Harvey's Bold Move: Why AI Startups Are Embracing Multi-Model Strategies

When AI Startups Choose Multiple Models: What Harvey’s Move Means for the Future of AI Competition

By Chandrakumar Pillai

AI is evolving fast — and so is the way businesses choose their AI partners. In a surprising yet strategically important move, Harvey, the leading legal AI startup, has decided to expand its use of foundation models beyond OpenAI, bringing Anthropic and Google into its ecosystem.

This is not just a simple technology update. It signals a major shift in how ambitious AI-first startups approach model sourcing, partnerships, and vendor competition.

Let’s break this down in simple terms, discuss why this move matters for the industry, and explore what lessons leaders can draw from this evolving AI landscape.

Harvey’s Big Move: From Exclusive OpenAI to Multi-Model Strategy

Harvey has been a poster child for OpenAI’s Startup Fund.

• It was one of the first four startups backed by the fund.

• It has grown into a $3 billion valuation company, raising major rounds from Sequoia, Coatue, Kleiner Perkins, and even the OpenAI Startup Fund itself.

• Harvey offers an AI legal assistant that supports lawyers in legal drafting, research, and more.

Until now, Harvey’s core products relied mostly on OpenAI’s models. But things have changed.

In its recent blog post, Harvey announced:

• It is now also using Anthropic’s Claude and Google’s Gemini models, via Amazon’s cloud.

• It is not abandoning OpenAI, but expanding its options.

• Its internal testing shows that different models perform better on different legal tasks.

This is a strategic evolution — from relying on a single AI model vendor to embracing a multi-model approach tailored to specific tasks.

Why Did Harvey Make This Move?

According to Harvey:

• Its internal benchmark, BigLaw Bench, revealed that seven models now outperform its previous systems in certain legal tasks.

• Different models shine at different tasks:

Instead of investing heavily in training its own models, Harvey decided it is more efficient to integrate the best-performing models from various vendors and fine-tune them for legal use cases.

This move is practical, cost-effective, and future-ready. It lets Harvey focus on creating specialized legal AI agents, while letting the model vendors compete and innovate on core capabilities.

The Bigger Message: AI Is Becoming Multi-Vendor, Multi-Cloud, Multi-Model

Harvey’s shift reflects a larger trend emerging across the AI industry:

• Gone are the days when AI startups locked themselves into a single vendor’s ecosystem.

• More businesses are choosing to work with multiple models, clouds, and vendors simultaneously.

• This provides them with:

Harvey is not only embracing this model diversity, but also adding pressure on its vendors (including OpenAI, Google, and Anthropic) to continuously prove themselves via public benchmarking and transparent reporting.

In its blog, Harvey announced plans to:

• Publish a public leaderboard of model performance on legal tasks.

• Go beyond single-number rankings by sharing expert legal insights on how different models perform in nuanced scenarios.

This is good news for transparency and healthy competition in AI. It also puts customer needs — not vendor loyalty — at the center of product strategy.

Key Lessons for AI Leaders and Business Decision-Makers

✅ Flexibility Wins Over Exclusivity: Harvey’s move proves that even early-backed startups are no longer afraid to work with competitors of their own investors. It’s all about performance, not politics.

✅ Vendor Lock-In Is Yesterday’s Strategy: Businesses must prepare for a multi-cloud, multi-model future, where the best combination wins — and loyalty is earned continuously.

✅ Specialization Is the New Differentiator: Instead of building generic models, Harvey focuses on fine-tuning existing models for specific legal use cases. This saves resources while delivering high-impact solutions for customers.

✅ Benchmarks and Transparency Are the New Trust Builders: Customers increasingly expect clear, transparent benchmarks and real-world testing results — not just vendor claims. Harvey’s open leaderboard and detailed reports are a step in this direction.

Critical Questions for Reflection and Discussion

Let’s pause and reflect.

These developments raise essential questions that deserve discussion across professional circles:

✅ Will more AI startups adopt a multi-model strategy like Harvey, or will some continue to bet exclusively on one vendor?

✅ Should investors encourage their portfolio companies to embrace vendor diversity, even if it means collaborating with competitors?

✅ Will transparent, customer-centered benchmarking become the industry norm? Or will AI leaders resist openness in favor of controlled narratives?

✅ What new skills and tools will enterprises need to manage, evaluate, and switch between multiple AI models and clouds?

✅ Will this shift push AI model vendors to become more open, modular, and accountable?

The Future of AI Is Competitive, Customer-Centric, and Open

Harvey’s move is a wake-up call to the entire AI industry.

It shows that:

• AI model performance is no longer monopolized by a few companies. New players like Anthropic, alongside giants like Google and OpenAI, are pushing each other to improve.

• AI startups are becoming more strategic and demanding in their vendor choices. They expect more than just powerful models — they expect task-specific excellence, transparency, and partnership flexibility.

• The era of “one-model-fits-all” is fading. We are entering a future where successful AI businesses will skillfully integrate multiple models, clouds, and vendors to create value for their customers.

This is not just a technical evolution — it’s a shift in mindset.

AI is becoming more like electricity — businesses will tap into the best available grid, wherever it comes from, and fine-tune it for their own use.

Final Thoughts

Harvey’s decision to embrace models from Anthropic and Google — while still working with OpenAI — is more than just a tech upgrade. It reflects a mature, customer-first strategy that focuses on performance, transparency, and flexibility.

For all businesses, especially those in sectors where AI is mission-critical (like law, finance, healthcare), this is a powerful lesson:

Don’t get locked in. Benchmark everything. Prioritize real-world results over vendor relationships. And always keep your customer’s needs at the center of your AI strategy.

Let’s Discuss 👇

• What are your thoughts on this shift?

• Are we seeing the beginning of a multi-model, open AI era?

• How can businesses prepare for managing multiple models, vendors, and clouds efficiently?

• Have you seen other startups or enterprises adopting this approach successfully?

Share your insights and stories in the comments.

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Reference: Tech Crunch