Nearly halfway into 2026, enterprises are beginning to see tangible returns on their AI investments. Yet many are discovering that scaling requires something far less glamorous than flashy frontier models and state-of-the-art benchmarking: Clean, interoperable, governed data.
According to a new AI Momentum Survey from Dun & Bradstreet, 97% of organizations report active AI initiatives, but just 5% say their data is ready to support them. — Read More
Daily Archives: May 15, 2026
Is Software Losing Its Head?
Last month Salesforce announced it would open its APIs and launch a headless product, essentially betting that in an agentic world, its value lies in the data layer, not the UI. It’s a smart repositioning. (Although it’s worth noting that not much appears to have changed technically: the APIs Salesforce is now marketing as a “headless product” have largely existed for years. In other words it was a classic Salesforce marketing launch.) The idea behind the new product is that agents can access the data from the system of record without needing to interact with the UI, which is designed for humans to track workflows.
The announcement is a useful prompt for a more interesting question: if you strip away the UI and expose the database, what are you actually left with? How is that different from a Postgres database, a well-designed schema, and an API? Do the classic factors that make systems of record durable persist, or is there a new set of criteria? In the SaaS era, the system of record was defensible because humans lived in the interface. In the agentic era, that advantage weakens. The defensible layers shift downward into data models, permissions, workflow logic, and compliance, and upward into networks, proprietary data generation, and real-world execution.
When software goes headless, where does defensibility move? — Read More
Engineering roles shift from developing code to managing AI
AI is changing the way engineering teams complete work and measure productivity, with more time spent on reviewing code, fixing bugs and context switching between tools. When AI generates an organization’s code, output metrics improve, cycle times shorten and developers report feeling more productive for moving through work more quickly.
But 81% of engineering leaders said much of the time saved on coding is now spent reviewing AI’s work. Nearly a third of a developer’s day is spent on this invisible work that doesn’t appear in productivity metrics such as output, the report found.
“It is not the work organizations are trying to accelerate; it is the overhead attached to the work. — Read More
2028: Two scenarios for global AI leadership
It’s essential that the US and its allies stay ahead of authoritarian governments like the Chinese Communist Party, or CCP. AI will soon become powerful enough to be used to repress citizens at unprecedented scale, and even to alter the balance of power among nations. And since AI is advancing more quickly by the day, we have only a limited period of time to set the conditions of the competition—and determine whether and how those threats materialize. It’s with this in mind that we outline what’s required to ensure America stays ahead.
The most important ingredient for developing AI is access to the computer chips on which the models are trained (or “compute”). Since the most capable chips are developed by American companies, the US government currently limits China’s supply by enforcing tight export controls on them. Recent history suggests these controls have been incredibly successful. In fact, AI labs in China have only built models close in intelligence to America’s because of their talent, their knack for exploiting loopholes around these export controls, and their large-scale distillation attacks that illicitly extract the innovations of American companies.
In this post, we present two scenarios for what the world might look like in 2028, when we expect transformative AI systems to have arrived. — Read More
World’s first native color LiDAR gives machines human-like vision
For years, machines have navigated the world color-blind. LiDAR sensors – the laser-based eyes of self-driving cars, industrial robots, and inspection drones – build precise 3D maps of their surroundings, but everything is built of monochrome geometric shapes. Ouster’s new Rev8 sensor family aims to change that, not by bolting a camera onto a LiDAR unit, but by fusing color directly into every point of data the sensor captures.
Autonomous perception systems generally fall into two camps: camera-only arrays – like the vision system Tesla uses for its underwhelming Full Self Driving tech – or a two-step sensor fusion approach with LiDAR for precise geometry, a camera for color, and a software algorithm to combine them. That stitching process introduces calibration errors, latency, and spatial mismatches – a problem that becomes critical when a robot or vehicle is moving fast through a crowded street.
Rev8 eliminates that architecture entirely. Each point in the 3D map the sensor generates already carries color information at the moment of capture, with no additional software processing required. — Read More
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