The Next Generation of Software: AI Investment Strategy from Silicon Valley's Most Storied Firm

Mamoon Hamid reveals how Kleiner Perkins rebuilt itself around AI investing, using job pyramids to target trillion-dollar opportunities in autonomous work.

Key Takeaways

• AI represents the "super cycle of all super cycles" with $60 trillion in labor automation opportunities available
• Investment strategy should follow job pyramids: target highly skilled workers first, then move down to autonomous agents
• Successful venture firms require small teams of 5-7 partners who can debate investments around one table effectively
• Pattern recognition across tech cycles shows timing matters more than technology readiness for startup success
• Rebuilding storied firms requires returning to core strengths while adapting to new technological waves
• Product-obsessed founders who poke holes in their own solutions consistently outperform visionary-only entrepreneurs
• Small user base with high engagement signals beats large user base with low engagement for early investment decisions
• Winning 100% of desired investments requires full partnership commitment and systematic competitive intelligence

Timeline Overview

• Early Career (1997-2005) — Engineering at XYlinks during internet boom/bust, witnessing rise of Google, Amazon, Netscape; business school during downturn
• Cloud Investment Era (2005-2015) — Joining venture capital, making first investment in Box (2007), backing Slack when enterprise engagement metrics weren't standard
• Kleiner Perkins Rebuilding (2017-2019) — Assessing firm assets/liabilities, returning to small early-stage partnership model, creating "back to the future" strategy
• AI Investment Focus (2022-Present) — ChatGPT demo moment in October 2022, developing job pyramid investment thesis, targeting co-pilots for skilled workers
• Current Strategy Execution — 15 consecutive AI investments, autonomous agents for mid-skilled work, preparing for robotics/physical labor automation

Silicon Valley Evolution Through Multiple Tech Cycles

• Mamoon arrived in Silicon Valley in 1997 as a 19-year-old engineer at XYlinks, experiencing the internet boom with firsthand exposure to Netscape, Sun workstations, Google's early days, and Amazon book purchases for Stanford classes. All these companies were Kleiner Perkins series A investments, sparking his initial interest in venture capital.
• The late 1990s felt similar to today's AI moment but with more parties and excess. By 1998-1999, "non-builders had arrived to help monetize the internet" with questionable business models sitting on top of the hype bubble, creating a palpable feeling of progress and momentum.
• During the boom, even engineers were day trading stocks with E-Trade and Schwab accounts because companies would have 10x public market runs in single days. "You could not connect reality to the prices of these stocks" - companies with no revenue commanded tens of billions in market cap.
• The 2000-2005 downturn created a period where "nobody could get a job." Mamoon stayed at XYlinks for six years partly due to visa restrictions, then used the slow period strategically to attend Harvard Business School and return when things became exciting again.
• Timing proves crucial across all cycles. Many dot-com era ideas were simply premature - it took 5-7 years for mainstream adoption of technologies and behaviors that seemed obvious during the bubble. The lesson: "keep an open mind and some naivety" rather than becoming the old person who dismisses new opportunities because similar ones failed before.
• Pattern recognition requires balancing historical perspective with openness to new possibilities. Successful investors must ask "is it the time now or is it 3, 4, 5 years from now?" while remembering that some concepts like online grocery delivery took 15 years between failed and successful attempts.

Investment Philosophy and Pattern Recognition

• Early cloud software investing required conviction against consensus. When Mamoon invested in Box in 2007, "there were no other investors who wanted to invest in the company at the time" and people questioned whether young founders could build enterprise software businesses.
• The venture ecosystem in the late 1990s was dominated by business-focused investors with limited technical backgrounds who were "buttoned up" and focused on hardware, networking, and semiconductors. The transition to backing young founders in t-shirts building software represented a fundamental shift in investor mentality.
• Small user base with high engagement consistently signals better investment opportunities than large user bases with low engagement. Slack's "insane" engagement metrics - 50% daily active users spending four hours per day in the product - mattered more than user count when evaluating the 2013 series A.
• Product-obsessed founders who "poke holes in their own products" before investors can represent the strongest archetype for backing. These founders understand their solutions' limitations and continuously iterate rather than falling in love with initial versions.
• The second successful archetype involves visionary founders who are primarily "execution machines" that "run through brick walls" to will their future into existence. Parker Conrad at Rippling exemplifies this by building 25 different products beyond the initial HRIS focus.
• Prepared mind investing involves developing deep conviction about specific categories before meeting founders. Mamoon's focus on workplace productivity stemming from his German upbringing created predisposition to back Aaron Levie, Stewart Butterfield, Dylan Field, and other productivity-focused entrepreneurs.

Rebuilding Kleiner Perkins: Return to Historical Strengths

• When Mamoon joined Kleiner Perkins in 2017, he spent the first two months meeting everyone from front desk staff to former partners to understand "what made us great, what were the assets and what were the liabilities."
• The assessment revealed that Kleiner's greatest historical periods involved "small partnerships of early stage technical people, practitioners who cared about the craft of venture capital, being in the trenches with founders, really being truly their first partners and their best partners."
• By 2017, the firm had diversified into "all kinds of different products" and "different geographies" rather than focusing on core early-stage strengths. The rebuilding strategy involved returning to a "small lean team of early stage practitioners" with the tagline "back to the future."
• Historical analysis showed Kleiner "pretty much nailed every single dominant company" across four major technology waves: semiconductors, computers, software, and internet. This track record justified the mission statement: "we want to be the first call for founders who want to make history."
• The rebuilding required assembling partners who are "technologists with some operating background but truly want to be exceptional at investing" as their primary career focus, not as a secondary activity after founding companies. This led to growing talent internally rather than recruiting established GPs.
• Optimal partnership size stays between 5-7 partners to enable effective decision-making around one table. "At some point you go beyond seven, stuff starts to be different" and research suggests the Goldilocks number is approximately six for group dynamics and debate quality.

AI Investment Strategy: The Job Pyramid Approach

• AI represents "the super cycle of all super cycles" because it addresses the $60 trillion global labor market, not just productivity improvements. "AI is not just a new way of working, new productivity technology... there's an element of labor, doing the job, doing the actual work autonomously."
• The investment strategy follows a job pyramid structure starting with highly skilled, highly paid workers ($200k+ annually) at the top: doctors, lawyers, engineers. These roles are "fairly scarce in nature" but command premium compensation, making them ideal targets for co-pilot solutions.
• Co-pilot approaches work best initially because "there are parts of these jobs that are very nuanced and the human brain needs to process those parts" while AI handles routine tasks like medical transcription, legal research, or code documentation.
• Ambience for doctors automates medical scribing, turning 20-minute conversations into fully transcribed EMR entries with diagnosis, prescribed drugs, and billing codes. Harvey for lawyers and Windsur for engineers follow similar co-pilot patterns for their respective professional workflows.
• The second pyramid layer targets slightly lower-paid but still skilled workers like nurses, salespeople, and financial analysts where "some of those parts of those jobs you can actually just do them completely autonomously" rather than requiring human oversight.
• Hypocratic exemplifies autonomous agent approaches by making thousands of phone calls for pre-op conversations, wellness checks, and post-op follow-ups at optimal times (8-9am or 5-6pm) when patients actually answer, something human nurses cannot scale effectively.

Team Building and Talent Development Strategy

• Kleiner Perkins typically adds "one person a year or so, maybe max" without hard growth targets because they're "not trying to necessarily grow fund sizes." The focus stays on maintaining optimal team dynamics rather than expansion for its own sake.
• Talent assessment follows a four-stage framework: seeing (identifying good investments), picking (choosing which to pursue), winning (successfully getting selected by founders), and working (driving portfolio company success through value-added support).
• Internal goal for winning rates: "100%. If we want to invest in a company, we have to win it." This requires full partnership commitment including endorsements from existing portfolio founders, leveraging Kleiner Perkins brand value, and systematic competitive intelligence.
• Seeing targets focus on 60% coverage of all series A deals by peer venture firms, measured weekly. "Seeing means you actually met with the company, not just like you heard about it. You spent time on it" and made a deliberate decision.
• Working assessment takes 4-7 years to evaluate properly. "Every partner has to deliver a company per fund that's a really good company" from the 35 investments made per fund cycle, with expectations for 2 fund-returner outcomes per fund.
• The firm maintains a "spreadsheet of all the losses" from competitive deals, reviewing them at every offsite to identify gaps in the playbook and improve future winning rates through systematic learning.

Investment Mechanics and Fund Strategy

• The current structure involves an $800 million early-stage fund investing in 35 companies, requiring $4 billion in returns (5x multiple) through portfolio companies worth $40 billion where Kleiner owns 10% stakes.
• Mathematical requirements: "out of those 35 companies, you need to have two fund returners that get you pretty close to the 5x" outcome, meaning every partner must deliver exceptional companies per fund cycle.
• The Select Fund ($1.2 billion) serves dual purposes: half goes to doubling down on existing portfolio companies like Figma, Rippling, Glean, and Harvey where "we just see that this has greatness written all over it."
• The other Select Fund half targets companies missed at series A or B stages. "Inevitably we're going to miss and not pick well at the A and the B" so the growth fund provides 1-2 opportunities annually to correct those mistakes.
• This dual-fund structure keeps decision-making centralized with "one partner group investing out of both funds" while maintaining the collaborative table-sitting approach to investment decisions.
• Geographic focus remains deliberately concentrated: "even just half of all the amazing stuff that happens in tech still comes from San Francisco to San Jose and we're there, that's plenty of pie to eat."

Leadership Philosophy and Personal Values

• Faith provides fundamental grounding for leadership approach. Mamoon's Hajj pilgrimage reinforced that "in front of God, we're all one and we're all the same" regardless of background, informing how he treats every interaction from "six-year-old children to 30-year-old billionaire founders."
• The servant leadership culture at Kleiner Perkins emphasizes "we are here to serve our founders... this is not about us as individuals, it's about our job to our founders which is to work on their behalf tirelessly to help them become successful."
• Founder meeting preparation involves recognizing that "typically pitching to VCs is the biggest thing you could be doing in that moment" and working to make entrepreneurs "feel just a little bit more comfortable and more respected" during inherently intimidating fundraising processes.
• Assessing founder intentions requires looking beyond stated motivations to understand "true intentions. Do you really want to be doing this for the next decade of your life?" through conversation analysis, body language, and understanding what drives willingness to sacrifice family time and other life priorities.
• Engineering background provides analytical foundation while emotional intelligence determines founder evaluation success. "I look for the EQ and use the EQ I have to figure out some of those things about the other person" when determining moral compass and authentic motivations.
• The long-term perspective involves building generational wealth and impact while maintaining human dignity. "Every interaction matters" whether in professional settings or family life, creating consistency between personal values and professional conduct that founders can trust.

Mamoon's approach demonstrates how the most successful venture capital strategies combine historical pattern recognition with adaptive frameworks for new technological waves. His rebuilding of Kleiner Perkins shows that even storied institutions can reinvent themselves by returning to core strengths while embracing emerging opportunities like AI's trillion-dollar labor automation potential.