Why AI Will Eventually Surpass McKinsey – but Not Anytime Soon

Navin Chaddha, managing director at Mayfield—a venture firm with a 55-year history in Silicon Valley—forecasts that AI will revolutionize labor-intensive sectors like consulting, law, and accounting. With a track record of investing in successful companies such as Lyft, Poshmark, and HashiCorp, he presented his views at TechCrunch’s StrictlyVC event in Menlo Park. He believes that “AI teammates” could generate profit margins akin to software in sectors plagued by high operational costs, encouraging startups to focus on underserved markets rather than competing against giants like Accenture. He also highlights the challenges of disrupting industries built on trust and relationships. This discussion has been lightly edited for clarity and conciseness.

You assert that law firms, consulting agencies, and accounting services—a $5 trillion market—will face a major transformation due to AI-driven companies utilizing software-like margins. What evidence supports this claim beyond standard presentations?

A firm with over five decades of experience has witnessed technological advancements from mainframes to cloud solutions and now to AI. In the late ’90s, e-business became essential for brick-and-mortar companies wanting an online presence. This evolved into outsourcing and offshoring, creating a demand for software service firms in regions like India and other developing nations. A similar evolution occurred in supply chains with countries like China and Taiwan. As we move into the AI era, it’s apparent that AI is a transformative force that enhances human capabilities and redefines business processes.

As AI takes over repetitive tasks, we expect to see two models of growth: organic and inorganic…

Can you provide a concrete example of how this will occur?

What capabilities can a language learning model (LLM) or AI offer? For example, during a Salesforce implementation, a human client manager oversees operations. By leveraging AI for various tasks, humans can focus on areas requiring their expertise.

Organizations will have the ability to allocate greater responsibilities to AI, billing clients based on actual AI usage.

The goal should be to avoid direct competition with major firms like Accenture, Infosys, or TCS. Instead, concentrate on underserved segments. In the U.S. alone, there are 30 million small businesses, and globally, 100 million, many of which lack access to specialized knowledge workers. Create software solutions for these businesses—like helping them find a receptionist or build a website. AI can assist with tasks such as preparing startup funding applications, requiring minimal human intervention for negotiations. Rather than competing directly with large corporations like Accenture, target fragmented markets and transition from hourly billing to event-based pricing.

So you support an outcome-based pricing model as opposed to traditional time-based billing.

Exactly; this aligns with outcome-based pricing… Similar to cloud services or utility billing… If AI can manage 80% of the workload, that sector could offer 80% to 90% gross margins, while humans typically achieve only 30% to 40%. By attaining blended margins of 60% to 70%, net profits could reach 20% to 30%. Many service companies are profitable, whereas tech firms often rely on venture capital and public market funding.

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

This scenario illustrates the intersection of organic and inorganic growth. Gruve was launched by experienced entrepreneurs who previously founded two service firms, each generating around $500 million in revenue and $50 to $100 million in profits. Their focus this time was on security. They acquired a $5 million consulting firm specializing in managed security services, recognizing AI as the path forward. In just six months, they boosted revenue from $5 million to $15 million, attaining an 80% gross margin. Clients appreciated this outcome-based model; one noted, “Why pay a security team if I’m not getting hacked?” Gruve’s innovative approach states, “You only pay us if you get hacked or if there’s an incident.”

Can bigger firms like McKinsey simply acquire these AI capabilities? They have existing businesses to protect.

Yes, this poses the innovator’s dilemma. Large enterprise software companies, accustomed to perpetual licensing, have been slow to embrace SaaS models that necessitate monthly fees instead of upfront multi-year contracts with maintenance costs. Similarly, firms like McKinsey and Accenture are entrenched in preserving their current business frameworks. Thus, I advise founders to focus on overlooked markets and develop distinctive go-to-market strategies to engage clients that larger companies cannot efficiently serve.

Over time, these smaller firms could grow to take on major competitors like McKinsey or Accenture. Larger firms face their own innovator’s dilemma as they ponder adopting an outcome-based pricing model, wary of jeopardizing revenue from established sources.

You invested $100 million from your recent fundraising to promote “AI teammates” last fall. What separates a genuine AI teammate from just another tool?

The tech landscape is filled with buzzwords. At first, we talked about copilots, then AI tools, agents, and now AI teammates. At Mayfield, we believe that an AI teammate acts as a collaborative digital partner, working alongside humans toward shared goals for improved outcomes. This technology may be built on agentic systems or copilots, fulfilling various functions within organizations, like HR or sales engineering support. The emphasis is on collaboration, not replacement.

As discussions about teammates and assistants become more prevalent, some might find this insensitive given job losses. Does Silicon Valley face a PR issue?

You raise an important point; this needs to be addressed transparently. While job loss is a genuine concern, human adaptability is crucial. AI acts as a tool under human strategic direction. Historically, technological advancements have sparked fears of job loss, but they have ultimately broadened job landscapes. For instance, when Microsoft Word was introduced, many worried about the fate of executive assistants, yet their roles evolved. A similar situation unfolded with Excel and its impact on accountants. Rather than reducing job opportunities, markets typically expand.

I believe emerging markets like India, China, or Africa have bypassed traditional structures, allowing them to leap directly to wireless technologies. A similar transition is expected with AI taking on roles currently unfilled by humans, and while short-term disruptions may occur, I remain optimistic about the long-term advantages.

Regarding coding, a recent “vibe-coding” deal involved a six-month-old Israeli startup that garnered 250,000 monthly users and $200,000 in monthly revenue, sold to Wix for $80 million. Does that valuation resonate with you?

In the current market, traditional metrics might appear outdated. We are experiencing an AI-driven era marked by unpredictability. Given their $2.4 million in annual recurring revenue, I might have expected a sale price closer to $800 million. It’s a fascinating time, influenced by multiple factors that affect valuations.

How do you navigate investing in such an environment?

The true skill in investing lies in the wisdom of seasoned investors who have weathered different market cycles. It’s about blending discipline with a clear vision, avoiding the fear of missing out (FOMO), which misleads many. Remember, venture capital is about effective financial management, not just acquiring well-known brands. The focus should be on turning smaller investments into significant returns.

In this cycle, there are many lucrative opportunities, though numerous ventures may flounder due to a lack of comprehension.