The conversations enterprise IT leaders actually listen to.
Volume 343, Issue 2 · 12 articles from 2 issues
A Tech Field Day panel weighs AI's hidden costs: technical debt, maintainability, convergent answers and accountability.
Recorded before AI Field Day 9, this Tech Field Day podcast brings together Ron Pagani Jr., John Willis, Dave Nicholson and Stephen Foskett to look at the full cost of AI beyond the financial bill.
They highlight maintainability and the technical debt AI can create through unsustainable systems, costs that are hard to see or plan for.
The panel also covers implementation complexity even for experts, the risk that AI systems converge on the same answers for many users, and questions of accountability and IP ownership.
They stress that people remain responsible for what an LLM produces.
Phison CTO Sebastien Jean discusses defending against attackers who use the same AI tools that help close security skills gaps.
InformationWeek senior editor Joao-Pierre S. Ruth talks with Sebastien Jean, CTO of Phison Electronics, about the double-edged nature of AI in cybersecurity.
Companies are turning to AI to cover skills gaps their security teams can't fill fast enough, but hackers are already using the same tools to breach defenses.
The conversation covers why AI alone may not be enough to outmaneuver adversaries and how hiring and training fit in.
Jean also explains how he approaches the challenge at a hardware company.
Heather Ceylan explains how exposure management starts with key risk indicators instead of control effectiveness.
In the final episode of SC Media's CISO Stories podcast, Heather Ceylan discusses how exposure management is changing the way organizations prioritize and report cyber risk.
Because not every vulnerability or control gap can be treated as urgent, she recommends starting with key risk indicators: what is actually exposed, which threats are real and imminent, and what would have the biggest business impact.
Teams then map those risks back to the controls meant to reduce them.
This shifts the question from whether controls work to whether the risks that matter most are going down.
theCUBE analysts discuss IBM's bet on commodity models and Red Hat, and the industry's shift toward inference.
On theCUBE Pod, John Furrier and Dave Vellante call the current wave of public AI skepticism a temporary "AI freakout moment" and review takeaways from IBM's Analyst Council.
Vellante argues that competitive advantage will come from an organization's own data, processes and AI operating model rather than from the models themselves.
IBM is betting that models will commoditize and is positioning a four-part architecture of agents, data, automation and hybrid infrastructure on Red Hat.
The hosts question whether IBM has a clear solution yet, and they also discuss Marvell's growth and the market's turn toward inference.
The skill Christopher Childs credits most for his success has nothing to do with technical ability: listening more than he speaks and meeting people where they are. The CIO of Evans Army Community Hospital, speaking for himself, explains how a master technician can also become a master isolationist, and why he tells his branch chiefs to use IT as a force multiplier and a healthcare enabler.
He describes telling his own leadership that he is not the help desk, why he asks every department for downtime procedures so nobody uses IT as a crutch, and the two questions behind his decisions: does it help deliver safe, quality care, and does it make the patient experience better. Then come his four pillars of leadership, compassion, passion, standards and discipline, and the Frankenstein IT you get when nothing is standardized.
Chris also pushed for the hospital's Innovation Board, a bimonthly meeting where anyone in the organization can bring an idea to the experts who would have to deliver it. It brought IT to the table before decisions get made, and it runs on one rule: how do we get to right? He closes with how he sells standardization when the payoff is slow, using Vocera badges that let the hospital drop five contracts, and with the legacy he wants to leave.
Oregon State's research computing director explains edge data collection at sea and in forests, and why AI is a data problem.
In this 22-minute episode recorded at Yotta 2026, Chris Sullivan, who runs research and academic computing for Oregon State's earth, ocean and atmospheric sciences college, discusses the infrastructure behind field science.
His examples include 4,000 acoustic recorders monitoring spotted owls, 100TB of plankton imagery per five-day cruise, a $150 million research ship costing $1 million per ten-day cruise, and 250TB arriving from a ranger station every six to eight weeks.
He keeps data forever because future models may make it valuable.
He also notes that grant cycles assume 18-month hardware refreshes while the industry moves in three to six months.
HackerOne finds fix times fell from 135 to 62 days, yet unresolved validated vulnerabilities grew 131% as AI speeds discovery.
A HackerOne analysis finds the rate of resolving vulnerabilities rose 54% in the past year and average resolution time fell from 135 to 62 days.
Yet known, validated but unresolved issues grew 131% over two years, and 70% of 111 surveyed security leaders say findings are added to backlogs faster than they are fixed.
CEO Kara Sprague says AI-assisted research is overwhelming DevSecOps teams, which now need AI-driven remediation and formal vulnerability operations (VulnOps) practices to manage exposure debt.
Veteran CIO Brian Ledbetter shares his LEAD IT framework and argues every AI conversation should start with the business outcome.
On this You've Been Heard podcast episode, veteran CIO and U.S. Air Force veteran Brian Ledbetter says titles matter less than how the organization sees you, a perception hardest to change when you rose from the same company's help desk.
He explains LEAD IT, his framework of Lead, Elevate, Align, Deliver, Innovate and Transform, and is pressed on delegation by former guest Bruce Otte.
Ledbetter argues that whoever makes a technology decision should share accountability when it fails, that every AI conversation should start with the outcome, and that guardrails and agent managers will dominate IT talk within 18 months.
National Sugar IT leader Jeremy Mayfield explains running fully SaaS and serverless and delivering a new ERP early and under budget.
This You've Been Heard episode features Jeremy Mayfield, who started in IT when it was called MIS and spent most of 30 years in hands-on leadership, including a VoIP program that saved about $11 million in copper at a 90,000-person company.
National Sugar now runs fully serverless and SaaS, with zero trust instead of VPNs.
Its new ERP went live in under 12 months, ahead of a 14-month approval and well under budget, after a year of requirements work, a 90-page RFP to about 10 vendors and a preference for configuration over customization and phased rollout.
Mozilla CTO Raffi Krikorian explains open source vs. open weight AI models and what keeps enterprises from putting them into production.
CXOTalk episode 933 features Mozilla CTO Raffi Krikorian, who led Mozilla's State of Open Source AI report and explains how open weight models differ from open source models and why license terms matter.
Open models now trail the best closed models by only a few points at a fraction of the price, though frontier models keep an edge on the hardest tasks and went offline for almost three weeks in June after a government export order.
Procurement, compliance and support determine whether open models reach production in large companies.
AI readiness is a human capability problem: workers lack the mental frameworks to work with AI, not just training, says theCUBE Research.
In an AppDevANGLE episode, theCUBE Research speaks with Chad Dehmler, adjunct professor at Schiller International University, about how enterprises are adopting AI faster than workers can keep pace.
The discussion covers prompt engineering's move from workaround to core technical discipline, the cognitive demands AI places on workers, growing enterprise preference for generalists over specialists, and the role of universities in teaching the mental frameworks AI requires.
The takeaway is that organizations treating this as a hiring pipeline issue rather than a skills architecture issue will remain stuck.
Erich Gazaui's first computer was an Apple II, and his mom put him in a computer class to learn BASIC. He was pre-med until organic chemistry, graduated in economics, and learned DOS batch scripts in the dorms so he could play Privateer.
In college he and two friends started a company whose first customer was the university: a survey tool for a political science professor, then a computer lab to run it. That company later became a security firm he ran for 15 years. He went on to be a senior director at TrueCar with "a really big hand in the security side, the governance and compliance", was recruited to Beachbody by its CIO, and was head of IT at ZipRecruiter, where vetting job posters made marketplace safety a competitive advantage and procurement ran "janitorial all the way through AWS". Today security, IT compliance and enterprise apps sit under him at Papa.
His warning about AI: "When you leave a gap in a prompt or in a design, AI is going to fill that gap very quickly." The output looks so professional that you won't know what was filled, and the miss may only show up when it is very expensive to change. He compares it to firing a laser pointer at the moon, where being off by a fraction of a millimeter means you miss it. His answer is what he calls an intent engine: a central source of truth for what the company believes. One request at ZipRecruiter to "make sure that marketing is included" in an event shows why: even the head of marketing came back with a question.
Then the other end of the work: the success criteria, the definition of done. Using mean time to resolution as the example, he explains why "systems do not automatically know the finish line" and keep refining past the state you wanted. He talks about the political capital he spent with his CEO and president to build AI properly, how restrictive his team was about which AI features to enable while they built a central knowledge store, and the playbooks that now run in almost every department, including the day the CEO asked to take his old laptop for personal use and the system handled it with no human interaction. His rule for his own meetings: "Everybody has a voice and it's equal. You leave the title at the door."
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