StarZero All shows Try the podcast clipper →

Milos Rusic

CEO & Co-Founder, deepset AI · 1 appearance on the record.

computed by AI from the episodes · how this works → · full disclaimer →

13statements → 2claims → 1claims resolved → 3.62/5average certainty → 2.08/5average debate potential → 4.1/5argument clarity · the sources →

1 supported 0 partly supported 0 contradicted 1 not checkable as stated how the 2 claims stand · each chip opens the sources

2 predictions · 3 opinions · 6 insights · 2 disclosures · every statement was checked. The predictions and assertions are the 2 claims: statements the public record can support or contradict. 1 is resolved, and 1 names 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 Milos argues and how the claims held up. Everything they said, and everything said about them, is in the tabs below.

Their most notable supported claim

Prediction Held up
Airbus single-pilot AI search application will take a few more years
“For Airbus, the case is it's something they are working on. It's probably something that will take a few more years because it's all embedded in this overall vision of single pilot operations.”
Milos Rusic Sep 6, 2023 ▶ 36:30 From NLP Start-Up to Generative-AI Platform: Milos Rusic (deepset) Unpacks Product-Market Fit

Argument clarity: do they answer the question? how? →

4.1 / 5 directness 4.4 · coherence 4.2 · precision 3.9 · compression 3.3

answered every one of 9 assessed questions directly

This is a score against a rubric. It is not a rank. Every host question → answer exchange is scored with names hidden on directness, coherence, precision and compression, 1–5 each, on meaning alone: disfluencies are ignored, and only raw unedited episodes count. This is the score that measures thought. Every scored exchange, scores shown → · The rubric and its checks →

How they sound: speaking style how? →

240 words/min while actually speaking · 39.4 um and uh per 1k words

Measured by listening to the audio itself: 6,377 words across 1 episode of raw-level tape, transcribed verbatim with every um and uh kept, each one attributed only where the alignment onto our timed stream is unambiguous. These are measurements of speaking style. We do not rank them: across this corpus, fluency and argument quality are nearly uncorrelated (ρ≈0.2), and smooth talking does not signal clear thinking. How it's measured →

Everything Milos Rusic said on the MAD Podcast that made the record, most notable first. Filter by type, assessment or year in the ledger →

Opinion
The only meaningful way to adopt NLP and LLMs is cloud-based
“The only meaningful way to adopt to adopt NLP and LLMs is in the cloud”
Milos Rusic Sep 6, 2023 ▶ 7:46 From NLP Start-Up to Generative-AI Platform: Milos Rusic (deepset) Unpacks Product-Market Fit
Insight
AI startups prioritize rapid shipping over accuracy and factfulness
“If you care so much around about the factfulness of your applications, you're probably not a startup, right? Startups, you know, you want to ship fast. You do whatever works best. You assemble something, it's shipped.”
Milos Rusic Sep 6, 2023 ▶ 29:17 From NLP Start-Up to Generative-AI Platform: Milos Rusic (deepset) Unpacks Product-Market Fit
Opinion
Europe's conservative enterprise tech adoption limits local startup growth ceilings
“Europe is that simply, you know, a bit more conservative in the way they adopt new technologies, right? And this is this is usually then a bit harder for these companies to, you know, to grow in their home markets, right?”
Milos Rusic Sep 6, 2023 ▶ 42:59 From NLP Start-Up to Generative-AI Platform: Milos Rusic (deepset) Unpacks Product-Market Fit
Insight
RAG systems need dedicated hallucination detectors to verify LLM outputs
“If you want to make sure that there's not a, that the model doesn't hallucinate, you probably want a hallucination detector on top, right? Something that classifies an answer and confirms, is this answer really part of my database?”
Milos Rusic Sep 6, 2023 ▶ 12:35 From NLP Start-Up to Generative-AI Platform: Milos Rusic (deepset) Unpacks Product-Market Fit
Insight
Model transparency and research drive enterprise AI adoption
“More transparency we can create also around, you know, how models have been trained, You know, the more research we have around the model, of course, the more trust it creates and easier it is for companies to adopt.”
Milos Rusic Sep 6, 2023 ▶ 14:40 From NLP Start-Up to Generative-AI Platform: Milos Rusic (deepset) Unpacks Product-Market Fit
Insight
Maturing database products differentiate, making integrations harder to maintain
“All these products are maturing and also differentiating, right? So it is becoming more and more of an effort, to be honest, to really manage this.”
Milos Rusic Sep 6, 2023 ▶ 15:49 From NLP Start-Up to Generative-AI Platform: Milos Rusic (deepset) Unpacks Product-Market Fit
Insight
Consumption is the best metric to reflect LLM system value
“Is probably the best metric to somehow reflect the value that comes out of these LLM systems, right? Because in the end, the more requests a model gets, or application gets, probably the more valuable it is. The more data you load into it, probably the more va…”
Milos Rusic Sep 6, 2023 ▶ 27:13 From NLP Start-Up to Generative-AI Platform: Milos Rusic (deepset) Unpacks Product-Market Fit
Disclosure
Deepset's core LLM tech stack will always remain open source
“Everything that you need to really have an LL application, like technically the full tech stack, that will always be open source.”
Milos Rusic Sep 6, 2023 ▶ 29:50 From NLP Start-Up to Generative-AI Platform: Milos Rusic (deepset) Unpacks Product-Market Fit
Insight
Enterprise GTM requires selling directly to use-case product owners
“To sell into the enterprise, what you need is you really need a use case, right? And you really need to have clarity about the use case. And the best person to sell to is the person that is responsible for serving a use case. And this is a persona that we refe…”
Milos Rusic Sep 6, 2023 ▶ 32:15 From NLP Start-Up to Generative-AI Platform: Milos Rusic (deepset) Unpacks Product-Market Fit
Prediction Not checkable as stated
RAG will be the first LLM architecture to see mainstream enterprise adoption
“We see that this whole RAC architecture is probably the first one that will really see a broad, massive adoption, right? Simply because it's all about accessing and, you know, somehow working with information that is already around. And this is where we see, l…”
Milos Rusic Sep 6, 2023 ▶ 34:04 From NLP Start-Up to Generative-AI Platform: Milos Rusic (deepset) Unpacks Product-Market Fit
Prediction Held up
Airbus single-pilot AI search application will take a few more years
“For Airbus, the case is it's something they are working on. It's probably something that will take a few more years because it's all embedded in this overall vision of single pilot operations.”
Milos Rusic Sep 6, 2023 ▶ 36:30 From NLP Start-Up to Generative-AI Platform: Milos Rusic (deepset) Unpacks Product-Market Fit
Opinion
Rusic highlights London-based LLM observability startup Context
“One company I like is, for example, Context from London.”
Milos Rusic Sep 6, 2023 ▶ 41:51 From NLP Start-Up to Generative-AI Platform: Milos Rusic (deepset) Unpacks Product-Market Fit
Disclosure
Deepset's early consulting clients included Airbus and European federal authorities
“We worked for companies like Airbus many federal authorities in Europe software providers in Germany.”
Milos Rusic Sep 6, 2023 ▶ 3:51 From NLP Start-Up to Generative-AI Platform: Milos Rusic (deepset) Unpacks Product-Market Fit

Appearances (1)

EpisodeDateSpeaking time
From NLP Start-Up to Generative-AI Platform: Milos Rusic (deepset) Unpacks Product-Market Sep 6, 2023 32m
Made with StarZero

Turn any episode into a week of clips.

This entire site, over 400 conversations transcribed, diarized, checked and made playable, runs on the StarZero media pipeline. Drop in your own episode and the podcast clipper finds the moments worth sharing, cuts them, captions them, and reframes them for every feed.

Try the podcast clipper Learn more