---
title: "Roundtable | Silicon Valley AI Founders on Finding PMF for Foundation-Model Startups"
author: "Unique Research"
sourcePublication: "Unique Research (FFCAP English library)"
originalPublishedAt: "2024-11-14T16:00:00+08:00"
canonical: "https://ffcap.cn/en/research/20241114a05yyo00"
source: "https://ffcap.cn/en/research/20241114a05yyo00"
language: "en"
cnSourceUrl: "https://view.inews.qq.com/a/20241114A05YYO00"
---

# Roundtable | Silicon Valley AI Founders on Finding PMF for Foundation-Model Startups

_Unique Research · Interview · November 14, 2024_

Source: HubSpot 2024 GenAI Summit roundtable. Unique Research editorial compilation. Moderator: **Charles Elfin** (Fellows Fund partner; Ficc.AI founder).

## Guests

-   **Young Zhao** — OpusClip co-founder & CEO. One-click long→short viral video. Sep: ~$30M raised (A + seed); A led by Millennium New Horizons + AI Grant, Samsung Next, GTM Fund, DCM, Alumni, Fellows Fund, Alpine, Jason Lemkin. 6M+ users; eight-figure ARR; clients incl. Billboard.com, Univision, Telefónica, Jenny Hoyos, Scott Galloway.
-   **Jia Li** — LiveX AI co-founder, Chief AI Officer & President. Human-like VIP AI agents across the customer journey (sales, trial conversion, renewals/upsells, fewer returns).
-   **Dmytro Dzhulgakov** — Fireworks AI co-founder & CTO. Inference + fine-tune platform for open GenAI models. Traffic ×100 in six months; Jul Series B led by Sequoia → **$552M** valuation. (Ex-PyTorch core.)
-   **Hassan Raza** — Tavus co-founder & CEO. GenAI video research; Mar Series A **$18M** led by Scale VP (Rory O’Driscoll joins board); Sequoia, YC, HubSpot. Personal digital twins, lip-sync, dubbing, text→video; web + API; authorized clones only.

## Q: How do you use AI — and what’s your founding story?

**Young:** OpusClip turns long footage into social-ready shorts with video understanding + generation so anyone can tell stories on social.

**Jia:** Multimodal AI agents for daily life / enterprise customer service — GenAI will change how we interact with products, services, hardware.

**Dmytro:** Developer platform for open GenAI (Llama, Mistral, Stable Diffusion, Whisper…) — customize, deploy, scale, optimize for quality and low latency; tops performance leaderboards.

**Hassan:** Models for personal digital avatars — text-only video without filming; healthcare partners send doctor messages in patients’ languages; even Zoom stand-ins. Consent-first, high-fidelity cloning at scale.

## Q: Selling vision to investors, employees, customers — which is hardest?

**Hassan:** Customers hardest — investors buy a dream; customers need the dream rooted in today’s product and their business. Win by partnering so their roadmap feels like yours.

**Jia:** Customers often non-technical vs. AI-fluent investors — listen to pain; alignment follows solving real problems.

**Young:** Customers easy (KOL creators often articulate the vision better than he does). Employees hardest — no precedent product, constant mission reinforcement. Investors also hard — many aren’t founders and don’t live social video.

**Dmytro:** Customer focus first; for developer products, craft in details matters; also reframe the customer’s business problem, not only the tech.

## Q: Niche differentiation vs. huge market?

**Dmytro:** GenAI market dwarfs prior deep learning; focus on customer problems over competitors — best product wins.

**Jia:** CX / agents are noisy; demos are ~5% of the work, production ~95% — educate, earn trust.

**Hassan:** Shifted from sales/marketing product models to developer-facing models — broader distribution beats locking models inside one product.

**Young:** Started niche, became early category leader; expanding ICP carefully — want 3–10× market but pace expansion with limited team.

## Q: Real-world ethics?

**Hassan:** Disclosure & consent for face/voice clones; refuse unauthorized celebrity virality even when marketing wants it.

**Dmytro:** Huge gap between training a model and shipping a product — guardrails, safety filters, privacy (log only when customer needs fine-tuning).

**Young:** Millions of copyright issues early (users clipping others’ content looking like OpusClip); warn/ban; make abuse harder while smoothing UX for legitimate creators.

**Jia:** Job-replacement fear — experience shows AI lifts agents off repetitive work toward harder cases; transformation not pure replacement.

## Q: Biggest production challenge — reliability, hallucination, prompts, guardrails…?

**Dmytro:** All of the above. Early: iterate fast (prompting > fine-tune speed). Later: cost, latency, TCO — specialized deploys can × performance without changing the model (e.g. phone assistants need ultra-low latency on first tokens).

**Jia:** Latency for real-time multimodal agents (vs. batch report agents). NVIDIA HW/SW work → >6× avg token speed — still not enough for edge dreams.

**Hassan:** Research→prod needs heavy testing for weird real inputs; then scale/latency/cost dominate.

## Q: How important is interpretability?

**Young:** Visual video outputs are inherently inspectable.

**Hassan:** Critical for B2B embeds — predictability + knowing why a training video was rejected.

**Dmytro:** Importance varies by product criticality.

**Jia:** Customers care less about parameters than journey insights — hot topics, frequent questions that drive product upgrades.

## Q: Future of foundation models?

Consensus: **multimodal**. Hassan: modalities preserve context lost in text-only translation. Dmytro: spatial reasoning / robotics / sensors next. Young: models still don’t match human world models — big improvement area. Jia: multimodal agent future matches LiveX’s bet.

## Q: Are transformers the ceiling of intelligence?

**Dmytro:** Transformers dominate partly because of HW/SW investment — hard to displace even if a new idea is slightly better. **Hassan:** agrees (non-expert). **Jia:** data under-discussed vs. architecture/scale — data encodes the problem you’re solving. **Young:** agrees models still miss true human world perception.

— END —

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Editorial English edition published on UniqueCapital: https://ffcap.cn/en/research/20241114a05yyo00
On-site reading page: https://ffcap.cn/en/research/20241114a05yyo00