Next in Tech

• Smart Spaces

  So much of the world around us is sensing and controlling with data, but it’s not often coordinated. Smart spaces technologies put that data to work and integrate capabilities and Zoë Roth returns to the podcast to dig into the potential of these systems with host Eric Hanselman. Smart spaces have grown out of the sensing roots in technologies like the Internet of Things (IoT) and shares a key element – the need to link operational technology (OT) with information technology (IT). Things like building environmental systems have historically been separated from the technology stack supporting the people that work in the building. Integration of these systems has the potential for a wide range of benefits, including improving working conditions, enhancing physical and logical security and reducing energy use. Stadiums and public venues are putting smart spaces technology to work to improve fan experience and improve safety. Extending to the macro scale, when smart spaces integrate at a city level, there’s greater efficiency and effectiveness across transportation systems, electric grids and governmental systems. Making this all work requires extending across information and functional silos. It’s a similar set of challenges that exist in digitization efforts in areas like retail, manufacturing and energy. As capabilities like digital twins and expanded support in edge computing mature, they hold promise to make smart spaces even more capable. More S&P Global Content: Smart Cities Connect 2024 Examining AI readiness and conducting a reality check What EU funding means for the next generation of European smart cities Unlocking value in smart spaces with location data Data Insight: Redefining retail — generative AI and automation Credits: Host/Author: Eric Hanselman Guests: Zoë Roth Producer/Editor: Amaan Zafar, Donovan Menard and Odesha Chan Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith

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• Mobile World Congress preview

  Mobile World Congress, the annual gathering of telecom industry players, has expanded to embrace a broad range of technology and this year’s event will bring together a broad range of vendors, telecom operators and industry leaders. The theme of convergence has seen real world impacts and Raul Castañon, Mohammed Hamza and Lynnette Luna return to discuss what they’re expecting to see with host Eric Hanselman. The rise of network API’s for security and location services looks to open the door to greater application integration. It’s something operators have been hoping for, but there are questions about how they’ll monetize it.  Many of the technologies that previewed in previous years are gaining traction. Open RAN technologies have matured and the ecosystem supporting them has expanded. There are still questions lingering about deployment timelines, but that seems to be shifting to a matter of when, rather than if. And, of course, AI is playing a role in much of what’s taking place in the exhibition halls. More S&P Global Content: MediaTalk | Season 2:  EP 20 - Wireless Outlook for 2024 as Carriers Next in Tech | Ep. 181: Lighting up Fiber Ericsson partners with global telecom operators to launch new network API venture Ericsson-led API joint venture gets its first CEO Credits: Host/Author: Eric Hanselman   Guests: Raul Castañon-Martinez, Mohammed Hamza, Lynnette Luna Producer/Editor: Amaan Zafar and Odesha Chan Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith

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• NVIDIA quarterly earnings

  Of the many companies that are part of the AI wave, NVIDIA has been at the core of AI infrastructure and a darling of the markets. Their performance in the market is driven in part by consistency in their performance and Melissa Otto, head of TMT Research for Visible Alpha, part of S&P Global Market Intelligence, joins host Eric Hanselman to look at the sentiment around the upcoming earnings. The enthusiasm for AI has driven expectations higher and higher and the company has delivered.  The rampant enthusiasm for AI’s future hasn’t been without some cautious moments. In January, the announcement of the DeepSeek model roiled markets with the prospect of lower compute consumption for model training. The concern was that demand for all things AI, GPU’s included, might wane. It might also have been a reason for a jumpy market to take a step back. If you dust off your economics textbooks, Jevon’s Paradox has been cited by many as an indication that consumption could accelerate. Time will tell! More S&P Global Content: Assessing the Factors Driving Technology Investments Big Picture for Generative AI in 2025: From Hype to Value Webinar: The Big Picture on GenAI and Market Impacts NVIDIA takes commanding revenue lead in semiconductor space Intelligent and agentic process automation and integration platforms: 2025 research agenda GPU as a Service Market Monitor & Forecast 2025 Trends in Data, AI & Analytics Credits: Host/Author: Eric Hanselman Guests: Melissa Otto Producer/Editor: Amaan Zafar and Odesha Chan Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith

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• Agentic AI Impacts

  The next phase of the AI wave is the arrival of agentic AI – where agents can take action on a user’s behalf. That’s enough of a big deal, but when the head of a tech giant says agentic is going to replace most SaaS applications, something different might be afoot. Analysts Sheryl Kingstone and Chris Marsh return to the podcast to look at the realities of this suggestion with host Eric Hanselman. Agents could become the new user interface for enterprise data, but there are a set of challenges in making this work. On the one hand, one of the largest issues with autonomous action, accountability for actions taken, is far from settled in both regulatory and legal frameworks. On the other, much of enterprise information is still held in systems where it may be difficult for an agent to reach.  Agentic AI could provide a gateway to the myriad of systems that run the modern business. Opening access to data and the ability to aggregate across an organization could be tremendously powerful. Capturing the business logic that is often embedded in SaaS systems is difficult, but the shift to decoupling through API’s and the expansion of systems of delivery could open the door to agentic progress.  More S&P Global Content: Big Picture for Generative AI in 2025: From Hype to Value Webinar: The Big Picture on GenAI and Market Impacts For S&P Subscribers: 2025 Trends in Data, AI & Analytics   Credits: Host/Author: Eric Hanselman Guests: Chris Marsh, Sheryl Kingstone Producer/Editor: Kyle Cangialosi and Odesha Chan Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith

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• Ethical AI Data

  Ethical concerns about the use of AI have to start with training data. Too often, the primary concern is simply generating sufficient data, rather than understanding its nature. Emily Jasper and Abby Simmons are back to continue the conversation started in episode 198 with host Eric Hanselman. With generative AI, the data is the application in its most formative sense. Unlike traditional application development, where the expectation is that functionality will be expanded in later releases, GenAI applications require careful design of training data before training takes place. The perspectives contained in data age rapidly and model training doesn’t differentiate between outdated and current indications. Old data can effectively poison model outputs. Businesses risk alienating customers with models that are trained with data that don’t properly represent them. This is particularly true with marginalized communities, where language and context can change over shorter time frames. While there is research work on model retraining, work in AI today has to focus on effective data quality management. DeepSeek is causing a significant rethinking. Human data cleansing can be effective, but can’t scale to AI demands. Data workbench tools and synthetic data approaches can help, but better automation is needed to ensure that data sets are truly representative. Data collection and data sourcing need much greater attention to ensure that model results can engage the target audience and not be a liability. It’s a fundamental question of accountability that requires thinking in ways that are different than legacy development processes. Mentioned in this episode: https://transtechtent.com https://kevinguyan.com/queer-data/  More S&P Global Content: Webinar: AI in Action: Leveraging NLP to Answer Subjective Questions 2025 Trends in Data, AI & Analytics Take 5: Data quality and AI — a bidirectional relationship Compliance automation, Part 1: Governance, risk and compliance, or something new? Credits: Host/Author: Eric Hanselman Guests: Emily Jasper, Abby Simmons Producer/Editor: Kyle Cangialosi and Odesha Chan Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith

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