Matt Fitzpatrick, CEO of Invisible Technologies, joins Bloomberg Intelligence’s Mandeep Singh on this episode of the Tech Disruptors podcast to discuss the use of reinforcement learning by frontier model providers for training, as well as the company’s enterprise business. They explore reinforcement learning from human feedback (RLHF), agentic AI and self-improvement, the evolution of large language models, coding agents and contact centers.

Artificial intelligence is reshaping banking infrastructure, from accelerating core migrations to enabling agentic experiences that can automate complex workflows. In this episode of Bloomberg Intelligence’s Tech Disruptors podcast, Vishal Dalal, chief product officer of financial solutions at Fiserv, joins BI fintech and payments analyst Diksha Gera to discuss how banks are using AI to modernize legacy systems and improve data quality while managing security, compliance and governance risks. The conversation explores Fiserv’s vision for AgentOS, an intelligence layer that sits on top of bank data and applications, enabling agents to interact dynamically across platforms.

As AI infrastructure scales up, the conversation is moving beyond graphic processing units (GPUs). Faster compute creates new pressure on the data layer, and companies are increasingly focused on whether their infrastructure can move, manage, protect and deliver data fast enough to keep AI systems productive. In this episode of Tech Disruptors, Bloomberg Intelligence analyst Woo Jin Ho speaks with Alex Bouzari, CEO of DDN, about the company’s role in AI data infrastructure, the shift from storage to broader data platforms, DDN’s high-performance computing heritage, its work with Nvidia and the business-model changes taking shape as AI moves from experimentation to production.

“It’s clear to us that the world is short compute right now, and the industry is racing to catch up,” Rob Bittencourt, partner and head of thematic investing at Apollo, tells Bloomberg Intelligence’s Alexandra Davidov and Paul Gulberg on this episode of the Tech Disruptors podcast. Bittencourt discusses why AI is becoming a private-credit story, with trillions of dollars of data-center, power, chip and infrastructure investment needed to support the next phase of adoption. He also explains how Apollo underwrites AI infrastructure risk, why hyperscaler demand and investment-grade financing matter, and how investors should separate temporary software valuation resets from true business-model disruption.

“This year is a much more radical technology shift and the most consequential, I believe, ever. But still, you need all foundations of the house to be in order,” Sebastian Steinhaeuser, Chief Operating Officer at SAP, tells Bloomberg Intelligence Senior Technology Analyst Anurag Rana. In this episode of Tech Disruptors, the pair discuss SAP’s autonomous enterprise vision, the rise of Joule assistants and agents, and why AI may strengthen the case for cloud migration, data modernization, and application consolidation. Steinhaeuser explains how SAP is embedding business process context, governance, and industry-specific knowledge into its agentic layer while navigating shifts in software pricing, model strategy, and customer demand for measurable AI adoption.

“Every data center will need some part of AI capabilities to run workloads like inference, because it’s just becoming such a fundamental part of all of this cloud technology,” says Alistair Speirs, general manager of Microsoft’s Azure Infrastructure. Speirs joins Bloomberg Intelligence Senior Technology Analyst Anurag Rana on this episode of the Tech Disruptors podcast to discuss how AI infrastructure is reshaping the modern data center, from liquid cooling and dense networking to custom silicon and distributed supercomputing. Speirs explains how Azure is preparing for a world where training, inference and traditional workloads increasingly converge, making software-defined infrastructure, power availability and global scale central to Microsoft’s cloud strategy.

Bloomberg Intelligence Head of Technology Research Mandeep Singh is joined by Nicole Hu, a Silicon Valley technology veteran and GLG expert, to explore the implications of Google’s TurboQuant paper and the evolving economics of AI infrastructure. As hyperscalers look to improve the efficiency of AI workloads, advances in quantization are redefining the tradeoffs between memory and compute, with far-reaching implications for cost, latency, and datacenter architecture. They examine how new approaches to model optimization and inference could reshape hardware requirements, deployment strategies, and the next wave of AI investment.

“OpenAI has only two AI accelerator compute vendors in production today, Cerebras and Nvidia,” Cerebras CEO Andrew Feldman says. Four days after Cerebras went public, Feldman joined Bloomberg Intelligence’s Kunjan Sobhani to discuss the company’s next chapter and the rapidly shifting AI infrastructure landscape. Feldman breaks down the OpenAI deal, the strategic AWS partnership around disaggregated inference and why Cerebras believes fast inference is becoming the industry’s defining battleground. He explains how Cerebras evolved from building the world’s largest chip to operating one of the fastest inference platforms, why disaggregated inference could reshape hyperscale AI deployments and how the company is navigating power, memory and data-center constraints. The episode also explores the competitive landscape beyond GPUs and Feldman’s broader perspective on the next phase of AI compute.

Younger users of dating apps want “lower pressure” and “more authentic ways of connecting,” and Tinder’s new products aimed at meeting those needs appear to be aiding Match Group’s turnaround, CFO Steve Bailey says. Bailey joins Bloomberg Intelligence’s Nicole D’Souza on this episode of the Tech Disruptors podcast to discuss how dating-app behavior is changing after the pandemic, why Gen Z women are central to Tinder’s strategy, and how AI, product updates and helping users connect in real life could reshape growth.

“The more microservices that you have, the more agent-ready you are because you can at least start taking these components, and convert them into agent infrastructure,” says Asa Kalavade, vice president of AWS Transform to Bloomberg Intelligence senior technology analyst Anurag Rana. The pair discuss how agentic AI is accelerating legacy modernization across mainframe, NET, VMware and other enterprise workloads. Kalavade explains how AWS Transform combines deterministic methods with AI to understand old systems, generate modern code and shrink projects that once took years into far shorter timelines, while also making applications more cloud- and agent-ready. She notes that in just one year, AWS Transform has helped customers save more than 1.6 million hours of manual effort and analyze 4.5 billion lines of code as they migrate and modernize applications in the cloud.