Raaz Dwivedi

Co-founder, Traversal · 1 appearance on the record.

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founderacademicscientistraazdwivedi.github.io ↗

Raaz Dwivedi is a machine learning researcher whose work focuses on causal inference, distribution compression, and reinforcement learning. He co-founded Traversal, an AI startup developing an autonomous AI Site Reliability Engineer to troubleshoot enterprise outages.

6statements → 2claims → 0claims resolved → 3.5/5average certainty → 2.67/5average debate potential →

2 not checkable as stated how the 2 claims stand · each chip opens the sources

2 predictions · 2 opinions · 2 insights · every statement was checked. The predictions and assertions are the 2 claims: statements the public record can support or contradict. 0 are resolved, and 2 name no date, number or outcome precise enough to check. Everything else (opinions, insights, what ifs, disclosures) can never be settled by the record, so it carries no assessment.

The record, in short

What the tape says about how Raaz argues and how the claims held up. Everything they said, and everything said about them, is in the tabs below.

How they sound: not measured why? →

We measure speaking style by listening to the audio itself, and a fair number needs at least 2,000 words from one person on tape we have measured. There is too little of Raaz Dwivedi on measured tape to publish a rate. This says nothing about how they speak.

Everything Raaz Dwivedi said on Latent Space that made the record, most notable first. Filter by type, assessment or year in the ledger →

Opinion
Dwivedi: Claude is superior at agentic tool calling and error unstacking
“For some of the agentic part of the stack, we are shifting towards Anthropic because they're agentic and the tool calling, especially the unstacking part, you know, when you go down the wrong path and you build context that forces you to keep going down the wr…”
Raaz Dwivedi Oct 5, 2025 ▶ 33:36 ⚡️Traversal: Causal ML and Reinforcement Learning
Prediction Not checkable as stated
Dwivedi: AI self-healing for complex incidents is 6-12 months away
“Now for, then there is this level of 30 to 40% of the incidents or issues where you need to involve you know, a senior engineer for sanity checking. I think that healing will appear in, I don't know, six months to a year that will be comfortably, the technolog…”
Raaz Dwivedi Oct 5, 2025 ▶ 39:34 ⚡️Traversal: Causal ML and Reinforcement Learning
Opinion
Dwivedi: Deterministic workflows cannot solve complex enterprise incident debugging
“No amount of workflows will suffice for a big enterprise. Like you have to link together some of the missing pieces, some of the poorly instrumented data, and that requires world knowledge and a few iterations with the world knowledge.”
Raaz Dwivedi Oct 5, 2025 ▶ 32:39 ⚡️Traversal: Causal ML and Reinforcement Learning
Prediction Not checkable as stated
Dwivedi: AI agents that reorganize whole codebases are years away
“That kind of a full agentic system that, you know, just reorganizes the whole code base. I would say, well, AI always keeps surprising you, but at least a couple of years away, if not more, where you can then rely on creating the code base.”
Raaz Dwivedi Oct 5, 2025 ▶ 40:08 ⚡️Traversal: Causal ML and Reinforcement Learning
Insight
Dwivedi: LLMs handle semantics while statistics must handle time series
“The agent is not good at looking at time series data, so that's, that is what statistics needs to take care of. But statistics doesn't understand what is the relationship between latency and memory usage and disk utilization. So that is the LLM part.”
Raaz Dwivedi Oct 5, 2025 ▶ 19:45 ⚡️Traversal: Causal ML and Reinforcement Learning
Insight
Dwivedi: Enterprise AI conviction requires production testing, not staging demos
“Everybody, the reason nobody gets convinced on staging environment is that everybody thinks that their environment is unique, right? Like it's the most complex thing on earth. So they're like, can your AI actually figure out the complexities of my system? Alth…”
Raaz Dwivedi Oct 5, 2025 ▶ 43:51 ⚡️Traversal: Causal ML and Reinforcement Learning

Appearances (1)

EpisodeDateSpeaking time
⚡️Traversal: Causal ML and Reinforcement Learning Oct 5, 2025 13m
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