Welcome to the latest edition of the World of DaaS Roundup.
Since our last edition, we've seen 2 acquisitions, 2 funding rounds, 5 reads, 1 podcast with OpenAI's Chief Economist, and a data art installation in San Francisco that might change how you see the city.
This week's edition is brought to you by the World of DaaS community. If you're not already a member, join us here.
🌐 WORLD OF DAAS SUMMIT 2026
The White House & Watergate Hotel, Washington, D.C. | September 2 - 3, 2026
⏳ The Countdown Begins.
Less than 35 days until the World of DaaS Summit. This September 2 - 3, a highly curated group of 200+ DaaS leaders for closed-door conversations, curated roundtables, and high-signal networking.
We are almost sold out!
📰 News & Reads
CFOs now have a data monetization playbook, and PwC says the margin structure is unlike anything in traditional business.
Once a data product is built and governed, it can scale at near-zero marginal cost — delivering gross margins that can reach 80%+ and pay back initial investment in 18 to 36 months. PwC's guide, produced with Eagle Alpha, gives CFOs a concrete framework for finding, modeling, and executing data monetization opportunities. The data asset was always there. The playbook wasn't.
The AI market just crossed a threshold. Enterprise buyers are no longer asking if AI works, they’re asking what it returns, and data is at the center of that shift.
ICONIQ's 2026 State of AI Report surveyed executives at software companies building AI products across Q4 2025 and Q2 2026. One standout finding: the market has moved from proving AI works to proving AI pays. The companies winning enterprise budgets aren't the ones with the best models. They're the ones with the best data foundations underneath them.
76% of 10,000 enterprises say AI is delivering ROI. Only 6% say their data is fully ready to scale it.
A quarterly survey across 32 countries confirms the same pattern the industry keeps circling: AI is generating returns, but data readiness remains the constraint. Only 6% say their enterprise data is fully ready to support AI at scale. The next phase of AI ROI runs through data foundations, not better models.
Satya Nadella: companies that rely on a single AI for everything are putting their enterprise data at risk.
Microsoft's CEO warned that enterprises handing over their proprietary workflows and data to a single AI provider are creating dangerous concentration risk. The companies that will survive the AI era aren't the ones that picked the winning model — they're the ones that kept control of their own data layer.
🎙️ DaaS Podcast Spotlight
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OpenAI Chief Economist Ronnie Chatterji on measuring the economic value of data and why AI adoption, not capability, is the next big leap. Summation with Auren Hoffman.
→ Ronnie Chatterji runs research at OpenAI on how AI reshapes growth, jobs, and the economy — using one of the most valuable proprietary data sets in the world. His most provocative finding: the next major shift in AI won't come from better models. It will come from enterprises finally closing the gap between having data and knowing how to delegate full workflows to agents built on top of it.
In this episode, they discuss:
How OpenAI's internal data shows a 10x jump in people delegating a full workday to agentic AI tools — from January to May 2026.
Why the economic value AI creates for consumers — in the hundreds of billions — never shows up in GDP, and what that means for how we measure data economies.
Why the next step function in AI isn't capability — it's adoption, and how data-rich enterprises are pulling ahead.
How releasing proprietary data sets publicly became one of Chatterji's core priorities at OpenAI — and why it matters for the broader research economy.
Why organizations that get data governance and agent workflows right first will see productivity gains others won't for years.
An Unexpected Data Read For Today.
The biggest difference maker in Formula 1 this season isn't a driver. It's not an engine. It's an algorithm.
At the Belgian Grand Prix last week, Mercedes drivers were slowing down mid-race for reasons they couldn't explain — controlled by deployment maps and algorithms they don't fully understand. George Russell went off-track. The code had overridden his instincts.
F1 teams generate over 1.5 terabytes of data per race weekend — tire wear, fuel loads, aerodynamic behavior, live telemetry from hundreds of sensors per car. That data feeds algorithms that now make faster decisions than any human can. The driver's job is to execute. The algorithm's job is to optimize.
The data layer doesn't just support the race anymore. It's running it.
Photo: Richard Mille
Where Data Meets Art
Photo: Fisher Bay Observatory Galley 6: Observing Landscapes | Exploratorium
At San Francisco's Exploratorium on Pier 15, a carved topographic map of the city comes alive with real-time data.
The movement of every bus and train, geolocated photos, and social media posts from across San Francisco are projected onto the map as they happen. The city becomes its own dataset and the dataset becomes the city.
It's one of the most literal visualizations of what the data industry calls "data exhaust": the signals people generate just by living, moving, and sharing. Invisible by default. Extraordinary when you see them.
🔥 In Case You Missed It…
M&A Funding Roundup
Nasdaq acquired Dasseti to expand its institutional data due diligence and monitoring capabilities.
Vertus Group acquired Delineate to strengthen its consumer insight data tracking platform.
Ropedia raised $22M to build data infrastructure for physical AI and robotics training.
Trooly raised $20M Seed to scale its AI-native user data research platform.
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📍 See you in September
This September 2 - 3, the World of DaaS Summit returns to Washington D.C. — at The White House and the Watergate Hotel.
200+ DaaS leaders. Candid conversations. Curated roundtables. Private networking. And the rooms where decisions actually get made.
Spots are limited.
