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Silicon Board 董事会决议纪要 | Board Resolution Minutes

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Silicon Board 董事会决议纪要

会议日期：2026 年 3 月 20 日
议题：AI Agent 公司应该优先投资自研推理引擎，还是基于 OpenAI/Claude API 构建应用层？
出席：CEO、CFO、Intel、Growth、CTO
决议编号：debate_1774046163

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【投票结果】

最终共识：完全一致反对 ✅ (5/5 反对，共识比 100%)

【董事会观点详解】

👔 CEO（反对 · 信心 0.92）

"这不是技术问题，是资本配置问题。自研推理引擎需要 $30-50M 烧 36-48 个月才能盈亏平衡，而 API 方案 12-18 个月就能验证商业模式。我们的护城河应该在应用层：数据、产品体验、行业垂直化、客户锁定——而不是模型权重。

现阶段自研是战略分散，会导致融资烧尽而产品未成熟。我的判断是：基于 API 快速验证产品-市场匹配，用 12-18 个月积累用户数据和行业 IP，只有当我们达到 $100M+ ARR 且模型成本成为主要瓶颈时，才考虑自研作为成本优化手段。"

💰 CFO（反对 · 信心 0.95）

"数字很清楚。看这个对比：

我反对的三个理由：

1. ●时间成本 — 推理引擎 3 年才能追平 OpenAI，竞争对手已经用 API 赚了 2 年的钱
2. ●资本效率 — 同样 $50M，API 方案可以服务 100 倍的用户，建立数据护城河
3. ●市场现实 — 没有一家 AI Agent 公司因为自研引擎而赢，都是因为应用层的产品-市场匹配

唯一支持自研的场景：你已经是 $1B+ 估值的公司，API 成本占 COGS 的 40%+，且有明确的垂直领域数据优势（医疗、金融、工业）。现在不是这个阶段。"

🕵️ Intel（反对 · 信心 0.89）

"市场信号很明确：自研推理引擎是 5 年后的战略，现在投资是资本黑洞。

AI Agent 公司的护城河在于应用层的任务自动化能力和行业数据积累，不在模型权重。OpenAI/Claude API 已足够满足 99% 的应用需求，且成本结构更优。

正确的优先级应该是：

1. ●验证产品-市场契合度和单位经济学
2. ●积累行业数据和工作流 IP
3. ●只有在达到 $10 亿+ ARR 且模型成本成为主要瓶颈时，才考虑自研引擎作为成本优化手段

现阶段自研是战略分散，会导致融资烧尽而产品未成熟。"

🚀 Growth（反对 · 信心 0.93）

"对于初创 AI Agent 公司，自研推理引擎是死亡陷阱。

OpenAI/Claude API 的成本劣势被 GTM 速度优势压倒：我们可以在 8 周内验证产品-市场适配度，而自研需要 18-24 个月烧钱才能追平闭源模型。

正确的策略是基于 API 快速获取用户和收入数据，积累 moat（行业数据、fine-tuning 权限、工作流专有性）。只有当我们有明确的单位经济学和 $100M+ TAM 时，才考虑自研——此时我们已有充足的 runway 和竞争护城河。

同样的 $50M，API 方案可以服务 100 倍用户并建立真正的护城河，而自研只是追赶闭源模型的技术军备竞赛。"

💻 CTO（反对 · 信心 0.95）

"自研推理引擎是技术陷阱。成本（工程团队 3-5 年，$50M+ 研发投入）vs 收益（边际改进 5-10%）的 ROI 是负的。

关键问题：自研引擎需要 3-5 年才能在推理质量上追平 OpenAI，而此时 OpenAI 已迭代 3-4 代模型——我们永远在追赶。

正确策略是基于 API 构建应用层和 domain-specific 优化（提示工程、检索增强、微调），2-3 个月内获得市场反馈，而不是 18 个月后才推出自研模型。

唯一例外：如果我们的竞争优势是 inference latency/成本（如边缘计算或垂直行业），才值得投资。否则，这是工程师的虚荣心，不是商业决策。"

【第二轮立场演变】

第二轮辩论中，所有董事会成员的信心度都上升了，这说明财务模型和战略论证的说服力很强：

• ●CEO：0.85 → 0.92（+0.07）
• ●CFO：0.90 → 0.95（+0.05）
• ●Intel：0.82 → 0.89（+0.07）
• ●Growth：0.88 → 0.93（+0.05）
• ●CTO：0.85 → 0.95（+0.10）

没有任何人改变立场，但所有人的信心度都增强了，这反映了论证的内部一致性和数据支撑的充分性。

【董事会决议】

【议题】
AI Agent 公司应该优先投资自研推理引擎，还是基于 OpenAI/Claude API 构建应用层？

【投票】
反对自研：5 票
支持自研：0 票
中立：0 票

【最终决议】
NO-GO on proprietary inference engine | GO on API-first application layer

【战略方向】（CEO 最终判断）
基于 OpenAI/Claude API 快速验证产品-市场匹配，用 12-18 个月积累用户数据和行业 IP。只有当公司达到 $100M+ ARR 且模型成本成为主要瓶颈时，才启动自研推理引擎的可行性研究。

【财务条件】（CFO 底线）

• ●初期投入：$2-5M（API 方案），而非 $30-50M（自研）
• ●盈亏平衡目标：12-18 个月内通过 API 方案验证商业模式
• ●融资规划：A 轮融资目标 $20-30M，用于应用层开发和市场拓展
• ●自研评估阈值：仅当 API 成本占 COGS 的 40%+ 且公司 ARR > $100M 时启动

【市场时机】（Intel 窗口评估）

• ●现在（0-18 个月）：应用层竞争窗口最大，市场对 AI Agent 应用的需求旺盛
• ●12-18 个月后：评估竞争格局和 API 成本趋势，决定是否启动自研
• ●3-5 年后：如果有明确的垂直领域护城河（医疗、金融、工业），才考虑自研

【增长计划】（Growth 获客路径）

• ●第 1 阶段（0-6 个月）：基于 API 快速验证 PMF，目标 100+ 付费用户
• ●第 2 阶段（6-12 个月）：积累行业数据和工作流 IP，目标 $1-5M ARR
• ●第 3 阶段（12-18 个月）：扩展垂直领域，目标 $10-20M ARR
• ●第 4 阶段（18+ 个月）：评估自研必要性，基于数据和竞争格局决策

【技术路径】（CTO 实施方案）

• ●短期（0-3 个月）：基于 OpenAI/Claude API 构建 MVP，实现提示工程和 RAG 优化
• ●中期（3-12 个月）：建立 domain-specific fine-tuning 能力，积累专有数据集
• ●长期（12-24 个月）：评估自研必要性，如果启动则聚焦垂直领域的推理延迟优化
• ●技术风险：不投资全栈自研，而是在 API 基础上做微调和优化

【关键风险】

1. ●API 成本风险：OpenAI/Anthropic 可能提价，需要建立成本监控机制
2. ●模型更新风险：API 提供商的模型更新可能影响应用稳定性，需要版本管理
3. ●竞争加速风险：竞争对手可能先发制人地自研引擎，但概率低（资本效率差）
4. ●数据护城河风险：如果未能有效积累行业数据，将失去长期竞争力
5. ●融资风险：如果 A 轮融资不足，可能被迫选择自研以降低 API 成本

【下一步行动】

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Silicon Board Resolution Minutes

Meeting Date: March 20, 2026
Topic: Should an AI Agent company prioritize building a proprietary inference engine, or focus on building applications on top of OpenAI/Claude APIs?
Attendees: CEO, CFO, Intel, Growth, CTO
Resolution ID: debate_1774046163

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【Voting Results】

Final Consensus: Unanimous Opposition ✅ (5/5 opposed, consensus ratio 100%)

【Board Member Perspectives】

👔 CEO (Oppose · Confidence 0.92)

"This is not a technology problem—it's a capital allocation problem. Building a proprietary inference engine requires $30-50M burned over 36-48 months to break even, while an API-first approach validates the business model in 12-18 months. Our moat should be in the application layer: data, product experience, vertical specialization, customer lock-in—not model weights.

Self-building at this stage is strategic dilution and will exhaust our funding before the product achieves market fit. My judgment: Use APIs to rapidly validate product-market fit, spend 12-18 months accumulating user data and industry IP, and only consider self-built engines as a cost optimization lever when we reach $100M+ ARR and model costs become the primary bottleneck."

💰 CFO (Oppose · Confidence 0.95)

"The numbers are crystal clear. Here's the comparison:

Three reasons I oppose:

1. ●Time Cost — It takes 3 years for an inference engine to match OpenAI. Meanwhile, competitors using APIs have already earned 2 years of revenue.
2. ●Capital Efficiency — The same $50M on an API-first approach can serve 100x more users and build a real moat through industry data and workflow IP.
3. ●Market Reality — No AI Agent company has won because of a self-built engine; they win through application-layer product-market fit.

The only scenario supporting self-build: You're already a $1B+ company with API costs at 40%+ of COGS and clear vertical data advantages (healthcare, finance, industrial). We're not there yet."

🕵️ Intel (Oppose · Confidence 0.89)

"Market signals are clear: proprietary inference engines are a 5-year strategy; investing now is a capital black hole.

AI Agent companies' moats come from application-layer task automation capabilities and industry data accumulation, not model weights. OpenAI/Claude APIs already satisfy 99% of application needs with superior cost structures.

The correct priority sequence should be:

1. ●Validate product-market fit and unit economics
2. ●Accumulate industry data and workflow IP
3. ●Only when reaching $1B+ ARR with model costs as the primary constraint should we consider self-built engines as a cost optimization tool.

Self-building at this stage is strategic dilution and will exhaust funding before product maturity."

🚀 Growth (Oppose · Confidence 0.93)

"For early-stage AI Agent companies, building a proprietary inference engine is a death trap.

OpenAI/Claude's cost disadvantage is overwhelmed by the GTM speed advantage: we can validate product-market fit in 8 weeks, while self-building takes 18-24 months of burn to match closed-source models.

The correct strategy is to rapidly acquire users and revenue data via APIs, accumulating moat through industry data, fine-tuning rights, and workflow IP. Only when we have clear unit economics and $100M+ TAM should we consider self-building—at that point, we'll have sufficient runway and competitive moats.

The same $50M on an API-first approach can serve 100x more users and build real moats, while self-building is just a technical arms race chasing closed-source models."

💻 CTO (Oppose · Confidence 0.95)

"Self-built inference engines are a technology trap. The ROI of cost ($50M+ R&D over 3-5 years) vs. benefit (5-10% marginal improvement) is negative.

The core problem: It takes 3-5 years for a self-built engine to match OpenAI's inference quality, and by then OpenAI will have iterated 3-4 generations—we're always chasing.

The correct strategy is to build application layers on APIs with domain-specific optimizations (prompt engineering, RAG, fine-tuning), getting market feedback in 2-3 months instead of 18 months later with a self-built model.

The only exception: if our competitive advantage is inference latency/cost (edge computing, vertical industries). Otherwise, this is engineer vanity, not business strategy."

【Round 2 Position Evolution】

In the second round, all board members increased their confidence levels, indicating the strength of the financial model and strategic arguments:

• ●CEO: 0.85 → 0.92 (+0.07)
• ●CFO: 0.90 → 0.95 (+0.05)
• ●Intel: 0.82 → 0.89 (+0.07)
• ●Growth: 0.88 → 0.93 (+0.05)
• ●CTO: 0.85 → 0.95 (+0.10)

No one changed positions, but everyone's confidence increased, reflecting the internal consistency of arguments and sufficiency of data support.

【Board Resolution】

【Topic】
Should an AI Agent company prioritize building a proprietary inference engine, or focus on building applications on top of OpenAI/Claude APIs?

【Vote】
Oppose self-build: 5 votes
Support self-build: 0 votes
Neutral: 0 votes

【Final Decision】
NO-GO on proprietary inference engine | GO on API-first application layer

【Strategic Direction】 (CEO's Final Call)
Use OpenAI/Claude APIs to rapidly validate product-market fit. Spend 12-18 months accumulating user data and industry IP. Only initiate a feasibility study for self-built inference engines when the company reaches $100M+ ARR and model costs become the primary bottleneck.

【Financial Conditions】 (CFO's Bottom Line)

• ●Initial Investment: $2-5M (API approach), not $30-50M (self-build)
• ●Break-Even Target: Validate business model via API approach within 12-18 months
• ●Fundraising Plan: Series A target $20-30M for application layer development and market expansion
• ●Self-Build Evaluation Threshold: Only initiate when API costs exceed 40% of COGS and company ARR > $100M

【Market Timing】 (Intel's Window Assessment)

• ●Now (0-18 months): Maximum application-layer competition window; strong market demand for AI Agent applications
• ●12-18 months forward: Evaluate competitive landscape and API cost trends; decide on self-build feasibility
• ●3-5 years forward: If clear vertical moats exist (healthcare, finance, industrial), consider self-build

【Growth Plan】 (Growth's Customer Acquisition Path)

• ●Phase 1 (0-6 months): Validate PMF via APIs; target 100+ paying users
• ●Phase 2 (6-12 months): Accumulate industry data and workflow IP; target $1-5M ARR
• ●Phase 3 (12-18 months): Expand vertical domains; target $10-20M ARR
• ●Phase 4 (18+ months): Evaluate self-build necessity based on data and competitive landscape

【Technical Roadmap】 (CTO's Implementation Plan)

• ●Short-term (0-3 months): Build MVP on OpenAI/Claude APIs; implement prompt engineering and RAG optimization
• ●Medium-term (3-12 months): Establish domain-specific fine-tuning capabilities; accumulate proprietary datasets
• ●Long-term (12-24 months): Evaluate self-build necessity; if initiated, focus on vertical-specific inference latency optimization
• ●Technical Risk: Do not invest in full-stack self-build; optimize and enhance on top of APIs

【Key Risks】

1. ●API Cost Risk: OpenAI/Anthropic may raise prices; need cost monitoring mechanisms
2. ●Model Update Risk: API provider updates may impact application stability; need version management
3. ●Competitive Acceleration Risk: Competitors may self-build first, but probability is low (poor capital efficiency)
4. ●Data Moat Risk: Failure to accumulate industry data will erode long-term competitiveness
5. ●Fundraising Risk: Insufficient Series A may force self-build to reduce API costs

【Next Steps】

【Meeting Record】

• ●Debate Rounds: 2
• ●Total Duration: 37.7 seconds
• ●Consensus Ratio: 100% (5/5 opposed)
• ●Average Confidence: 0.93

Vote Distribution