What are the key takeaways from this a16z Podcast episode?

Key insights include: **Separate fund structure:** A16z created a dedicated vehicle rather than folding hardware deals into existing funds because early-stage infrastructure companies require disproportionate upfront capital. Evaluated against software deals generating $1-2B revenue immediately, hardware investments lose every time in a shared pool — a dedicated fund removes that structural bias and signals commitment to founders.; **Hardware pitch surge:** Hardware and physical-world pitches grew from essentially zero to over 20% of all deals a16z reviews. Founders driving this are typically experienced operators spinning out of incumbents — NextHop's team came from Arista, Unconventional's Naveen Rao came from Databricks — not first-time founders, because hyperscaler relationships are prerequisite to building at this scale.; **Infrastructure mismatch:** Current data center infrastructure was built for internet and SaaS workloads, not AI's compute intensity. Rebuilding from a blank sheet looks fundamentally different — including a shift from AC to DC power, for which fewer than 2% of U.S. electricians are currently trained, creating a concrete workforce bottleneck alongside the hardware bottleneck.

What did Jen Kha discuss on a16z Podcast?

Andreessen Horowitz managing partner Jen Kha explains the rationale behind the firm's $1.1B Machine Age Fund, which targets physical AI infrastructure — chips, networking, memory, cooling, and data centers — a category that grew from near zero to over 20% of a16z's incoming pitches. Key topics include: **Separate fund structure:** A16z created a dedicated vehicle rather than folding hardware deals into existing funds because early-stage infrastructure companies require disproportionate upfront capital. Evaluated against software deals generating $1-2B revenue immediately, hardware investments lose every time in a shared pool — a dedicated fund removes that structural bias and signals commitment to founders.; **Hardware pitch surge:** Hardware and physical-world pitches grew from essentially zero to over 20% of all deals a16z reviews. Founders driving this are typically experienced operators spinning out of incumbents — NextHop's team came from Arista, Unconventional's Naveen Rao came from Databricks — not first-time founders, because hyperscaler relationships are prerequisite to building at this scale..

How long is this episode of a16z Podcast?

This episode is 24 minutes long. SignalCast provides an AI-generated summary so you can get the key insights in about 3 minutes.

a16z Podcast

Why a16z Launched the Machine Age Fund | Jen Kha

August 30, 2026

24 min episode · 2 min read

Jen Kha

Episode

24 min

Read time

2 min

Topics

Relationships, Investing, Startups

AI-Generated Summary

Published Aug 30, 2026

Key Takeaways

✓Separate fund structure: A16z created a dedicated vehicle rather than folding hardware deals into existing funds because early-stage infrastructure companies require disproportionate upfront capital. Evaluated against software deals generating $1-2B revenue immediately, hardware investments lose every time in a shared pool — a dedicated fund removes that structural bias and signals commitment to founders.
✓Hardware pitch surge: Hardware and physical-world pitches grew from essentially zero to over 20% of all deals a16z reviews. Founders driving this are typically experienced operators spinning out of incumbents — NextHop's team came from Arista, Unconventional's Naveen Rao came from Databricks — not first-time founders, because hyperscaler relationships are prerequisite to building at this scale.
✓Infrastructure mismatch: Current data center infrastructure was built for internet and SaaS workloads, not AI's compute intensity. Rebuilding from a blank sheet looks fundamentally different — including a shift from AC to DC power, for which fewer than 2% of U.S. electricians are currently trained, creating a concrete workforce bottleneck alongside the hardware bottleneck.
✓Global AI adoption race: Countries are treating AI adoption as a national priority equivalent to industrialization. South Korea is deploying premium AI as a public utility; El Salvador integrated Grok into schools at no cost and uses AI doctors. A16z's global LP relationships serve a dual function — capital raising and accelerating technology adoption within partner governments.
✓Early-stage ownership strategy: The fund targets seed and Series A entry points, deploying $25-35M to maximize ownership before inflection. The Unconventional investment — a chip redesigned from scratch for AI workloads — exemplifies this: a16z entered at seed, then the company raised a substantially larger follow-on round, validating the ownership-maximization thesis over later-stage participation.

What It Covers

Andreessen Horowitz managing partner Jen Kha explains the rationale behind the firm's $1.1B Machine Age Fund, which targets physical AI infrastructure — chips, networking, memory, cooling, and data centers — a category that grew from near zero to over 20% of a16z's incoming pitches.

Key Questions Answered

•Separate fund structure: A16z created a dedicated vehicle rather than folding hardware deals into existing funds because early-stage infrastructure companies require disproportionate upfront capital. Evaluated against software deals generating $1-2B revenue immediately, hardware investments lose every time in a shared pool — a dedicated fund removes that structural bias and signals commitment to founders.
•Hardware pitch surge: Hardware and physical-world pitches grew from essentially zero to over 20% of all deals a16z reviews. Founders driving this are typically experienced operators spinning out of incumbents — NextHop's team came from Arista, Unconventional's Naveen Rao came from Databricks — not first-time founders, because hyperscaler relationships are prerequisite to building at this scale.
•Infrastructure mismatch: Current data center infrastructure was built for internet and SaaS workloads, not AI's compute intensity. Rebuilding from a blank sheet looks fundamentally different — including a shift from AC to DC power, for which fewer than 2% of U.S. electricians are currently trained, creating a concrete workforce bottleneck alongside the hardware bottleneck.
•Global AI adoption race: Countries are treating AI adoption as a national priority equivalent to industrialization. South Korea is deploying premium AI as a public utility; El Salvador integrated Grok into schools at no cost and uses AI doctors. A16z's global LP relationships serve a dual function — capital raising and accelerating technology adoption within partner governments.
•Early-stage ownership strategy: The fund targets seed and Series A entry points, deploying $25-35M to maximize ownership before inflection. The Unconventional investment — a chip redesigned from scratch for AI workloads — exemplifies this: a16z entered at seed, then the company raised a substantially larger follow-on round, validating the ownership-maximization thesis over later-stage participation.

Notable Moment

A16z closed a $1.1B fund in roughly two months over summer — historically the deadest fundraising period — with one partner half-seriously suggesting the capital could deploy in six weeks. The speed signals how dramatically LP appetite for private AI infrastructure exposure has shifted.

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Episode Transcript

South Korea, by the way, just announced that they're giving premium AI to every citizen as sort of a public utility thing. So very, very But by the way, it's not just them. It's El Salvador. They implemented Grok in their schools, for example, for free, and they're utilizing AI doctors, for example. And you see these different examples around the world where they're accelerating their AI development and adoption way faster than in The US. We might also see the influx of a lot of this data center supply chain build up happen overseas because of this sentiment in The US as well. For decades, venture capital moved further and further away from hardware. AI is pulling it back. Jen Cobb, managing partner and head of global partnerships at e sixteen z, joins Theo Jaffe and Sofia Du on NTS to discuss a sixteen z's machine age fund and why the physical infrastructure underneath AI is suddenly one of the most active areas for founders. They get into why the existing stack wasn't designed for today's AI workloads, what needs to change across chips, networking, memory, cooling, and data centers, and why hardware has gone from a tiny fraction of the pitch's a 16 z sees to more than 20%. Jen also explains why this infrastructure race is increasingly global, how governments and institutional investors are thinking about AI as a national priority, and why, as she puts it, what's old is new again. Hello, everyone, and welcome back to MTS. Andreessen Horowitz just launched a new $1,100,000,000 machine age fund focused on the physical infrastructure underneath AI, from chips networking to data centers, robotics, and energy. And joining us today is Jen Kah, who's a managing partner and head of global partnerships at a sixteen z, to talk about the Machine Age Fund and the investment thesis behind it. Jen, welcome to MTS. Hello. Hello. It's good to be here. Great to have you I know Theo just said a big woo, but it's pretty exciting. And we wanted to talk about why this Machine H fund. Why now? For sure. By the way, the classic adage is sell in May and go away. Has been the most prolific summer, at all. Fact that we're announcing a fund on August 28 when typically, you know, Wall Street is dead is like a classic sign of where we are in the cycle and the time, which is just there's so much going on. But we announced this $1,100,000,000 machine age fund to invest into all of the physical constructs of the world that is now so bottlenecked, given all of the demands in AI. And think about it as everything below the software stack. So we've got our infra fund, which invests into products for developers. We've got our apps fund, sells into business to business and business to consumer. This is below all that. All the physical parts of enabling AI from data centers to chips, to custom …

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