Welcome to the latest edition of the World of DaaS Roundup.
AI companies want it. Regulators are fighting over it. Companies are buying it. The most interesting thing happening in AI right now isn't the models, it's the scramble for proprietary information. This week's stories are a reminder that in a world where technology gets commoditized fast, unique data remains one of the few assets you can't easily copy.
📰 News & Reads
AI-related data breaches surpass stolen credentials for the first time in 19 years.
Verizon’s 2026 Data Breach Investigations Report analyzed 31,000+ incidents and found AI is shrinking defender response time from months to hours. Employee use of unapproved AI tools tripled to 45%, opening a fresh channel for company data to walk out the door.
Training data is the new trade secret — and lawmakers are forcing AI companies to choose.
The Training Data Transparency Act puts AI developers in a bind: disclose what data trained your models or face legal exposure. X.AI called it "a trade-secret-destroying regime." A federal court disagreed. The fight over data provenance just entered constitutional territory.
UK regulator gives publishers the right to opt out of training Google’s AI models.
Britain's CMA imposed new conduct requirements on Google search today, giving publishers more control and bargaining power over how their content is used. Google now accounts for 90% of UK queries — and regulators just decided that's too much power over someone else's data.
Yahoo Finance launches AlphaSpace — a Bloomberg Terminal for the other 150 million investors.
AlphaSpace brings institutional-grade financial data, AI-powered research, and real-time analytics to everyday investors at $479/year vs Bloomberg's $27,000. Financial data just stopped being a privilege.
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🎙️ The Latest World Of DaaS Podcast
Goldman Sachs CIO Marco Argenti on the data challenges and regulatory concerns that come with deploying AI at enterprise scale.
A year and a half after their last conversation, Argenti is back to discuss what AI deployment at Goldman Sachs actually looks like today — from how AI coding is changing the work of developers and engineers, to the data governance and compliance frameworks that make it possible at a regulated bank. The message is clear: the hardest part of enterprise AI isn't the model. It's the data.
In this episode, they discuss:
Why data quality and governance are the real bottlenecks in enterprise AI deployment — not model capability.
How Goldman is building internal AI tools while managing regulatory exposure around sensitive financial data.
Why AI coding is reshaping how engineers work — and what that means for data teams.
The tension between moving fast on AI and maintaining the data controls a regulated institution requires.
Why the banks that get data governance right now will have the most durable AI advantage.
An Unexpected Data Read For Today.
New research from Harvard Business School challenges a long-held assumption: stronger privacy regulations don't shrink your data. They expand it.
An analysis of 16,000 users found that after California and Virginia's privacy laws took effect, users in those states shared more data — and more diverse data — than users elsewhere. Clearer disclosures and consent mechanisms built enough trust to unlock participation from consumers who previously refused to share anything.
The implication for data businesses is significant: privacy compliance isn't a cost. It's a data acquisition strategy.
HBR Staff; travelview/Getty Images
Where Data Meets Art
This week, the data set is you.
Holly Herndon & Mat Dryhurst transform a museum into a live AI training ground — visitors record their voices to build a dataset that trains an AI choir. Starmirror, opening June 27 at Kunstsammlung NRW in Düsseldorf, makes the invisible mechanics of data collection something you can hear, feel, and participate in.
Behind every AI-generated image, sound, or text lies a dataset. This exhibition makes that process tangible.
🔥 In Case You Missed It…
M&A Funding Roundup
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