting AI to work in a business• Why employee frustrations can reveal valuable AI opportunities• The gap between personal AI use and workplace adoption• What Todd learned from investing in more than 100 AI companies• Building a tech career outside San Francisco and New York• Why meeting recorders and voice dictation can make an immediate difference• Using Claude to protect your calendar and priorities If you're leading a team, trying to make AI useful in your own job, or deciding where to start, this conversation offers practical ways to connect AI's capabilities with the work people do every day. Subscribe to Deep View Conversations for interviews with the leaders shaping the future of AI, business, and technology: https://tdv.transistor.fm/  And don't forget to sign up for The Deep View daily newsletter. We don’t just cover AI, we decode it. In a world flooded with hype, we deliver sharp, no-nonsense insights to keep you ahead of the curve and help you put AI to work every day: subscribe.thedeepview.com TIMELINE0:00 Introduction2:27 From AI investing to real-world adoption7:19 Three paths to making AI useful10:16 Adapt & Adopt: learning how to think16:34 Unexpected AI wins in sales and manufacturing21:14 Finding AI opportunities in the work you hate23:45 Why your AI champions may surprise you27:02 Investing in AI before the boom33:49 Building a tech career beyond the coastal hubs40:44 Meeting recorders, Wispr Flow and everyday AI45:24 Using AI to protect your time48:58 Closing advice: slow down when life gets hard

AI is making it easier for employees to work beyond their specialties and that has big implications for the future organizations of all sizes.  In this episode of The Deep View Conversations, we sit down with Bill Barton, chief operating officer at Rokt and the former leader of the Alexa team at Amazon. Rokt has gotten called out by some of the biggest tech companies in the world for its innovative use of AI inside the company.  In fact, several of them have had Rokt come and talk to their teams about how they do it. So we wanted to have Bill on the show to share those insights with a larger audience. Barton shares how Rokt is using an internal AI fleet across software development, finance, legal, security and other functions. He explains how AI is helping blur the boundaries between roles, why the company has moved from more than a dozen job ladders to four, and what it takes to turn experimentation into everyday practice. Other topics covered include:• Why Bill returned to Rokt after trying retirement• How AI is expanding the reach of generalists• Enabling shared AI experimentation companywide• Why fewer, more flexible systems can help teams move faster• How Barton thinks about productivity gains and hiring• What Google search reveals about frictionless AI• Why more powerful tools can make it harder to step away from work Bill also shares his six timeless attributes that can help anyone succeed in the AI era: relentlessness, resilience, rigorousness, inquisitiveness, optimism and humility. For leaders rethinking their teams and professionals navigating changing roles, this conversation connects the possibilities of AI with the habits and judgment needed to make them useful. Subscribe to Deep View Conversations for interviews with the leaders shaping the future of AI, business, and technology: https://tdv.transistor.fm/  And don't forget to sign up for The Deep View daily newsletter. We don’t just cover AI, we decode it. In a world flooded with hype, we deliver sharp, no-nonsense insights to keep you ahead of the curve and help you put AI to work every day: subscribe.thedeepview.com

Companies want to own the intelligence that makes their business valuable. So what will that mean for the AI models they use, and the systems they build around them? In this episode of The Deep View Conversations, we sit down with Justin Boitano, VP of enterprise AI at Nvidia, to explore why NVIDIA is investing in open models and how enterprises are turning them into specialized intelligence. Boitano talks about why NVIDIA makes Nemotron's datasets, training techniques and model weights openly available, how proprietary business knowledge can become a competitive advantage, and why lower token costs can unlock much greater demand for AI. The conversation also examines why the agent harness that manages tools, memory and execution can matter as much as the model itself. Boitano shares how Nvidia worked with CrowdStrike on AI models for cybersecurity, why AI incidents need the equivalent of an aviation flight recorder, and how OpenShell moves agent controls into the infrastructure layer. Other topics covered:• Why NVIDIA is building open models alongside its chip business• What Nemotron needs to compete with China's open models• Owning institutional knowledge, protecting data and controlling AI costs• How model development helps NVIDIA improve hardware efficiency• Why harness engineering is becoming a new frontier for AI• Moving cybersecurity from finding bugs to verifying and fixing them• Transparency and learning from AI incidents• OpenShell, sandboxing and enforceable limits on agent access• How Justin uses Hermes as an AI chief of staff• Jensen Huang's approach to priorities and team alignment If you're deciding how to build enterprise AI, where your company's competitive advantage comes from, or how to give agents useful access without giving them unlimited control, this conversation offers a perspective from Nvidia's work across the ecosystem. Subscribe to Deep View Conversations for interviews with the leaders shaping the future of AI, business, and technology: https://tdv.transistor.fm/  And don't forget to sign up for The Deep View daily newsletter. We don’t just cover AI, we decode it. In a world flooded with hype, we deliver sharp, no-nonsense insights to keep you ahead of the curve and help you put AI to work every day: subscribe.thedeepview.com

AI agents can act faster than humans can review them. So how do companies keep control without slowing everything down? In this episode of The Deep View Conversations, Jason Hiner sits down with Blake Brannon, chief innovation officer and a co-founder of OneTrust, to explore why AI governance needs to evolve from static rules to decisions made in real time. Brannon explains why an agent can inherit an employee's identity and access without inheriting their trust. He makes the case for independent guardian agents that evaluate what other agents are trying to do, apply a company's policies, and redirect or block actions when necessary. One example captures the challenge: deleting CRM records could be routine cleanup or an attempt to inflate a salesperson's performance. The action alone doesn't tell you whether it should be allowed. Its purpose matters. Topics covered: • How OneTrust evolved from data privacy to AI governance• Why first-party data and customer trust go together• Why human review can't keep pace with a growing population of agents• How guardian agents and independent checks could help close the trust gap• The difference between containing AI and aligning it with business policies• Why governance should help companies move faster• What the bring-your-own-device era teaches us about AI adoption• How Blake uses visual storytelling and voice-to-text tools to work more effectively If you're deploying AI agents and trying to move beyond experiments without giving up control, this conversation offers a practical way to think about the systems that need to surround them. Subscribe to Deep View Conversations for interviews with the leaders shaping the future of AI, business, and technology: tdv.transitor.fm  And don't forget to sign up for The Deep View daily newsletter. We don’t just cover AI, we decode it. In a world flooded with hype, we deliver sharp, no-nonsense insights to keep you ahead of the curve and help you put AI to work every day: subscribe.thedeepview.com

AI is accelerating cyberattacks. Could it also give defenders the upper hand? In this episode of The Deep View Conversations, we sat down with Adam Meyers, CrowdStrike's head of counter adversary operations, at Fal.Con 2026 to explore how AI is changing the balance of power between attackers and defenders. Meyers explains why he believes security teams can now match the speed and scale of their adversaries, how CrowdStrike is training offensive and defensive AI models against each other, and why the harness that guides an AI model can matter just as much as the model itself. He shares how changing that harness helped CrowdStrike reduce false positives in its vulnerability research without changing the underlying model, and how cyberdefenders are uniting to assist one another. The conversation also goes inside the live disruption of the Sality botnet, explores how nation-state attackers are using agents that learn from their mistakes in seconds, and examines why powerful AI systems need clear boundaries as they pursue their goals. Topics covered:• Why AI could change the defender's dilemma• How CrowdStrike and its partners disrupted a botnet that had operated for more than two decades• Training red team and blue team models based on Nvidia's Nemotron models and CrowdStrike's security data• Why AI harnesses are critical to reducing hallucinations and false positives• How AI attacks are compressing the time organizations have to patch vulnerabilities• What the Hugging Face incident raises about goal-seeking agents and guardrails• Why local, open-weight models matter to both attackers and defendersIf you're trying to understand what AI means for cybersecurity, how to put agents to work safely, or where specialized models can make a difference, this conversation offers a view from someone tracking the adversaries firsthand. Subscribe to Deep View Conversations for interviews with the leaders shaping the future of AI, business, and technology: tdv.transitor.fm  Get The Deep View daily newsletter for clear analysis of AI's biggest developments and practical insights you can put to good use: subscribe.thedeepview.com

Nearly every smartphone launched in the past year features agentic AI capabilities, offering users an early look at what a fully agentic smartphone future could do for them. Of course, the tech powering it is driven by the chipsets. In this episode of The Deep View Conversations, we talked with Vinesh Sukumar, Qualcomm's VP of AI at the Snapdragon Summit, the company's annual conference where it launches its latest processors. This year, the launch included the mobile platforms Snapdragon 8 Elite Extreme Gen 6 for phones and Snapdragon Sound Elite Gen 2 for wearables. Vinesh discussed how the chipsets came to be, including the special considerations made during their design such as improving connectivity, on-device support for large models, longer battery life, and other features crucial to smoothly running agentic AI applications. We also discussed what the future of a truly agentic AI phone looks like and what's been holding it back. Topics covered: • What an ideal agentic smartphone experience would look like  • The demands agentic AI models make of mobile chipsets • The obstacles to agentic solutions becoming a game changer  • The crawl, run, walk phases of agentic solutions, and where we are now • The role of other smart devices in creating agentic experiences  • How support for a 30 billion MoE on-device model was made possible  • Qualcomm's role in working with partners to bring AI experiences to life If you want to learn more about how the latest chipsets will change the future of Android flagship devices in the next year, including new AI experiences, this conversation will give you a clear idea. 📺 Watch on YouTube: https://youtu.be/YXvw9Gw41Xg  🎧 Listen in your favorite podcast player:  Subscribe to Deep View Conversations for interviews with the leaders shaping the future of AI, business, and technology. And don't forget to sign up for The Deep View daily newsletter. We don’t just cover AI, we decode it. In a world flooded with hype, we deliver sharp, no-nonsense insights to keep you ahead of the curve and help you put AI to work every day: subscribe.thedeepview.com Disclaimer: Sabrina Ortiz's travel to Snapdragon Summit was paid for by Qualcomm. The Deep View's coverage is editorially independent from the companies we cover.

Can AI move fast and still be safe? As the debate over AI safety and innovation grows more polarized, we explore the practical work of making powerful systems accountable. In this episode of The Deep View Conversations, we sit down with Navina Singh, CEO of Credo AI, to talk about what AI governance means for frontier labs, enterprises, and policymakers. They explore why governance is broader than regulation, how organizations can test and verify their AI systems, and what happens when capabilities advance faster than oversight. Topics covered: • Why Navrina sees AI safety and innovation as goals that can advance together• What the Hugging Face agent incident raises about frontier AI oversight• The “spectrum of trust,” from internal testing to independent audits and regulation• The risk of regulatory capture and the role of open models• How businesses weigh AI capability, cost, and control while managing "governance debt"• How Credo AI uses forward-deployed governance experts to help companies put oversight into practice• Why expertise, taste, and judgment remain valuable as AI takes on more work If you’re building, buying, or governing AI, this conversation offers a practical way to think about trust without losing sight of the technology’s promise. Subscribe to Deep View Conversations for interviews with the leaders shaping the future of AI, business, and technology: tdv.transitor.fm  And don't forget to sign up for The Deep View daily newsletter. We don’t just cover AI, we decode it. In a world flooded with hype, we deliver sharp, no-nonsense insights to keep you ahead of the curve and help you put AI to work every day: subscribe.thedeepview.com

What does it take for companies to use agentic AI to transform the enterprise, without losing control of the technology? In this episode of The Deep View Conversations, we sit down with Shibani Ahuja, SVP of data and AI strategy at Salesforce, to discuss how one of the world's leading software companies is applying AI with practical use cases, matching governance to risk, and building toward larger transformations ahead.  Salesforce has surprisingly embraced a "headless" AI strategy that lets customers use any AI to access their Salesforce data safely and securely. That includes its own Slackbot, which sits inside one of the world's most widely used business messaging systems. In this interview, we learn more about why Salesforce wants to give customers optionality. Shibani also lays out Salesforce’s four modes of enterprise AI, from everyday assistive tools to agents that can reshape end-to-end operations. We also discuss Koa, Salesforce’s new CRM reasoning model, why the model-plus-harness approach is so critical, and why adaptability may be the defining enterprise skill of the AI era. Topics covered: • Why organizations should start with practical, level-one AI use cases• How Salesforce matches governance and ROI expectations to the risk of an AI deployment• What Koa, Salesforce's AI model built on NVIDIA Nemotron, changes for enterprise AI• Why operating models, process expertise, and professional services matter as much as the latest technology• Shibani's case for AQ: the adaptability quotient for technology stacks and teams If you’re trying to make AI more efficient, safer, and more ROI-driven, this conversation offers a practical framework for how to build it, how to govern it, and where to start. Subscribe to Deep View Conversations for interviews with the leaders shaping the future of AI, business, and technology: tdv.transitor.fm  And don't forget to sign up for The Deep View daily newsletter. We don’t just cover AI, we decode it. In a world flooded with hype, we deliver sharp, no-nonsense insights to keep you ahead of the curve and help you put AI to work every day: subscribe.thedeepview.com

Apple's first foldable iPhone makes the case that a bigger screen is better for nearly everything in the AI era. Its new Apple Watch features raise a harder question: how much of our conversations should AI remember? In this special episode of The Deep View Conversations, we record from Apple's campus in California following its September 2026 event to unpack the iPhone Duo, the new Audio Intelligence features in Apple Watch, and what Apple's latest devices mean for AI. We share our first hands-on impressions of the Duo, explain why foldables are becoming more useful for AI agents and multitasking, and examine the price and hardware compromises that come with Apple's new form factor. We also debate Live Rewind and Siri Recap, two new Apple Watch features coming in beta later this year. We disagree on which feature we would feel more comfortable using, opening up a broader discussion about privacy, trust, and staying present. The conversation also covers: • How the iPhone Duo compares with foldables from Google and Samsung • Why Apple's software experience is the Duo's biggest advantage • Camera, battery, durability, and Touch ID tradeoffs • Whether foldables will eventually become the default iPhone • Siri AI, iOS 27, and Apple's approach to other smart features without AI washing • The social questions surrounding AI-generated conversation summaries • The iPhone 18 Pro's camera upgrades and Apple's computational photography • Apple silicon, the A20 Pro, and the possibilities of running AI models locally on a phone If you're following the future of AI in phones and wearables, this conversation connects Apple's announcements to the ways people will actually use them, along with the questions that still need answers. Subscribe to Deep View Conversations for interviews with the leaders shaping the future of AI, business, and technology: tdv.transitor.fm  And don't forget to sign up for The Deep View daily newsletter. We don’t just cover AI, we decode it. In a world flooded with hype, we deliver sharp, no-nonsense insights to keep you ahead of the curve and help you put AI to work every day: subscribe.thedeepview.com

AI is transitioning from just answering questions to doing valuable work. The next challenge is making agents more accessible and simple enough that the technical details fade into the background. In this episode of The Deep View Conversations, we sit down with two members of OpenAI's ChatGPT Work team, Tara Seshan and Ty Geri, to dig into ChatGPT Work and what OpenAI is doing to make advanced agent capabilities useful to a lot more people. We also dig into some of the current challenges and how the team is approaching them.  Seshan and Geri explain how scheduled tasks and proactive assistance are changing the way people start their workdays, why AI lets teams move from debating ideas to testing prototypes, and how personalized software can turn one-off needs into purpose-built tools. They also discuss the challenge of token costs and model selection, why "super app" isn't the most useful framing for ChatGPT and Codex, and what it will take for agents to become more persistent, proactive, and connected. The conversation also covers:• How OpenAI is trying to bridge local and cloud workflows• Why Tara and Ty start their days with agents instead of Slack• Building personal apps and tools without traditional software overhead• The tradeoff between model capability, cost, and user control• More persistent agents and proactive personal assistance• Connecting agents to email, calendars, enterprise systems and third-party tools• Privacy, security and administrative controls for agentic work If you’re figuring out where agents fit into your work or what has to improve before you trust them with more of it, then this conversation offers a practical look at how OpenAI is preparing for that transition. Subscribe to Deep View Conversations for interviews with the leaders shaping the future of AI, business, and technology: tdv.transitor.fm  And don't forget to sign up for The Deep View daily newsletter. We don’t just cover AI, we decode it. In a world flooded with hype, we deliver sharp, no-nonsense insights to keep you ahead of the curve and help you put AI to work every day: subscribe.thedeepview.com

AI makes software easier to create, but the harder and more valuable challenge is controlling what gets built, proving that it works, and managing it over time. In this episode of The Deep View Conversations, we sit down with Florian Douetteau, CEO and co-founder of Dataiku, to explore how large organizations can turn AI agents from impressive demos into safe, maintainable systems that deliver measurable business results. Douetteau explains why enterprise AI models are becoming commoditized, why companies may buy 90% of their agents but build the 10% that differentiates their business, and why the emerging discipline of "agent management" will be essential. He also breaks down the dilemma facing CEOs: move too slowly and competitors may gain a structural cost advantage; move too quickly without control and one major AI failure could create a crisis. Topics covered: • Why the cost of creating with AI is falling toward zero• Where value will accrue as models commoditize• How to balance openness, innovation and enterprise control• Why subject-matter experts must retain ownership of AI agents• Why business problems, not perfect data, should drive data strategy• How enterprises can prioritize transformative AI use cases without stifling experimentation• The three qualities Dataiku now values most when hiring• How leaders can use AI without falling into cognitive laziness If you’re trying to move enterprise AI beyond pilots, govern a growing portfolio of agents or understand where durable value will emerge as AI creation becomes cheaper, this conversation offers a practical framework for building quickly without losing control. Subscribe to Deep View Conversations for interviews with the leaders shaping the future of AI, business, and technology: tdv.transitor.fm  And don't forget to sign up for The Deep View daily newsletter. We don’t just cover AI, we decode it. In a world flooded with hype, we deliver sharp, no-nonsense insights to keep you ahead of the curve and help you put AI to work every day: subscribe.thedeepview.com

AI's appetite for compute keeps growing, but so does the pressure to deliver more intelligence per watt and per dollar. Can AMD's first rack-scale AI system open up an ecosystem dominated by Nvidia? In this episode of The Deep View Conversations, we sit down with Andrew Dieckmann, AMD's general manager of its data center GPU business, to unpack the company's Helios platform and the rapidly changing economics of AI infrastructure.  Dieckmann explains why frontier AI requires more than just GPUs. It demands tightly engineered racks that combine GPUs, CPUs, networking, software, cooling and serviceability. The conversation examines the tension around AI data centers: hyperscalers still cannot get enough compute, while communities worry about power, water and whether the benefits justify the buildout. Andrew argues that responsible deployment and open ecosystems are essential as these systems become intelligence factories. The conversation then turns to Helios: AMD's performance claims against Nvidia Vera Rubin, pricing and value, the first likely customers, and the Cerebras partnership for high-throughput, low-latency inference. Andrew closes with his advice for leaders navigating AI velocity: reassess priorities more often and use coding agents as force multipliers for scarce engineering talent. Topics covered: • Why AMD is moving from chips to full rack-scale systems• AI demand, data center constraints, and community impact• Open hardware, open software and customer choice• How agentic AI changed infrastructure planning• Helios performance, efficiency, pricing and customers• AMD Helios versus Nvidia Vera Rubin• How AMD and Cerebras split inference workloads This conversation offers a clear look at the technology and economics shaping the infrastructure that will power everyday AI and the breakthroughs to come. Subscribe to Deep View Conversations for interviews with the leaders shaping the future of AI, business, and technology: tdv.transitor.fm  And don't forget to sign up for The Deep View daily newsletter. We don’t just cover AI, we decode it. In a world flooded with hype, we deliver sharp, no-nonsense insights to keep you ahead of the curve and help you put AI to work every day: subscribe.thedeepview.com