Oshen Davidson

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Oshen Davidson
Get to know me

AI Researcher

Hi! I'm Oshen

I’m an applied AI researcher with a background in search, information retrieval, and LLM evaluations, and I’m currently working toward a transition into AI safety. I’ve spent the last year focused on understanding how people use AI as search engines, and how language models retrieve information, use context, generate responses, and influence how brands show up in AI search systems.

I’ve always been drawn to questions that sit just outside what I already know. Growing up as the internet became woven into everyday life made it hard not to notice how deeply technology influences culture, communication, and the way people understand the world.

The more time I spend studying AI, the more questions I have. I’ve become more interested in understanding what a model’s behavior reveals about the system itself.

As advanced AI systems become more capable and autonomous, the consequences of behavior we cannot explain or reliably predict become harder to ignore. There’s still a lot of work to be done to understand what model behavior can tell us about risk, what is happening internally, and how that evidence should shape the way these systems are evaluated, safeguarded, and deployed.

More about me →

My Thoughts on AI

AI has already proven useful in everyday life, and I believe it could help address some of society’s hardest problems. But the future it creates is not a predictable path toward either progress or catastrophe. The outcomes will depend on what these systems become capable of, how they are built, deployed, and regulated and whether our ability to understand these risks can keep up.

Right now, capabilities are advancing faster than our ability to understand what is happening inside these systems, creating a significant gap in our ability to predict how advanced AI will behave outside controlled environments or when given greater autonomy. Understanding that gap is important, but it is only one part of the work ahead.

As a society, we need all hands on deck to prepare for risks that may be difficult to predict or reverse. That means identifying dangerous behavior early, strengthening safeguards and defenses, developing effective regulations, creating institutions capable of governing advanced AI, and building response plans for when prevention fails. It also means improving public understanding and turning what we learn about AI risk into coordinated preparation and action. There are still not enough people working on these questions, while many more remain unaware of how quickly the capabilities and risks are changing or what is at stake.

I firmly believe humanity will not be able to control advanced AI. We may be able to slow its development, limit access, build safeguards, and reduce the likelihood of harm, but we cannot assume every failure will be predictable or containable. For me, the most important goal is preventing a future in which AI disempowers humanity and weakens our ability to shape society’s direction. That disempowerment could take many forms, including giving powerful actors or hostile states new ways to undermine democratic institutions, concentrate power, destabilize societies, or contribute to wider societal collapse.

The work we do now may determine not only what a future with AI looks like, but whether there is a safe and viable future for humanity at all. I see AI safety as a shared societal responsibility, one that needs people from every field working to preserve humanity’s ability to shape its own future.

If reading this leaves you feeling overwhelmed, just know that you’re not alone. If you want to better understand the risks, learn what AI safety actually means, or figure out how you can contribute, I'd love to chat with you.

Specimen 001 · citation behavior
Citation Selection in Commercial AI Search: A Stage-Based Analysis of Retrieved Pages
Collected Mar 2026
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Specimen 002 · citation behavior
From Search to Citation: Measuring How ChatGPT Retrieves and Selects Sources
Collected Mar 2026
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Specimen 003 · ugc
Community Sources in AI Search: A Cross-Model Analysis of 5.5 Million Answers
Collected Nov 2025
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As Featured In

  • Zyppy
  • Search Engine Land
  • MSN
  • eMarketer
  • MSN Deutschland
  • Forrester