CXOTalk

Michael Krigsman

C-Suite Conversations on AI and Strategy. Join industry analyst Michael Krigsman for unfiltered discussions with the leaders shaping the future of business. From AI implementation to digital transformation, hear directly from CIOs, CTOs, CEOs, and more from the world's largest companies. No scripts. No PR fluff. Just real questions from our live audience and honest answers from the C-Suite. Want to participate? Get invited to the next live show: https://www.cxotalk.com/subscribe

  1. 1 hr ago

    Can Open Weight AI Compete with Frontier? Mozilla CTO Raffi Krikorian explains.

    Companies running AI must decide which work to send to the frontier labs and which to run on open-weight models. Raffi Krikorian, Mozilla's Chief Technology Officer, explains open-weight AI for the enterprise: what open-weight and open-source models are, which work belongs on frontier models, and when running your own models pays off. The conversation covers cost, security, backup plans, token pricing, and lock-in. YOU'LL DISCOVER ✅ Why Raffi compares an open-weight model to a compiled binary: you can download and run it, but you cannot see what went into it, unlike a fully open-source model. ✅ Where to draw the line: frontier models for scientific research, open-weight models for everyday tasks at about 35 to 50 times lower cost. ✅ How to find your own line: record and log every prompt and response, so those traces show which work a cheaper model can handle. ✅ Why openness leads to more security, not less, with Signal as the example. ✅ Why Raffi expects the moat to shift from building models to using them: fine-tuning, deployment, and the tooling around the model. ✅ How to run open models today, from NeoCloud API providers to Ollama, vLLM and llamafile on a MacBook, Mac Studio or DGX Spark, and why RAM is king. ✅ Why Raffi tells CTOs not to switch providers today, but to start evaluating a backup plan for outages, cost spikes, and lowered token limits. ✅ Why today's token prices are subsidized ahead of the labs' IPOs: a $200 subscription that one Mozilla engineer measured at about $6,000 a month in tokens. ⏱️ TIMESTAMPS 0:00 Introduction 0:14 Open weight versus open source models 5:11 Rent the frontier, own everyday AI 12:21 Openness leads to more security, not less 14:45 The moat shifts from model to usage 18:28 Open models are getting easier to run 27:42 Start evaluating a model backup plan today 32:51 When enterprises should move to open models 38:51 Subsidized token prices will not last 43:13 IP rights and inspection at scale 47:00 Open-weight competition, China, and safety 51:08 Token price hikes and context lock-in CXOTalk streams live with audience questions. Join us live and ask the guests your own questions: https://www.cxotalk.com/subscribe Show notes, transcript, and summary: https://www.cxotalk.com/episode/mozilla-cto-open-source-and-open-weight-ai-rent-or-own Episode 933 | Recorded October 9, 2026 #CXOTalk #OpenWeightAI #OpenSourceAI #EnterpriseAI #AIStrategy #Mozilla #OpenModels #AICosts #CTO #AIInfrastructure

    Can Open Weight AI Compete with Frontier? Mozilla CTO Raffi Krikorian explains.

  2. 1 hr ago

    AI Agents: Why You Need to Be at the Helm | Salesforce's Chief Ethical and Humane Use Officer

    AI agents now act on more decisions than any manager can review, so the choice of which work goes to an agent and where a person stays in charge decides the result. Paula Goldman, Salesforce's Chief Ethical and Humane Use Officer and EVP of Product, explains why the human in the loop model fails for agents and what replaces it: human at the helm. Drawing on her book, Manage the Machine, she walks through choosing use cases, setting an agent's boundaries, audit trails, token spend, and the accountability structure that CEOs must put in place. YOU'LL DISCOVER ✅ The test that decides a use case: not whether AI can do the work but whether it should, with the healthcare scheduling and 1-800Accountant tax examples ✅ Why the human in the loop idea from Cold War missile defense breaks when one agent run leaves 50,000 data points no person can verify one by one, and what human at the helm means instead ✅ Why a public tracker that now counts well past 1,000 court cases of AI-hallucinated citations points to overtrust, and the junior employee method for expanding an agent's scope ✅ The first step in setting up an agent: its goal, off-limits topics, escalation triggers, and domain tripwires such as brand voice ✅ How deterministic controls and probabilistic models work together, and why audit trails give you receipts for what an agent did ✅ The IKEA couch story and the human goes first method that stops AI from reverting to the mean ✅ How to manage token spend by allocating it per use case and matching the model to the job ✅ Advice for CEOs: a real accountability structure for AI and employees at the table ⏱️ TIMESTAMPS 0:00 Introduction 0:27 Not just can AI, but should it 6:57 Human at the helm, not the loop 11:26 AI has data, humans have context 16:24 AI finds insights, people catch mistakes 19:45 Demand AI you can monitor 24:16 Set the agent's boundaries in advance 28:58 Audit trails give you the receipts 34:58 Human goes first, then the AI 40:35 AI is simpler than it seems 45:12 Society sets the floor, companies experiment 48:12 AI needs a clear accountability structure Join us live and ask the guests your own questions: https://www.cxotalk.com/subscribe Show notes, transcript, and summary: https://www.cxotalk.com/episode/salesforces-chief-ethical-and-humane-use-officer-how-to-build-great-ai-use-cases Episode 931 | Recorded September 11, 2026 #CXOTalk #AIAgents #AgenticAI #EnterpriseAI #Salesforce #AIEthics #AIGovernance #ResponsibleAI #CIO

    AI Agents: Why You Need to Be at the Helm | Salesforce's Chief Ethical and Humane Use Officer

  3. 2 hr ago

    Why Most AI Pilots Fail (And How to Fix It)

    Patrick Bangert, Chief AI Officer at Occidental Petroleum, advises enterprise leaders to avoid running AI pilots altogether. He explains that most enterprise AI initiatives fail during the transition from a successful pilot to a full-scale deployment. Instead, he recommends securing funding and management support from the outset and viewing adoption as the key to converting projected returns into actual benefits. His team manages hundreds of AI systems across Oxy's physical operations, including CO2 injection in the Permian and the deployment of cameras and edge computers in the field. The discussion covers how these systems are trained, secured, funded, adopted, and evaluated. YOU'LL DISCOVER ✅ Why POC should stand for proof of competency, not proof of concept, and why Patrick can validate a concept in ten minutes by searching for it ✅ How free or discounted pilots leave client management hearing about a project for the first time at the results meeting, and what that does to the budget cycle ✅ The change management method that gets field operators to use a tool: stakeholders involved before the project starts, two-week sprints, co-creation and training ✅ Why revenue impact comes first, cost savings second, and adoption is the metric that lets either one happen ✅ How machine learning and optimization set CO2 injection across dozens of wells 24/7 while LLMs only handle the communication layer ✅ Why cleansing the data is the secret sauce of training, with physics rules and hard engineering limits as guardrails ✅ Why Patrick errs on the side of buying infrastructure software rather than building it, and why Oxy does much of its LLM work on open weight models with no token cost ✅ Patrick's rule that if you cannot measure it, it is zero, and the two pieces of hype leaders should stop believing ⏱️ TIMESTAMPS 0:00 Introduction 0:10 LLMs communicate; they do not decide 2:53 Field conditions push computing to the edge 5:05 Cleaning the data is the secret sauce 11:09 Buy rather than build; run open weights 17:58 Proprietary models win; vendors need scientific proof 21:25 AI results still need human validation 26:45 Scaling AI is a hardware problem 30:14 Adoption is how the revenue KPI happens 34:02 Skip the pilot, commit on day one 39:41 If you cannot measure it, it's zero 42:07 AI fails for human reasons, not technology Join us live and ask the guests your own questions: https://www.cxotalk.com/subscribe Subscribe for weekly conversations with leading business and technology leaders. Get the CXOTalk newsletter: https://newsletter.cxotalk.com Show notes, transcript, and summary: https://www.cxotalk.com/episode/occidental-petroleums-chief-ai-officer-ai-at-scale-in-the-physical-world Episode 932 | Recorded September 18, 2026 #CXOTalk #EnterpriseAI #AIAdoption #PilotPurgatory #IndustrialAI #OilAndGas #ChiefAIOfficer #AIStrategy #CIO

    Why Most AI Pilots Fail (And How to Fix It)

  4. 16 Sept

    Atlassian Chief AI Officer: Building Products for Agents and Humans

    Check out Gartner Symposium: https://cxo.news/KGg8XY === Tamar Yehoshua is Chief Product and AI Officer at Atlassian, where AI agents are now direct users of the software her team builds. She led product at Google Search and Slack before joining Atlassian, and in this conversation she explains how the company designs products for both people and agents: the teamwork graph that gives an agent the organizational context it arrives without, who stays accountable when a chain of agents goes wrong, how Atlassian measures AI spending with its CFO, and why she never believed agents would kill SaaS. YOU'LL DISCOVER ✅ Why an AI agent arrives like someone joining your organization with no background whatsoever, and how the teamwork graph (over 200 billion entities) supplies the missing context ✅ The result of Atlassian's A/B tests: 44% higher quality and 48% lower token usage when coding agents use the teamwork graph, versus MCP alone ✅ How Mercedes-Benz built a Rovo agent that cut manual bug triage work for engineers by 85% ✅ The 10x question Atlassian asks before automating a process, instead of settling for a 10 to 20% incremental gain ✅ How Atlassian measures AI value with the CFO: PRs deployed per engineer, features developed, and whether a growing token budget delivers more value ✅ Why vibe-coded replacements for enterprise SaaS "just don't last" at large companies: support, security, and enterprise readiness ✅ The accountability principle for agent chains: the human who builds the first agent stays responsible, with agents increasingly acting as guardrails for other agents ✅ Why Atlassian's AI gateway routes each job to the right model on quality, latency, and cost instead of locking into one model ⏱️ TIMESTAMPS 0:00 Introduction 0:26 Building products for humans and agents 5:50 The teamwork graph and agent context 13:33 Human oversight and agent guardrails 18:26 Moving fast versus getting it right 21:56 Efficiency first, then new capabilities 26:25 Tokens, vibe coding, and SaaS survival 33:00 Agent accountability and the software lifecycle 38:53 Simpler interfaces and visualizing agent work 41:49 How AI reshapes leadership and hierarchy 48:07 Reimagining processes and the Forge ecosystem 50:44 Advice for CIOs and managing AI costs CXOTalk streams live with audience questions. Join us live and ask the guests your own questions: https://www.cxotalk.com/subscribe Subscribe to CXOTalk for weekly conversations with leading business and technology leaders. Get the CXOTalk newsletter: https://newsletter.cxotalk.com Show notes, transcript, and summary: https://www.cxotalk.com/episode/atlassian-chief-ai-officer-building-products-for-agents-and-humans Episode 930 | Recorded August 28, 2026 #CXOTalk #AIAgents #Atlassian #EnterpriseAI #ArtificialIntelligence #AgenticAI #ChiefAIOfficer #ProductManagement #DigitalTransformation #SaaS

    Atlassian Chief AI Officer: Building Products for Agents and Humans

  5. 1 Sept

    How Snap Built a Production System Where Agents Write 90% of Code

    Check out Gartner Symposium: https://cxo.news/KGg8XY ======== More than 90% of the code at Snap is now written by AI agents, and a code review agent checks most of it within ten minutes. Saral Jain, SVP, Head of Engineering and CIO at Snap Inc., built the system that makes that safe for nearly a billion monthly users. He explains the golden path of 14 managed agents, the guardrails that account for more than half of the investment, and why accountability still rests with a named human for every change that ships. YOU'LL DISCOVER ✅ Why Snap funds 14 blessed agents on a golden path instead of letting every team build its own ✅ How Casper turns a Slack conversation into a sandboxed pull request that CodePal reviews before a human sees it ✅ Why more than half of Snap's agent investment goes into evals, code review and rollback rather than into building ✅ The risk-tiering test that separates an internal dashboard from a privacy-sensitive change ✅ Why Saral says the risk is not AI-generated code, it is unowned code ✅ How Snap measures value on outputs, with commits up 75% year over year and severe outages down 57% ✅ Why context beats model 100 out of 100 times, and what context actually means inside a 15-year-old codebase ✅ Four pieces of advice for CIOs, ending with why adoption has to start at the top ⏱️ TIMESTAMPS 0:00 Agents write the code, engineers own it 7:17 Agents as teammates with defined personas 12:52 Systems for agents, features for humans 16:09 How Snap governs thousands of agents 22:39 Invest in guardrails before building agents 29:46 Output metrics and the coordination tax 33:08 Context you can give, judgment you cannot 38:14 The risk is unowned code 44:58 Working with unpredictable AI systems 47:13 Agents beyond engineering, and human skills 50:24 Token spend and model routing 52:35 Advice for leaders and the risks ahead Subscribe for weekly conversations with leading business and technology leaders. Get the CXOTalk newsletter: https://newsletter.cxotalk.com Show notes, transcript, and summary: https://www.cxotalk.com/episode/snap-agentic-ai-software-development-at-scale Episode 929 | Recorded August 21, 2026 #CXOTalk #AgenticAI #SoftwareDevelopment #EngineeringLeadership #AICodeReview #Snap #CIO #AIGovernance #DeveloperProductivity #snapchat

    How Snap Built a Production System Where Agents Write 90% of Code

  6. 10 Aug

    Enterprise AI Biggest Opportunities: A Top VC's Take

    Enterprise AI has moved past experimentation and now has to prove its return. Ed Sim, Founder and General Partner of boldstart ventures, ranked the No. 1 seed investor in the Business Insider Seed 100 two years running, sees hundreds of AI startup pitches a year, and writes the first check into companies enterprises buy from years later. He wrote the first check into Snyk and backed Protect AI, which Palo Alto Networks acquired for more than $700 million. In this conversation, he lays out the three waves of enterprise AI adoption, why rising token costs are pushing companies toward open-weight models and their own hardware, how agent identity and access create a new attack surface, and what separates AI vendors that survive a shakeout from the ones that do not. YOU'LL DISCOVER ✅ The three waves of enterprise AI: get AI running, get agents running, and the wave happening now, where ROI and tokenomics decide what survives ✅ Why Ed expects dozens of models inside a single enterprise, and the choice he frames as renting intelligence versus owning it ✅ How one portfolio company packaged eight GPUs, CPUs, and a model router into an appliance, routing roughly 10% of queries to the frontier labs and claiming 70% savings per year ✅ Why agents should be granted access at runtime that expires when the task ends, so a breach's blast radius stays contained to one narrow authorization ✅ Cost per outcome as the yardstick: the human doing the task, the AI doing the task, and the human assisted by AI, applied first to discrete work like coding and customer support ✅ A 57-step insurance claims process where the AI was correct 98% of the time and the humans 85%, a gap only visible because every step was recorded ✅ The real difference between open source and open weight models, and why most of Ed's startups now build on open weight models under the hood ✅ Why he argues offense is the new defense, and what the Black Hat sandbox escape means for CISOs planning autonomous defense ⏱️ TIMESTAMPS 0:00 Introduction 0:36 Three waves and the ROI test 3:06 Many models and where startups win 10:32 Who owns access, context, and evaluations 17:21 It's the people, not the architecture 20:04 Measure the outcome, then cut the cost 28:14 Buying talent and changing culture 33:05 Systems of record versus headless agents 36:31 Venture money pivots to robotics and chips 40:11 Open weights and owning your intelligence 44:44 Autonomous attacks need autonomous defense 51:32 Judging vendors and earning enterprise trust 👉 Subscribe for weekly conversations with leading business and technology leaders. 📩 Get the CXOTalk newsletter: https://newsletter.cxotalk.com 📝 Show notes, transcript, and summary: https://www.cxotalk.com/episode/top-vc-perspective-where-enterprise-ai-is-headed Episode 928 | Recorded August 7, 2026 #CXOTalk #EnterpriseAI #AIAgents #AgenticAI #VentureCapital #AISecurity #OpenWeightModels #AIROI #CIO #CISO

    Enterprise AI Biggest Opportunities: A Top VC's Take

  7. 6 Aug

    Why Your Enterprise AI Pilot Won't Scale (with Nate B. Jones)

    Most enterprise AI pilots stall or fail before production, and the blocker is rarely the model. Nate B. Jones, an AI analyst and advisor who works with Fortune 500 companies and global banks, tells Michael Krigsman that naming an effort a pilot invites small budgets and safe goals. He explains how to pick a first project that matters to the business, why adoption is roughly 80 percent a people problem, and how to budget AI by cost per completed task rather than cost per token. Recorded live on CXOTalk with questions from the audience throughout. YOU'LL DISCOVER ✅ Why calling the work a pilot produces smaller budgets, safer goals, and weaker learning ✅ The two starting points Jones gives leaders: get hands-on with the tools yourself, then pick a project where success creates real business leverage ✅ Why adoption follows a bell curve, and what actually moves the middle of the distribution ✅ Why data flow, not the model, is the technical issue that stops initiatives most often ✅ The harness (context, memory, reusable procedures, review gates) treated as company intellectual property ✅ How to compare open weights against frontier models on cost per completed action, including token efficiency between models ✅ Why cost per task keeps falling even as frontier work stays expensive, and how to budget against that ✅ The case for one named owner per agent, and what Jones tells CIOs about shadow AI and cyber defense ⏱️ TIMESTAMPS 0:00 Introduction 0:24 Why pilots fail and where to start 3:30 Adoption is mostly a people problem 8:51 Data, outcomes, and undocumented knowledge 14:19 Learning from pilots and proving value 17:57 The harness and AI fluency 23:59 Why a culture of experimentation wins 28:03 Open weights, costs, and team fluency 33:25 When new model releases matter 38:15 Job fear, AI costs, and accountability 46:41 Agent owners, evals, and production gates 51:03 Advice for CIOs and when to stop Subscribe for weekly conversations with the business and technology leaders shaping enterprise AI strategy: https://newsletter.cxotalk.com Show notes, transcript, and summary: https://www.cxotalk.com/episode/why-ai-pilots-stall-how-to-make-enterprise-ai-work Episode 927 | Recorded Friday, July 31, 2026 #CXOTalk #EnterpriseAI #AIStrategy #AIAdoption #DigitalTransformation #CIO #AIAgents #Tokenomics #AIGovernance

    Why Your Enterprise AI Pilot Won't Scale (with Nate B. Jones)

  8. 21 Jul

    AI Agents in Banking: UBS Former Chief Information Officer

    Are AI agents ready for banking and financial services? Former UBS Group CIO Oliver Bussmann explains the risks to trust and reputation in banking. This session examines the current adoption landscape of AI agents within the financial sector. With roughly 50% of financial institutions now integrating these tools into their workflows, understanding the operational implications is critical for industry leaders and tech professionals alike. Bussmann breaks down why financial AI requires a balanced approach. You will learn how firms are navigating the tension between rapid innovation and the need to maintain client trust as they deploy AI agents at scale. Whether you are managing banking technology or assessing the impact of financial AI, this overview provides context on the real-world challenges facing major institutions today. The discussion highlights the specific reputational hazards that arise when automating sensitive financial processes. YOU'LL DISCOVER ✅ How copilot use is shifting toward autopilot across back office, IT, and marketing functions ✅ What has to be in place before an agent gets write access to a core ledger: testing, traceability, audit logs, and rollback ✅ Why Bussmann expects audit agents to move into the second and third lines of defense, with PwC and Deloitte already bringing their own ✅ How the risk classification of a use case drives the level of cross-model validation, human verification, and cross-checks ✅ Trust is the asset a bank cannot lose, and Bussmann is waiting for an industry incident driven by hallucination ✅ Why junior software engineer job advertisements are down about 40%, and why you cannot stop hiring juniors you will need as seniors in three to five years ✅ Coding is not the bottleneck; the organizational change required for process redesign is the real constraint ✅ Bussmann is optimistic that agents will run across bank functions within a year, with gains of one, two, or three times in certain use cases against the copilot era's 10 to 30% ⏱️ TIMESTAMPS 0:00 Introduction 0:22 The technology works, the controls lag 6:22 Guardrails first, then measure the gains 9:38 How regulation shapes what agents may do 17:15 Machine learning and high-risk decisions 20:14 Trust is what a bank cannot lose 25:19 Agents are reshaping technology careers 34:26 Risk classification sets the autonomy line 39:29 Customer agents need verified digital identity 45:12 Proving control to regulators and boards 48:23 AI native banks still need people 51:41 Agents in production within a year Subscribe for weekly conversations with leading business and technology leaders. Get the CXOTalk newsletter: https://newsletter.cxotalk.com Show notes, transcript, and summary: https://www.cxotalk.com/episode/agentic-ai-in-financial-services-former-ubs-and-sap-group-cio Episode 925 | Recorded July 17, 2026 #CXOTalk #AgenticAI #AIinBanking #FinancialServices #AIGovernance #EnterpriseAI #AIAgents #RiskManagement

    AI Agents in Banking: UBS Former Chief Information Officer
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About

C-Suite Conversations on AI and Strategy. Join industry analyst Michael Krigsman for unfiltered discussions with the leaders shaping the future of business. From AI implementation to digital transformation, hear directly from CIOs, CTOs, CEOs, and more from the world's largest companies. No scripts. No PR fluff. Just real questions from our live audience and honest answers from the C-Suite. Want to participate? Get invited to the next live show: https://www.cxotalk.com/subscribe

Information

  • Creator
    Michael Krigsman
  • Years Active
    2015 - 2026
  • Episodes
    561
  • Rating
    Clean
  • Copyright
    © Michael Krigsman
  • Show Website
    CXOTalk

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