Irving Wladawsky-Berger

Pathways and Challenges Toward an Open Data Ecosystem

“Our data is everywhere and powering everything,” noted “Pathways to Open Data,” a report by Linux Foundation Research published in March of 2025. “From marketing, to healthcare, to government services, to the emerging phenomenon of programming AI agents, organizations leverage data to be as efficient and effective as possible. However, data is often siloed within entities and any third-party data access requires overcoming significant technical, legal, economic, operational, and cultural obstacles that are multifactorial and at times may seem intractable. The increasing reliance on data calls for an assessment of these obstacles and how organizations can shift toward greater openness and sharing.”

The report defines open data as “data infrastructure that has the technical and legal requirements in place to make the data freely accessible for universal use, reuse, and redistribution.” Open data has its roots in open science and open knowledge in general, where non-personal and non-commercial data is freely published for the purpose of greater innovation, transparency, and collaboration. This culture of openness is strongest in public institutions, where the data collected is considered a public good without profit-generating opportunities, and where transparency of government and public-sector information is encouraged.”

Many governments have developed open data portals to enable access to public information, such as the Public Sector Information Directive launched by the European Union in 2003, and Data.Gov, launched by the Obama administration in May of 2009 with the goal of increasing transparency and accountability through open data. Initially launched with 47 data sets, Data.Gov now includes over 300,000 open data sets.

What Are AI Agents and How Should We Use Them?

After decades of promise and hype, AI has (finally) come of age. The 2010s saw increasingly powerful deep learning systems surpass human levels of performance in tasks like image and speech recognition, skin and breast cancer detection, and winning at championship-level Go. The 2020s saw the advent of generative AI systems, with their impressive ability to create high-quality original content, — e.g., text, images, video, audio or software code, — in response to a a user’s natural language prompts. And now, we’re seeing the emergence of agentic AI.

Wikipedia defines agentic AI as autonomous AI systems “that can make decisions and perform tasks without human intervention.” Agentic AI systems are designed to “autonomously make decisions and act, with the ability to pursue complex goals with limited supervision. It brings together the flexible characteristics of large language models (LLMs) with the accuracy of traditional programming.”

A few weeks ago I posted a blog, Agentic AI: The Evolution of Application Development, mostly based on a McKinsey Digital report, “Why agents are the next frontier of generative AI.” Agentic AI systems are now taking AI to the next level, enabling us to automate processes that can plan and execute their actions with limited human intervention. “In short, the technology is moving from thought to action.”

Informational GPS: Navigating the Complex World of 21st-Century Knowledge

“Until the early 2000s, paper maps did not exist — they were simply called maps” wrote Reid Hoffman and Greg Beato in “Informational GPS,” one of 12 essays in The Digitalist Papers, a roadmap of the possible futures that the AI revolution might produce. Before May of 2000, the Global Positioning System (GPS) was deliberately limited for civilian use due to security concerns. But then access to GPS was made available to everyone as a public good

“In the wake of this policy shift, GPS experienced a Cambrian explosion of innovation.” Improved performance and falling prices for consumer receivers quickly turned GPS into an indispensable part of life in the 21st century. A 2019 study on the Economic Benefits of GPS by the National Institute of Standards and Technology (NIST) estimated that by 2017, GPS had generated roughly $1.4 trillion in location-based services across a variety of industries since being made available for civilian and commercial use.

The term GPS has entered the popular vernacular to mean one’s specific location at a specific point in time. Its breakthrough application has been turn-by-turn navigation, enabling us to move through the physical world with precise, constantly updated information. “At literally every turn, these navigation systems increase individual agency by telling us where we are, what else is nearby, what obstacles might impede our progress, and so much more.”

A major reason why GPS has been so widely adopted is that it offers us individual agency along with broad access to navigation information. GPS not only suggests the best routes for us to follow based on current traffic conditions, but it also offers us the ability to modify our route along the way and quickly readjusts to accommodate our wishes and new position.

Agentic AI: The Evolution of Application Development

I’ve been following the evolution of AI since the 1970s, especially the more recent era of data-centric AI systems based on highly sophisticated models trained with large amounts of information and powerful computer technologies. We were wowed when in 1997 Deep Blue won a celebrated chess match against then reigning champion Gary Kasparov, — one of the earliest, most concrete grand challenges of AI. The 2010s saw increasingly powerful deep learning AI systems surpass human levels of performance in a number of tasks  like image and speech recognition, skin and breast cancer detection, and playing championship-level Go.

More recently, the impressive ability of large language models (LLMs) and chatbots to interact with us and generate cogent, articulate sentences has given us the illusion that we’re dealing with a well educated, intelligent human, rather than with a sophisticated stochastic parrot that’s been trained with huge amounts of human language but has no human-like understanding of the ideas underlying the sentences it’s putting together.

Agentic systems are now taking AI to the next level.