Neoclouds are a new wave of GPU-first cloud providers built specifically for AI training and inference, offering a focused alternative to hyperscalers like AWS, Azure, and Google Cloud. Instead of optimizing for thousands of general-purpose services, neoclouds optimize for what modern AI teams actually bottleneck on: high-availability NVIDIA-class GPUs, fast provisioning, bare‑metal performance, and low-latency networking for distributed workloads.

That specialization can translate into lower effective cost per training run, higher sustained utilization, and faster iteration cycles—especially when hyperscaler capacity is tight or pricing is unpredictable. Providers such as CoreWeave, Lambda, Crusoe Cloud, Nebius, and Vultr market themselves on speed-to-GPU, simplified scaling, and infrastructure tuned for HPC-style jobs, from fine-tuning foundation models to high-throughput inference. For startups, labs, and enterprise AI teams, neoclouds can reduce the friction of getting from experiment to production by removing layers of platform complexity and prioritizing raw compute. The tradeoff is that hyperscalers still lead in global footprint, compliance breadth, and deep managed service ecosystems, so many teams adopt a hybrid approach—using neoclouds for heavy GPU workloads while keeping core data and platform services on a hyperscaler.

Microsoft Copilot arrived as an AI layer across Windows, Microsoft 365, and cloud consoles, but many users experienced it less as a breakthrough and more as another interface demanding attention. In Word, Outlook, and Teams it could draft and summarize, yet the output often required careful editing for tone, accuracy, and missing context—work people didn't expect to add to already busy days. In Excel and PowerPoint, where users want precision and control, Copilot sometimes felt unreliable or slower than familiar formulas, templates, and search. The assistant also raised awkward "can I paste this?" moments: uncertainty about sensitive data and organizational policies led users to withhold the very details that would make results useful.

When Copilot appeared prominently in UI, some interpreted it as being pushed rather than chosen, increasing resistance. Finally, the pricing model turned mild curiosity into hard scrutiny; if Copilot only saves a few minutes occasionally, a per‑user monthly fee looks like paying for prompts plus extra proofreading. The net effect was skepticism: helpful in pockets, but not essential, not trusted enough for critical work, and not compelling enough to budget for at scale. Adoption stalled where training was thin, and the benefit story never became personal enough.

A surge in fraudulent cloud mining schemes is pulling in unsuspecting investors with promises of guaranteed, sky-high returns—claims that regulators stress are not just too good to be true, but technically impossible. These scams mimic the model of legitimate cloud mining, where users lease real hashing power from authentic data centers, but instead operate like Ponzi schemes: returns are paid with the money of new recruits rather than genuine crypto mining profits. Hallmarks of these frauds include fixed daily payouts, multi-level marketing ploys, and cloned websites that present a façade of sophistication.

Meanwhile, global regulators are stepping up enforcement, with the SEC recently securing a $46 million judgment against a major scam operator and exchanges like Binance actively freezing assets tied to these illicit activities. At the same time, transparency-focused regulations such as the EU's Digital Operational Resilience Act are setting standards only legitimate providers can meet, making it easier to spot and stop fakes. This video exposes the red flags every investor should know, explains how authentic cloud mining really works—including what "hashing power" actually means—and arms viewers with tools to separate financial opportunity from crypto fakery.

In this video, David Linthicum delivers a blunt critique of how large enterprises mishandled cloud adoption and are now repeating the same mistakes with AI. He explains that many IT leaders treated cloud as a simple outsourcing and cost‑shifting exercise rather than a deep architectural and operating‑model transformation, baking failure in from the start. Billions were spent lifting and shifting technical debt into the cloud, only to see complexity, fragility, and run‑rate costs rise while executives declared success.

Linthicum argues that these outcomes were serious enough that many leaders should have been fired, yet boards and CEOs—lacking technical literacy—rewarded them and let vendors shape the narrative. Now, the same people are running AI like another procurement program, chasing hype metrics instead of measurable business value. He shows how poor data discipline, weak governance, and vague "transformation" goals are setting up a second wave of expensive disappointment.

Finally, he explores why this keeps happening: corporate cultures punish technical dissent, reward optimistic PowerPoints, and let vendors and consultants create a halo of hype around failed strategies. His core message: allowing the architects of your cloud failures to lead AI isn't innovation—it's institutionalized incompetence at massive scale.

RSAC 2026 is shaping up to be the year cybersecurity stops talking about "using AI" and starts obsessing over securing it. In this video, we break down the top conference trends emerging from early session themes and Innovation Sandbox signals: the Securing AI pivot (agent governance, inference-time protection, prompt injection, supply-chain integrity, and data leakage), Identity as the new perimeter (machine identities/NHIs eclipsing human users, phishing-resistant authentication, PKI at IoT scale), and the rise of Shadow AI as a board-level risk (discovering, inventorying, and controlling unauthorized AI apps and agents).

We'll also unpack why "vibe coding" accelerates delivery while amplifying software supply chain exposure—and what actionable security inside CI/CD actually looks like in 2026, from SBOM programs and license compliance to automated dependency updates and build integrity.

Finally, we connect the dots to operational resilience: when breach times can be measured in seconds, microsegmentation, lateral-movement controls, and real-time quarantine matter as much as prevention.

If you're planning your RSAC agenda—or your 2026 roadmap—this is your fast, practical briefing, plus a shortlist of Innovation Sandbox finalists to watch. Subscribe for weekly security strategy takeaways, and drop a comment with the tool or trend you want us to analyze next in depth.

Jo's LinkedIn: https://www.linkedin.com/in/jopeterson1/

Jo's email: [email protected]

Dave's LinkedIn: https://www.linkedin.com/in/davidlinthicum/

Dave's email: [email protected]

Top 10 Innovation Sandbox Finalists (RSAC 2026) to Watch:

• @Charm Security: Agentic AI Workforce to prevent scams.
• @Clearly AI: AI-powered code reviews.
• @Crash Override: CI/CD build security.
• @Fig Security: SecOps resilience.
• @Geordie AI: Security and governance for AI agents.
• @Glide Identity: Next-gen authentication.
• @Humanix: Stopping social engineering via behavioral AI.
• @Realm Labs: Monitoring AI agent behavior.
• @Token Security: Managing non-human identity (NHI).

Google Cloud Platform (GCP) lags in third place among the top cloud providers, despite impressive financial growth, due to systemic challenges and fierce competition from Amazon Web Services (AWS) and Microsoft Azure. As of Q3 2023, GCP generated USD 8.4 billion in revenue, significantly trailing AWS's USD 23.1 billion and Azure's estimated USD 24 billion, reflecting its smaller market share of about 11% compared to AWS's 31% and Azure's 25%. GCP entered the market later, missing the early adoption wave that entrenched its rivals, and struggles with enterprise trust due to a less comprehensive hybrid cloud strategy and a smaller ecosystem of third-party integrations.

While Google excels in AI, machine learning, and data analytics—key differentiators—it lacks the breadth of industry-specific solutions and developer tools that AWS and Azure offer. Additionally, AWS benefits from Amazon's e-commerce-driven infrastructure, and Azure leverages Microsoft's enterprise software legacy, creating loyalty GCP can't easily replicate. Without significant strides in addressing enterprise needs, expanding partnerships, and accelerating innovation in hybrid environments, GCP is poised to remain in third place, unable to close the gap with its more established competitors in the near future.

For years, big consulting firms have been selling "cloud and AI strategy" as if it were a product you can just buy off the shelf. In 2026, the jig is up. This video is your no‑nonsense buyer's guide to how large consulting firms should be engaged for cloud and AI work—what to let them do, what to keep in‑house, and where you're most likely to get burned.

We'll walk through the real incentives behind those "preferred partner" AI cloud recommendations, how vendor alliances quietly shape your architecture, and why so many roadmaps end up looking like a sales deck instead of an operating model. You'll learn how to structure engagements so you get industrial‑scale execution—landing zones, governance, migrations—without handing over your strategy, your data, or your future.

We'll cover the critical questions to ask before you sign, the red flags in proposals and SOWs, and how to demand deliverables that leave you stronger and more independent, not permanently dependent on the same firm. If you're a CIO, CTO, architect, or tech leader about to bring in a big consultancy for cloud or AI, watch this first. It might change how you buy—and what you're willing to buy.

Oracle is reportedly tied to roughly USD 56 billion in AI data-center financing, and Wall Street is treating it like a stress test, not a victory lap. In this video I break down why "build it and they will come" can turn into "borrow it and you will bleed." Data centers are fixed-cost monsters: power commitments, depreciation, and interest expense don't care if enterprise customers take six quarters to migrate workloads. If demand ramps slower than supply, Oracle's only lever is discounting—lower prices to fill empty capacity—which can crush margins right when debt service rises. That's how overbuilds become spirals: weaker cash flow leads to tighter financing, tighter financing forces cuts, cuts weaken competitiveness, and the cycle feeds itself.

I'll also compare Oracle's bet to the broader AI infrastructure boom—other builders using heavy leverage—and explain why a sector-wide capacity glut could trigger a price war. If you're an investor, operator, or just tired of AI hype, this is the cold-water analysis: what could go wrong, how it snowballs, and what signals to watch next. We'll talk contract reality, utilization math, and why "strategic capex" can become a balance-sheet hostage situation. Plus: the red flags—syndication strain, downgrades, and sudden price cuts.

In this crucial video, cloud security leader David Linthicum exposes a troubling statistic: 61 percent of cloud security incidents are completely preventable. Drawing on the latest security research, David reveals the most common—and avoidable—mistakes that leave enterprises vulnerable to cyberattacks, data leaks, and compliance violations. He breaks down the top culprits, from misconfigured cloud environments and weak access controls to lapses in ongoing monitoring and lack of employee training.

David explains why organizations often falter on basic security hygiene and shows how these oversights create easy targets for attackers. Importantly, he translates these findings into actionable solutions. Viewers will learn why regular audits, continuous education, and automated security tools are critical for reducing risk, and how building a security-first culture can close the door on costly incidents.

Packed with practical advice, this video gives IT leaders, security professionals, and business executives a blueprint for dramatically improving their organization's cloud security. Don't let your company become another statistic—discover how to avoid the 61 percent of incidents that should never happen, and transform the cloud from a weak spot into your enterprise's strongest line of defense.

Why did Microsoft stock drop even after a headline beat? In this episode, cloud analyst David Linthicum breaks down the market's "beat-and-drop" reaction to Microsoft's latest earnings and what it signals about Azure, AI, and hyperscaler spending. He explains how expectations for a clear cloud re-acceleration collided with guidance that sounded more like "we're investing ahead of demand," raising concerns about capital intensity and near-term margins. Linthicum walks through the optics around Azure growth, capacity build-outs for AI training and inference, and why investors are increasingly sensitive to capex without fast operating leverage.

The conversation also explores a structural shift in enterprise cloud choices—hybrid, private and sovereign cloud, colocation, and managed service providers—and how workload economics (egress, governance, and consumption creep) can make public cloud less attractive for steady-state compute. Finally, he balances the bear case with the bull case: Microsoft's distribution advantage across Microsoft 365, security, and developer tools, and the possibility that today's spend is strategic moat-building. If you want a clearer framework for reading cloud earnings, this video delivers. You'll learn what metrics to watch next quarter, how to interpret "capacity constraints" versus demand, and where AI monetization may show up first in pricing, usage, and margins.