Security Gravity

The equipment that fills data centers is evolving rapidly, driven by the need to fulfill the seemingly insatiable appetite of AI applications. The Open Compute Project (OCP) was founded by Meta/Facebook to promulgate equipment standards and its annual Summit has grown from a small specialized gathering, to an event that strains the capacity of the San Jose Convention Center. Senior research analyst Perkins Liu returns to offer his take on this meteoric growth with host Eric Hanselman. AI requirements are pushing ever greater scale both logically and physically, with the width of server racks doubling in the Open Rack Wide (ORW) specification to support greater density and better serviceability. The OCP Foundation is also working on silicon interoperability and is setting specifications for chiplet integration. Liquid cooling has moved from a nice to have feature to a required capability as a means to dissipate the huge amount of energy drawn by ever denser GPU arrays. Energy delivery is changing with the advent of higher voltage DC power. The early OCP efforts on 48 volt DC are paling in the face of new 800 volts designs. The OCP Foundation is also expanding its mission to include education, with the establishment of the OCP Academy. It aims to raise workforce skills in open hardware and will offer online training in data center technologies. That underscores not only the expansion of the OCP Foundation's mission, but also the increasing scale of the ecosystem that supports data center environments and complexity and interdependency that AI creates.   More S&P Global Content: Sustainability continues to drive datacenter infrastructure evolution Webinar: Talk to the Expert - Artificial intelligence, datacenters and energy: Is APAC ready for th… For S&P Global subscribers: Air cooling remains prevalent, but liquid cooling is gaining momentum – Highlights from VotE: Datac… Adjusted definitions of datacenter markets in China align with socioeconomic processes Datacenters increasingly use direct current to cope with AI workloads Credits: Host/Author: Eric Hanselman  Guest: Perkins Liu Producer/Editor: Feranmi Adeoshun Published With Assistance From: Sophie Carr, Kyra Smith

There are many ways to frame conversations around cyber security. They can take on many aspects of security, ranging across the technical to the human. With the press towards platform consolidation, it's critical to reconsider the interaction between the human and technical elements and research director Scott Crawford and Javvad Malik, CISO advisor at KnowBe4, join host Eric Hanselman to dig into this important interplay. It's all too easy to fall into security practices that focus on technical requirements and don't account for the friction that is created for the people who use them. It's also easy to drop into a mindset that better security is just a matter of user education. Effective security requires thinking about user experience, as well as technical controls. Authentication is one of the most frequently experienced security interactions and also one where a technical focus can have the highest impact on the people using. Authentication happens often and is also a key element in securing IT environments. The push to multifactor authentication, for example, is an important step in security enforcement and can require a significant change in how people interact with the systems that support their daily lives. A wholistic approach to security can help teams move beyond the frustrating cycle of user training and shift to collaborative security implementations. More S&P Global Content: The evolution of security platforms – 6 centers of gravity shaping the market AI for security: Agentic AI will be a focus for security operations in 2025 From KnowBe4: The Hidden Cybersecurity Threat: Securing the Human-AI Relationship For S&P Global Subscribers: Security for agentic AI: Key areas of focus Worlds colliding: Uniting proactive and reactive security Identity & Access Management Market Monitor & Forecast Beyond ITDR: Viewing identity security through a wider lens Credits: Host/Author: Eric Hanselman Guests: Scott Crawford, Javvad Malik Producer/Editor: Feranmi Adeoshun Published With Assistance From: Sophie Carr, Kyra Smith

The enthusiasm for AI has been impressive and it's leading to the inevitable questions about whether or not all of it is warranted. Melissa Otto CFA, head of S&P Global's Visible Alpha research team, returns to discuss concerns about an AI bubble with host Eric Hanselman. Defining what actually indicates a bubble might be the trickiest aspect of the question. Is it outsized levels of debt? Unrealistic valuations? Both debt and valuations are high, but are they unreasonably so? So much depends on seeing what AI capabilities can deliver and we're still in the early days of understanding what ROI really is. There are still challenges in getting the domain approaches right. Doing real analytical work is more challenging and there is still more work to do in integrating with business processes.  And it's not just the technical aspects that are in play. It's possible that macroeconomic restraints are holding back even more enthusiastic spending that could create a bubble. Current interest rates create caution in taking on additional debt. It's also possible that rate cuts could unleash more risk taking and overextension through debt. That might be an indicator of a looming bubble. Or maybe not… More S&P Global Content: Datacenter & Energy Innovation Summit 2025 Otto: Markets are grappling with how to price AI-related stocks   Netflix earnings preview: Q3 2025 Next in Tech podcast, Episode 239: AI Infrastructure Next in Tech podcast, Episode 221: Datacenter slowdown? For S&P Global Subscribers: Shifting AI exits Venture capital outlook: Investments surge as exits lag Hyperscaler earnings quarterly: AWS, Azure and Google Cloud capex projections trend still higher Tech Trend in Focus: GPUaaS market momentum Credits: Host/Author: Eric Hanselman Guest: Melissa Otto, CFA Producer/Editor: Feranmi Adeoshun Published With Assistance From: Sophie Carr, Kyra Smith

The world of cryptocurrencies has seen no shortage of upheaval, but regulatory clarity that has arrived with recent legislation has created opportunities. Stablecoins are an area that's seeing renewed interest in FinTech as a means of reducing friction for global payments. Jordan McKee, Sampath Sharma and Nathan Stovall return to the podcast with host Eric Hanselman to look at how stablecoins are being put to work and how they're being used. For applications like cross border trade and the remittances trade, stablecoins can increase speed and potentially reduce costs. They can be considered another payment rail through which to conduct business.   Stablecoins are not without challenges. There are regulatory imperatives to be met, like anti-money laundering (AML) and know your customer (KYC). Trust needs to be built in the market and education is needed around their potential benefits and risks. Traditional banks are getting involved, but they have additional stumbling blocks. There are technical hurdles, such as the readiness of backend systems to handle the higher precision values of stablecoins. And there are new concerns around custody risk, as handling wallets and the enhanced security required are new skills for many. The potential benefits and lucrative markets may give them enough incentive to take the plunge.   More S&P Global Content: US banks maintain favorable earnings while confronting economic uncertainty Money20/20 Europe 2025: Key trends and developments Balance sheet, M&A and capital raising strategies for a volatile environment Next in Tech | Ep. 230: FinTech Advances Street Talk | Episode 142: Despite Looming Rate Cuts, Strong Deposit Franchises Still Take The Cake   For S&P Global subscribers: Data Insight: Cross-border payments volume to surpass $17 trillion by 2030 Cross-Border Payments Market Monitor & Forecast - Data Visualization Cross-border payments, stablecoins generate buzz at a low-key Money20/20 Asia Tariffs create urgency for payment optimization strategies among large merchants, SMBs   Credits: Host/Author: Eric Hanselman  Guest: Jordan McKee, Sampath Sharma, Nathan Stovall Producer/Editor: Feranmi Adeoshun Published With Assistance From: Sophie Carr, Kyra Smith

Some might think that the metaverse has been a passing technology fad, but rumors of its demise are greatly exaggerated. While the language may have shifted to things like spatial computing and augmented reality, the technologies and use cases have been flourishing. Analysts Neil Barbour and Ian Hughes return to discuss recent study results and industrial metaverse progress with host Eric Hanselman. Companies are working to build a digital thread that runs through their organizations, linking the physical and virtual worlds. They're leveraging digital twins to simulate operations and putting AI to work creating and populating the virtual environments in which they run. When gaming companies shift to building virtual large world models in partnership with defense contractors, the metaverse has clearly shifted gears. Virtual environments are being used for training as well as strategic planning. Smart phones are being integrated into retail space planning and assessment and emergency teams are playing Tetris to secure helicopter landing sites. The metaverse is alive and well and making some large steps forward. More S&P Global Content: 451 Digital Industries Insider Metaverse Survey: Nearly half of consumers interested in buying smart glasses Metaverse Digest: A look at Augmented World Expo, Snap updates Lens Studio As the Esports World Cup Wraps, Kagan Looks at the Growing Potential of Competitive Gaming For S&P Global Subscribers: Industrial metaverse adoption grows – Highlights from Metaverse B2B survey Metaverse Digest: Generative AI interactive world-building accelerates More consumers piling into virtual worlds – Highlights from Metaverse Consumer survey The dawn of industrial AI Credits: Host/Author: Eric Hanselman Guests: Neil Barbour, Ian Hughes Producer/Editor: Feranmi Adeoshun Published With Assistance From: Sophie Carr, Kyra Smith

Managing the capabilities of an organization's workforce is challenging in the best of times, but the current upheaval created by AI technologies entering the workplace makes it all the more complex. The technologies that have put to work around this problem are many and varied and analyst Ethan Ray joins host Eric Hanselman to look at where we've come from and where we could be headed. There's a rush to determine if new technologies are making workers more productive and what skills are needed to leverage them. Both a recent S&P Global study and the HR Tech conference showed some trends that could be transforming working environments. Workforce intelligence approaches are looking to build a wholistic picture of knowledge, skills and ability of employees and then do skills matching and development planning.   As in other technology disciplines, a proliferation of tools has made many organizations' efforts at workforce management complicated. It's another place where many are considering integrated platforms that that can bring together HR, IT and financial teams to better understand the use of tools and resulting productivity. There are questions about what we can and what we should measure to truly understand efficiency and effectiveness. How can we understand what combination of skills and tools are generating the best financial outcomes? Better technology should hold the answer, but the path forward is not always clear.   More S&P Global Content: AI upskilling: Navigating the urgent need for workforce transformation   For S&P Global subscribers: Take 5: AI shakes up HR Workday strengthens standing in AI-driven frontline recruitment space with Paradox buy HR teams see AI as vital but face adoption barriers – Highlights from VotE: Workforce Pro… AI holds potential for workplace transformation, but employee concerns must be addressed – Highligh… MIT Manufacturing Day headlined by circular economy, workforce skills and modularity   Credits: Host/Author: Eric Hanselman  Guest: Ethan Ray Producer/Editor: Feranmi Adeoshun Published With Assistance From: Sophie Carr, Kyra Smith

Getting data to where it's needed has always been an infrastructure challenge, but the scope and scale of the problem has become much more acute as data volumes rise and AI demands more. Research director Henry Baltazar returns to look at the latest results from the Voice of the Enterprise Data Migration study with host Eric Hanselman and reflect on how organizations are addressing the crunch in data movement. There's been a significant increase in the number of enterprises reporting that they've moved to migrating petabytes of data and they're now not only shifting to cloud, but moving between clouds, as well. They're also enlisting the help of service providers more often, an indication that the scale of these activities requires professional support to manage risk. Data movement at this scale demands a shift in tactics and more are using physical transport, the shipping of storage media to cloud providers, to get the job done. They can't risk the downtime needed for network transit, although network capacities are also rising. There are opportunities that cloudy environments present in managing data costs and new strategies that enterprises can put to work to lower not only costs, but risks, too.   More S&P Global Content: Next in Tech | Ep. 234: Broadcom VMware Explore Conference Next in Tech | Ep. 224: Context Around MCP   For S&P Global subscribers: Data Insight: Public cloud storage market to hit $56B by 2029 as data demand rises As data migration evolves, outage and security concerns grow Security concerns, large payloads are key challenges for data migration – Highlights from VotE: Sto…   Credits: Host/Author: Eric Hanselman Guest: Henry Baltazar Producer/Editor: Feranmi Adeoshun Published With Assistance From: Sophie Carr, Kyra Smith  

To get to the benefits that AI offers, organizations have to address their technology infrastructure in ways that are much broader than historical approaches. Senior analyst Greg Macatee joins host Eric Hanselman to delve into what's required and what enterprises are identifying in the recent Voice of the Enterprise AI and Machine Learning study. Enterprises are struggling with raising the success levels of AI projects. Over 60% report moderate to severe challenges in achieving AI success. Bringing together the computational power and the right quality data in the right locations can be complicated in the hybrid environments that more are operating. It's not just a matter of being more selective with use cases, AI requires a set of organizational skills that have to be honed. Starting small and iterating can reduce risk while building competency.  Infrastructure has to shift in new ways, as well. Data management processes that can build the necessary data pipelines to feed AI applications bring together a broader set of tech disciplines. There are new wrinkles in AI infrastructure ecosystems, with new providers looking to address supply chain constraints, like the Neocloud or GPU as a Service  (GPUaaS) providers. Even hyperscalers are looking to them to meet surging demand in a tight market. Those new options offer new choices, but enterprises need to match them with their AI goals. More S&P Global Content: Navigating the AI infrastructure landscape The path from LLMs to agentic AI Next in Tech | Ep. 225: Security for MCP For S&P Global Subscribers: AI infrastructure strategies evolve amid widespread data challenges – Highlights from VotE: AI & Machine Learning Generative AI Market Monitor & Forecast AI infrastructure: Trends, thoughts and a 2025 research agenda Credits: Host/Author: Eric Hanselman Guest: Greg Macatee Producer/Editor: Adam Kovalsky Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith

The annual gathering on infrastructure virtualization that is the VMware by Broadcom Explore conference has wrapped up and there are important takeaways from both the formal program and informal discussions. Analysts Jean Atelsek, Henry Baltazar and William Fellows join host Eric Hanselman to talk about their takes on the event. The newly integrated VMware Cloud Foundation 9.0 (VCF) suite has shipped and along with it a new approach to the product portfolio. AI-focused capabilities have been added as VMware aims to create a private cloud portfolio to rival the public cloud offerings. Higher level data services are the first service abstractions available, with the promise of more to come. In the new organizational structure, the Tanzu product offering is now in its own division in Broadcom. While that offers it independence, it's also meant that in areas like AI functionality, there is some overlap between it and AI capabilities being built into VCF 9. The larger challenge for Broadcom is to motivate customers to fully implement VCF 9 and put all of its capabilities to work. More S&P Global Content: Next in Tech | Ep. 222: FinOps – Managing Cloud and AI Costs Next in Tech | Ep. 214: KubeCon and Cloud Native Next in Tech | Ep. 183: Exploring Broadcom VMware For S&P Global Subscribers: VMware Cloud Foundation 9.0 manifests Broadcom's vision for modern private cloud VMware Tanzu enhances support for generative AI and agents with Tanzu AI Solutions VMware remains dominant as organizations evaluate alternative virtualization technologies Credits: Host/Author: Eric Hanselman Guest: Jean Atelsek, Henry Baltazar, William Fellows Producer/Editor: Adam Kovalsky Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith

Can you have too much data for an AI application? In the mad dash to collect the raw material for AI applications, it can be tempting to pull in as much as you can. Product manager Emily Jasper returns to the podcast with a set of recommendations for more strategic use of data with host Eric Hanselman. Just as it might not be wise to load up on everything on a buffet, being strategic about using the data that best suits the goals of your project can improve outcomes and help to manage risk. By understanding the data that you're putting to work, you can bound the universe of outcomes and simplify the process of bringing it into the AI application pipeline. At the same time, the process of data governance becomes clearer when the sources are better understood.  Bringing an understanding of the set of data resources that an enterprise has is critical and has to be accompanied by knowledge of the quality of that data. The principles of library sciences are back in focus in AI, as organizations work to curate data characteristics and provenance. As in so much of AI, matching the ecosystem of tools, data providers, and capabilities to the use cases being built is fundamental to project success. Managing risk in AI has become a process of bringing the right data to the right problem. More S&P Global Content: The path from LLMs to agentic AI Next in Tech | Ep. 224: Context Around MCP Next in Tech | Ep. 225: Security for MCP For S&P Global Subscribers: Data Management Market Monitor & Forecast Automation pervades data management practices – Highlights from VotE: Data & Analytics Technology Primer: Model Context Protocol explained The evolution of agentic process automation: Midyear market and technology update Pace of AI agent advancement could spur M&A in the sales automation market Credits: Host/Author: Eric Hanselman Guest: Emily Jasper Producer/Editor: Adam Kovalsky Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith