Greg Rivera

Enterprise AI is moving fast, but measurable value is still lagging. CIO Dive reports only 16% of companies see significant AI impact. The bigger lesson: AI value comes when it’s embedded into workflows, data, governance, and operations. ciodive.com/news/enterpris… #AI #CIO

AI ambition is everywhere. AI readiness is not. CIO.com reports that nearly every enterprise is investing in AI, but only 5% say their data is ready. The next phase of AI is a readiness challenge. cio.com/article/417097… #AI #CIO

Consumer AI may be moving beyond apps and screens. Forbes reports Apple could explore AirPods with cameras as a future AI accessory...pointing to a more ambient, context-aware phase of personal technology. forbes.com/sites/davidphe… #AI #ConsumerTech

AI is scaling fast…but not evenly. Stanford’s latest AI Index highlights rapid progress in adoption, investment, and performance, alongside big gaps in real-world impact. The challenge now: turning scale into value. #AI #TechStrategy hai.stanford.edu/news/inside-th…

AI isn’t just about scale. New work from Google Research shows how extreme compression can deliver strong performance with far less compute. Efficiency may matter as much as infrastructure. research.google/blog/turboquan… #AI #Innovation

As AI demand grows, infrastructure may expand beyond Earth. AWS explores using space-based systems for AI workloads...highlighting how AI is becoming an infrastructure challenge at planetary scale. pages.awscloud.com/aws-reaction-a… #AI #Infrastructure

Before scaling AI, many orgs face a simpler issue: They lack clarity into their application portfolio. Not every app is ready for what’s next...progress starts with knowing what to keep, modernize, or retire. cio.com/article/414343… #AI #CIO

There’s no single “engineering productivity” metric. Focusing on output (LOC, commits, tickets) often misses the point. Productivity is about solving the right problems and delivering value...especially as AI accelerates development. balderton.com/resources/the-… #Engineering #AI

AI adoption isn’t uniform across an application portfolio. TIME analysis (Tolerate, Innovate, Migrate, Eliminate) helps answer a key question: Where should we apply AI...and where shouldn’t we? Portfolio clarity = faster, safer AI adoption. staunstender.com/article/is-it-… #AI #APM

AI adoption is shifting from capability to trust. McKinsey highlights that organizations focusing on AI governance, transparency, and reliability are seeing stronger business value. Trust is becoming a driver of impact. mckinsey.com/capabilities/t… #AI #Leadership

March Madness might be the most public data science exercise of the year. Fun piece from Georgia Tech on using models and rankings to build a bracket...and why uncertainty still wins plenty of games. Go Huskies! news.gatech.edu/news/2026/03/1… #MarchMadness #DataScience

The next AI constraint may be power, not chips. Reuters reports data-center growth is running into electricity limits, turning AI into an infrastructure and energy story. Alongside new capacity, software efficiency will matter more at scale. reuters.com/markets/commod… #AI

@josemariasiota Interesting to see data on how high-performing companies see CIOs more involved in business strategy. We are seeing IT issues like tech debt becoming a board-level topic. CIOs who quantify and reduce complexity first move faster with far less risk.

The new CIO mandate: Strategy, speed, and scaled intelligence dlvr.it/TQrtZ3

Tech layoffs often dominate headlines. But the deeper story is capital and talent shifting toward AI, automation, and new infrastructure. Moments like this often signal industry turning points... cfodive.com/news/ai-linked…

Five big macro trends are shaping 2026...from AI investment surges to protectionism and central-bank shifts. Worth understanding for tech and business leaders alike. atlanticcouncil.org/dispatches/fiv… #Economy #CIO #TechLeadership

@SarahChieng Interesting framing. In many enterprises, latency debt is a downstream symptom of broader technical debt. AI models may get faster, but if the application portfolio is bloated or poorly architected, speed gains don’t translate into business agility.

The Year of Latency Debt (And How Big Tech Is Paying It Down) In the past six months, the four most important companies in AI have all made massive billion dollar bets on faster inference, and none of those bets were on the NVIDIA hardware that already dominates AI today. What is ‘Latency Debt’? In software engineering, "technical debt" refers to the accumulated cost of shortcuts and slop code that works today but creates problems tomorrow. Engineers move fast, auto-accept AI suggestions, and defer the cleanup. Latency debt works the same way, it is the hidden cost we accumulated while optimizing models faster than infrastructure. We optimized for intelligence, but each advancement like more model parameters or new reasoning models requires more compute per response. If you've been building with frontier AI models, you've probably felt this too. This is the best technology humans have ever built, and using it often feels like watching paint dry. The Smart Money Is Already Moving Here's how you know latency debt is real: follow the money. > Google, despite being one of NVIDIA's largest customers, built its own Ironwood TPU, 4x faster than NVIDIA's GPUs > Anthropic committed tens of billions of dollars to Google's TPU infrastructure > NVIDIA itself paid $20 billion to gut Groq for their IP and top talent > And OpenAI just purchased 750 MW worth of Compute from @cerebras Here is a full deep dive on how we got here, the software vs. hardware misalignment, and how we are finally paying down this debt.

Global IT spend is on track to top $6 trillion in 2026, driven by security, cloud and AI. But the shift now is from pilots to production scale, with cost visibility and governance front and center. #CIO #ITSpending #TechLeadership splunk.com/en_us/blog/lea…

In 2026 the CIO role is expanding from IT management to enterprise strategy and business impact. New from InformationWeek: tech leaders are now shaping operations, governance, and business outcomes - not just running systems. informationweek.com/it-leadership/… #CIO #Leadership #ITStrategy

AI success starts with the right first wins. New from CIO Dive: targeted, measurable use cases build real momentum - not broad experimentation. Leadership is about choosing where to start. ciodive.com/news/gold-bond… #CIO #AI #TechLeadership

AI hype from the top won’t stick if employees don’t trust it. New Axios piece shows exec enthusiasm often falls flat, underscoring that trust & alignment matter as much as tech. axios.com/2026/02/05/ai-… #CIO #AI #Leadership