Uncapped with Jack AltmanThe Next Generation of Software | Mamoon Hamid, Partner at Kleiner Perkins | Ep. 16
CHAPTERS
From 1997 engineer to VC curiosity: living inside the first internet boom
Mamoon Hamid recounts arriving in Silicon Valley in 1997 as a young engineer at Xilinx and experiencing the internet’s early momentum firsthand. Seeing iconic products (Netscape, Sun, Amazon, Google) and learning they shared a common Series A backer planted the seed that venture capital could be an impactful career.
- •First job at Xilinx (a Kleiner-backed company) during the internet buildout era
- •Early exposure to Netscape, Amazon, Google, and the feeling of rapid innovation
- •Realization that top companies often shared the same early investors
- •The late-90s Valley energy: exuberance, momentum, and escalating hype
The dot-com bubble: when prices detached from reality
The conversation moves into what the dot-com era felt like from the ground—both exciting and increasingly irrational. Mamoon describes day trading culture, companies with no revenue achieving massive valuations, and the growing presence of “non-builders” chasing monetization.
- •Shift from builder-driven excitement to excess and speculative behavior
- •Engineers day trading; IPO pops and short-term flipping became common
- •Valuations couldn’t be connected to real customer value or business fundamentals
- •Takeaway: bubbles can be recognized when reality and pricing diverge sharply
Lessons from cycles: stay optimistic, but obsess over timing
Mamoon explains how living through bubbles changes (and shouldn’t over-harden) an investor’s mindset. The key is balancing openness and optimism with a rigorous “why now” framework—many dot-com ideas were correct but too early for adoption and infrastructure.
- •Avoid becoming overly cynical—otherwise you miss the next wave
- •Timing matters: adoption readiness and infrastructure lagged in dot-com
- •Examples of ‘too early’ ideas later succeeding (Webvan → Instacart)
- •Framework: evaluate technology maturity, product feasibility, and reachable users
2000–2005: the slowdown, immigration constraints, and business school as reset
After the crash, Mamoon describes a quieter Valley where jobs were scarce and mobility was limited, especially for those on visas. He uses the downturn to step away for business school, returning when the next wave begins to form.
- •Post-bubble contraction made hiring and switching jobs difficult
- •Visa/green card realities shaped career decisions
- •Business school (Harvard) as a strategic ‘leave and return’ move
- •Witnessing early signals of the next consumer-web era forming
Web 2.0 and the emergence of cloud software: the pre-consensus years
Returning in 2005, Mamoon joins venture and shifts focus from semiconductors to web and cloud software—before it became the dominant investing narrative. He highlights how early Web 2.0 products and UI-centric experiences reshaped expectations, while SaaS adoption was still far from a given.
- •Early Web 2.0 era: Facebook, MySpace, Flickr, Yelp as signals of a shift
- •Cloud/SaaS wasn’t consensus; hardware/networking still dominated VC thinking
- •Salesforce existed but the market hadn’t broadly embraced cloud for work yet
- •Cultural mismatch: investors doubted young founders selling to enterprises
Investing in Box: a product thesis meets an exceptional founder
Mamoon explains his early thesis: if desktop software moves to the browser, file sharing/file exploration would be foundational. Box became his first major investment, supported by both a clear product-category logic and immediate conviction in Aaron Levie’s founder quality.
- •Thesis: “What desktop workflow moves to the browser first?” → files and sharing
- •Box investment (2007) before SaaS became mainstream
- •Founder signal: Aaron’s clarity, depth, and ability to preempt objections
- •Early cloud realities: even cloud-native companies still “racked and stacked”
Mobile’s early days: from HTML5 wrappers to native killer apps
Mamoon places mobile as a wave that overlapped with cloud but took time to mature into native-first experiences. He notes how debates about wrappers vs native ended as gaming and apps like Uber pushed performance and engagement expectations forward.
- •Even 2011–2012: major products weren’t fully native mobile experiences
- •Native vs wrapper debate; eventual dominance of native iOS apps
- •Games drove early App Store charts and accelerated mobile innovation
- •Cloud + mobile defined much of the 2010s tech landscape
AI as a supercycle: mapping a $60T labor opportunity
Mamoon frames AI as larger than prior waves because it targets not only software and productivity but labor itself. He uses macroeconomic context (GDP, tech as % of GDP, labor share) to argue AI can reshape value allocation and create trillion-dollar venture outcomes.
- •‘Day zero’ moment: seeing ChatGPT demo in Oct 2022 as a qualitative inflection
- •Macro lens: world GDP growth and tech’s rising share of value creation
- •Labor is ~60% of GDP → AI automation/augmentation represents ~$60T opportunity
- •Early winners already visible in public markets (e.g., NVIDIA and hyperscalers)
Where to invest in AI: application layer first, using a ‘jobs pyramid’
Rather than focusing on foundation models or infrastructure, Mamoon explains Kleiner’s application-centric approach. They organize opportunity by job categories—starting with high-skill professions—initially building “copilots” that evolve toward autonomy.
- •Three layers: models, middleware/infrastructure, and applications; KP leans apps
- •Jobs-to-be-done framing and a ‘job pyramid’ from high skill to physical labor
- •Copilots for doctors/lawyers/engineers (e.g., Ambient, Harvey, Windsurf)
- •AI scribing example: automated transcription, diagnosis support, and billing codes
From copilots to autonomous agents—and why robotics is later
Mamoon describes the next step down the pyramid: roles like nursing, sales, and analysis where agents can perform work end-to-end. He contrasts this with robotics, which faces harder data, cost, and physical-world complexity constraints—making it a longer-timeline bet.
- •Autonomous agent example: Hippocratic handling nurse-like outreach calls at scale
- •AI creates ‘abundance’ (persistence, perfect follow-ups, time-window targeting)
- •Robotics challenge: far more complex data/action space than text-only corpora
- •Near-term wins: constrained industrial robotics (e.g., Dexterity loading trucks)
Reigniting Kleiner Perkins: ‘back to the future’ and a refounding mindset
Mamoon explains joining Kleiner in 2017 as both a personal full-circle moment and an institutional reset. The firm focused back on what historically made it great: a small, early-stage, craft-driven partnership centered on serving founders.
- •Kleiner as the original inspiration from his early engineering days
- •Diagnosis phase: meet everyone, identify assets vs liabilities
- •Return to core: small team, early-stage specialists, founder-first orientation
- •Mission statement: “first call for founders who want to make history”
Growing vs recruiting talent: building a small partnership with strong culture
He outlines why Kleiner intentionally keeps the partnership small and emphasizes internal development. Culture fit—servant leadership and founder service—matters as much as raw investing skill, and the firm optimizes for deep debate around a single table.
- •Target partner count ~5–7 to preserve decision quality and open debate
- •Add talent slowly (roughly one person per year) vs scaling headcount
- •Preference for growing from within to protect culture and consistency
- •Servant leadership: the job is to work tirelessly for founder success
Win-rate discipline and the KP operating system: see, pick, win, work
Mamoon shares how KP evaluates both deals and investors via a four-stage funnel: seeing, picking, winning, and working. They track Series A coverage weekly, aspire to win every deal they choose to pursue, and review losses systematically to improve their playbook.
- •Metrics: target seeing ~60% of peer Series A deals; track weekly
- •Internal aspiration: 100% win-rate on deals the firm decides to pursue
- •Post-mortems: maintain a loss log and revisit at offsites for learning
- •Investor assessment: seeing/picking/winning early; ‘working’ shows over years
Pattern recognition in top investments—and the founder qualities that matter
Reflecting on Box, Slack, Figma, Rippling, and Applied Intuition, Mamoon distinguishes between non-competitive early bets and later competitive wins. His recurring signals include small-N but extreme engagement, product-obsessed builders, and founder-executors who can “will the future” into existence.
- •Early investments like Box/Slack weren’t competitive; conviction came from signals
- •Slack insight: enterprise engagement metrics (DAU/MAU, time-in-product) stood out
- •Figma pattern: companies incubating for years, then breakout after launch
- •Founder archetypes: product-obsessed builders vs visionary execution machines
KP’s fund strategy and personal grounding: doubling down + family and faith
Mamoon explains the two-fund approach: an early-stage fund requiring outlier outcomes and a select/growth vehicle to double down on the best performers (and occasionally re-enter missed deals). He closes with what anchors him outside work—family and faith—and how those values shape how he treats founders and colleagues.
- •Early-stage fund math: ~35 companies; needs a few outliers to drive returns
- •Select fund: concentrate follow-on capital into strongest board-led companies
- •Operating model: partners co-located; in-person table debates as a design choice
- •Personal priorities: family first; Hajj experience; faith guiding respect and humility
