---
title: "Unique Friend | Baihai Tech’s Lu Yilei: Every Industry Is Being Reshaped by Foundation Models"
author: "Unique Research"
sourcePublication: "Unique Research (FFCAP English library)"
originalPublishedAt: "2024-01-17T15:00:00+08:00"
canonical: "https://ffcap.cn/en/research/20240117a057qy00"
source: "https://ffcap.cn/en/research/20240117a057qy00"
language: "en"
cnSourceUrl: "https://view.inews.qq.com/a/20240117A057QY00"
---

# Unique Friend | Baihai Tech’s Lu Yilei: Every Industry Is Being Reshaped by Foundation Models

_Unique Research · Insight / Unique Friend Q&A · January 17, 2024 · Guest: Lu Yilei, founder & CEO, Baihai Tech (白海科技)_

Inventory type remains **insight** (Unique Friend series — not partner welcome).

## Guest

**Lu Yilei** — founder & CEO, Baihai Tech. Standing member, CCF Big Data Committee; member, CCF High-Performance Committee; Beihang distinguished professor; chair, China Big Data Tech Conference; 2017 China Big Data Industry Leader. 15+ years in big-data and AI compute platforms; led HPC and AI base platforms at Baidu, Lenovo, Minglue.

**Baihai Tech** is a next-gen cloud-native AI base-software vendor helping developers and enterprises cut AI landing friction on efficiency, performance, and cost. Core product: training+inference AI cloud platform **IDP (Intelligent Development Platform)** — full-lifecycle tools for AI / LLM development plus a heterogeneous resource scheduler. Patents, software copyrights, and top-conference papers in core areas.

## Q&A

**1\. Product and core scenes?**  
Core: AI training+inference cloud with three modules. **IDP Studio** — one-stop AI platform (data → model develop/train/manage/serve) with a self-built AI IDE for controllable environments. **IDP LM** — zero-code LLM fine-tune builder (preprocess → fine-tune → deploy). **IDP Engine** — AI resource scheduler for heterogeneous clusters and higher GPU utilization. Live in intelligent-compute centers, energy, mining, media/internet, universities — Fortune 500 and sector leaders. Scenes: energy AI middle platforms, industrial AI base platforms, university LLM labs, media DMaaS. App layer: multimodal marketing agent **IMC-GPT** (posters, slogans, campaign planning).

**2\. R&D / GTM — 2023 challenge and win?**  
From two years of AI base-platform work, quickly shipped an LLM fine-tune platform. For large and SMB customers: standardized solutions plus tailored commercial models; cloud service for SMBs needing flexible, low-cost compute. Challenges: 2023 macro (common to B2B) and the pace of AI change. Challenge and harvest are two sides — early tools and landings in the LLM wave; biggest win is team growth and fast product innovation.

**3\. Opportunities vs. past waves?**  
Consensus: every industry will be reshaped by foundation models — huge room (legal / investment-decision assistants; auto coding; AI translation). Two differences vs. past: (1) **democratization of innovation** — non-technical founders can build complex AI apps; (2) **scene diversity** — general-purpose models let founders solve real problems across many domains.

**4\. Challenges landing AI in China?**  
Baihai’s job is enabling enterprise AI. Clients face **high bar** and **uncertainty**. High bar: costly compute (hard to get and utilize); hard fine-tuning on owned data; weak engineering from lab to production; long build cycles (compute + platform from scratch). Uncertainty: unclear business value / ROI of new LLM tech — Baihai helps find scenes where models actually show “magic.”

**5\. Keys for product and GTM?**  
(1) **Position** — real user problems + own strengths; startups cannot do everything. (2) **Speed** — models change daily; use startup flexibility. (3) **Ecosystem** — B-side customers want full solutions; partner for complement.

**6\. Competing with giants?**  
Focus a domain — don’t fight giants across the board. Use speed and flexibility. Partner up/downstream for complete solutions. (CN item “3” skipped in source numbering.)

**7\. Are LLMs still too weak?**  
Challenges remain: depth of understanding/generation, reasoning, bias/ethics. Remember they are still _language_ models — strongest on text. Don’t mythologize. Plenty to explore around strengths: internal knowledge bases, writing assistants, customer service.

**8\. Human–machine collaboration?**  
Aim: AI augments humans on clear tasks for efficiency and innovation. Enterprises should map workflows to find best AI insertion points, and invest in high-quality data — the foundation of LLMs. Data collection/processing is often the slow step; digitize and prepare data early.

**9\. 2024 tech to watch?**  
From a landing view, 2024 is year one of the LLM _tooling_ layer — LMOps, Agent tools, etc. 2023 was base-model year; after a year of exploration, value is clearer. 2024: tooling for model landing blooms.

_1 CN image; attach on ingest. Signup CTA in CN omitted._

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Editorial English edition published on UniqueCapital: https://ffcap.cn/en/research/20240117a057qy00
On-site reading page: https://ffcap.cn/en/research/20240117a057qy00