Why AI Will Ultimately Surpass McKinsey—But Not Right Away

Navin Chaddha, Managing Director at Mayfield—a venture capital firm boasting a remarkable 55-year presence in Silicon Valley—predicts that AI will significantly transform industries reliant on human labor, such as consulting, legal, and accounting services. At the TechCrunch’s StrictlyVC event in Menlo Park, he discussed his insights, highlighting investments in groundbreaking companies like Lyft, Poshmark, and HashiCorp. Chaddha noted that “AI teammates” could match the profit margins typically associated with software in high-cost sectors, urging startups to target overlooked markets instead of going head-to-head with giants like Accenture. He stressed the challenges of disrupting industries built on trust and relationships, simplifying the discussion for clarity.

You suggest that law, consulting, and accounting firms—a staggering $5 trillion industry—are on the brink of a major transformation as AI-driven businesses begin to reach software-like profit margins. What additional evidence underpins this assertion beyond traditional presentations?

A company with over five decades of experience has witnessed technology’s evolution from mainframes to cloud computing and now to AI. In the late 1990s, the transition to e-business became vital for traditional firms seeking an online presence. This shift resulted in outsourcing and offshoring, creating a demand for software services in locations like India. A similar progression occurred with supply chains in China and Taiwan. As we enter the AI era, it stands as a transformative force, enhancing human capabilities and redefining business practices.

By automating repetitive tasks, we foresee two growth trajectories: organic and inorganic…

Could you explain how this is expected to develop?

What capabilities can a language learning model (LLM) or AI offer? For example, in a Salesforce implementation, a human client manager oversees operations while AI handles various tasks, allowing humans to concentrate on areas requiring their expertise.

Businesses can assign more responsibilities to AI, charging clients based on actual AI usage.

The strategy should focus on not directly competing with established firms like Accenture, Infosys, or TCS. Instead, attention should be directed toward underserved markets. In the U.S. alone, there are approximately 30 million small businesses—and around 100 million worldwide—many of which lack access to specialized talent. Developing software solutions for these companies—such as helping them find receptionists or build websites—can yield significant benefits. AI could assist with tasks like preparing startup funding applications with minimal human involvement in negotiations. Rather than clashing with major players like Accenture, focus on fragmented markets and shift from hourly billing to event-based pricing.

So, you support an outcome-based pricing model in place of traditional hourly billing.

Exactly; this aligns with outcome-based pricing… akin to cloud services or utility models… If AI can take on 80% of the workload, that sector might achieve gross margins of 80% to 90%, whereas human tasks typically produce only 30% to 40%. By aiming for combined margins of 60% to 70%, net profits could reach 20% to 30%. Many service firms are already profitable, while tech companies often rely on venture funding and public market financing.

You recently led the Series A funding for Gruve, an AI consulting startup. What impressed you during its early customer trials?

This case exemplifies the combination of organic and inorganic growth. Gruve was founded by experienced entrepreneurs who previously ran two service firms, generating around $500 million in revenue with profits between $50 to $100 million. Their current focus is on security. They acquired a $5 million consulting firm specializing in managed security services, recognizing AI’s potential as the industry’s future. In just six months, they increased revenue from $5 million to $15 million, achieving an 80% gross margin. Clients appreciated their outcome-based pricing; one noted, “Why hire a security team if I’m not getting hacked?” Gruve’s tagline captures their approach well: “You only pay us if you experience a hack or there’s an incident.”

Can major firms like McKinsey easily adopt these AI capabilities? They have established businesses to protect.

Indeed, this points to the innovator’s dilemma. Large enterprise software companies relying on perpetual licenses have been slower to shift toward SaaS models, which require monthly payments instead of upfront, multi-year contracts with maintenance fees. Similarly, firms like McKinsey and Accenture are heavily invested in maintaining their existing business models. Therefore, I encourage founders to target underserved markets and develop innovative go-to-market strategies for clients that larger firms cannot adequately serve.

Over time, these smaller firms could evolve to compete with larger rivals like McKinsey or Accenture. Major players face their innovator’s dilemma as they consider shifting to an outcome-based pricing model while being cautious of risking established revenue streams.

You allocated $100 million from your recent fundraising efforts to support “AI teammates” last fall. What differentiates a true AI teammate from just another tool?

The tech landscape is filled with buzzwords. Initially, we referred to these entities as copilots, then AI tools, agents, and now AI teammates. At Mayfield, we define an AI teammate as a collaborative digital partner working alongside humans toward shared objectives for improved outcomes. This technology may include agentic systems or copilots that support various organizational functions, such as HR or sales engineering. The focus is on partnership, not replacement.

As conversations about teammates and assistants increase, some may view this as insensitive due to potential job losses. Is Silicon Valley facing a public relations concern?

You raise an important issue; this necessitates open discussions. Job displacement is a genuine concern, but human adaptability is critical. AI acts as a tool under human strategic guidance. Historically, technological advancements have triggered fears of job loss; however, they often lead to job market expansion. For example, when Microsoft Word was released, fears arose for executive assistants, yet their roles evolved. A similar transition occurred with Excel for accountants. Rather than shrinking job opportunities, markets generally expand.

I believe emerging markets such as India, China, or Africa have bypassed traditional paths, allowing them to leap directly to wireless technologies. A similar shift is expected with AI filling roles currently unoccupied by humans; while short-term disruptions may occur, I remain optimistic about the long-term advantages.

Recently, a “vibe-coding” deal involved a six-month-old Israeli startup that attracted 250,000 monthly users and generated $200,000 in monthly revenue, which was acquired by Wix for $80 million. Does that valuation resonate with you?

In the current environment, traditional metrics can seem outdated. We are navigating an AI-driven era marked by uncertainty. With annual recurring revenue of $2.4 million, I may have expected a sale price closer to $800 million. It’s a fascinating time, influenced by numerous factors that shape valuations.

How do you navigate investing in such a landscape?

True investment wisdom comes from the insights of seasoned investors who have adeptly navigated different market cycles. It requires balancing discipline with a clear vision, avoiding the fear of missing out (FOMO), which can mislead many. It’s important to remember that venture capital emphasizes sound financial management over merely acquiring prominent brands. The goal should be to transform smaller investments into substantial returns.

There are numerous profitable opportunities in this cycle, although many ventures may encounter difficulties due to a lack of understanding.