Gartner Research on Emerging Tech: Navigating the Risks and Opportunities of Synthetic Data for AI Solutions
Can Synthetic Training Data Survive Its Own Regulation?
Synthetic training data has recently emerged as the quick fix to two different problems: the lack of high-quality training data, and the need to avoid using personal data in training to stay within the boundaries of privacy legislation. Gartner predicts that by 2026 75% of all enterprises will use generative AI to create synthetic training data, a huge increase from less than 5% in 2023.1 Moreover, Gartner predicts that by 2035, solutions that use synthetic data for training will grab three quarters of all end-user spending on enterprise AI software.2 Training data that only a few years ago was the subject of several research papers is now carrying the weight of huge enterprise systems.
Where does the rigor go? Research software and the future of trustworthy science.
Generative AI now makes it dramatically easier to produce something that looks like research: analysis code, figures, literature reviews, even whole pap…
AI is Creating Peak Software, Media is the Best Analogy
Let's learn more about the world's most important manufactured product. Meaningful insight, timely analysis, and an occasional investment idea.
In spite of hype, many companies are moving cautiously when it comes to generative AI | TechCrunch
Companies are extremely interested in generative AI as vendors push potential benefits, but turning that desire from a proof of concept into a working product is proving much more challenging.
70 years of AI hype
Quoting from Olivia Guest et al. (2025) "Against the Uncritical Adoption of AI Technologies in Academia."
Synthetic Data Statistics 2026: Market Size, Adoption Rates & Industry Trends - Axis Intelligence
Synthetic data market hits ~$750M in 2026. We tracked CAGR, adoption rates, regional share, and industry breakdowns across 10+ primary sources. Synthetic Data Statistics 2026 Update.
“Wait, not like that”: Free and open access in the age of generative AI
The real threat isn’t AI using open knowledge — it’s AI companies killing the projects that make knowledge free
A Short Guide to Data Strikes and Conscious Data Contribution in the Context of 2026 Frontier AI
Back to the basics of data leverage.
Firms like Meta and A16z admit having to pay billions for training data would ruin their generative-AI plans as they fight new copyright rules
Meta, Google, Microsoft, and Andreessen Horowitz are trying to keep AI developers from having to pay for copyrighted material used in AI training.
The Age of Wonders and Terrors
Twenty years ago, when the idea of AI taking over the world in our lifetimes still struck most of us as the unconstrained fantasy of those who knew too much science fiction and too little science, …
A Collectivist, Economic Perspective on AI
Information technology is in the midst of a revolution in which omnipresent data collection and machine learning are impacting the human world as never before. The word ``intelligence'' is being...
Measuring the Impact of Early-2025 AI on Experienced Open-Source...
Despite widespread adoption, the impact of AI tools on software development in the wild remains understudied. We conduct a randomized controlled trial (RCT) to understand how AI tools at the...
Deep Research, information vs. insight, and the nature of science
What AI will accelerate in the scientific process, what it cannot do, and how we can prepare for new manners of scientific investigation.
How we use AI at Stalwart
It is difficult to have a conversation about software in 2026 without AI showing up in it. Two years ago the interesting question was…
Microsoft’s AI Red Team Has Already Made the Case for Itself
Since 2018, a dedicated team within Microsoft has attacked machine learning systems to make them safer. But with the public release of new generative AI tools, the field is already evolving.
Inside the AI Index: 12 Takeaways from the 2026 Report | Stanford HAI
The annual report reveals a field hitting breakthrough capabilities while raising urgent questions about environmental costs, transparency, and who benefits from the technology.
