Josh Machiz

Chief Marketing Officer at @LightspeedVP, previously Partner at @Redpoint and Chief Digital Officer at @Nasdaq

[Josh Machiz @Machiz]

today i'm joining @lightspeedvp as Chief Marketing Officer.

after doing thousands of IPOs at Nasdaq and building Redpoint's exceptional platform team, i've learned:

it's not just one launch video. it's the relentless drumbeat that get you heard.

and that's what i'm here to build for Lightspeed and our founders.

let's make some noise!

[Jonah Cader @jonahcader]

Nearly every chip on the market forces a tradeoff between capacity and speed.

If you want a chip that can run tasks fast, handle long context, and serve multiple users concurrently at hyperscale, we're going to need a revolutionary new architecture.

@goodwin_ml and the @fractile_ai team are taking a full-stack approach to tackle this wildly difficult problem. Had a great time catching up with the team to talk through our investment in Fractile.

[Josh Machiz @Machiz]

@goodwin_ml @ThriveCapital @lightspeedvp @Redpoint @foundersfund congrats, Walter and team!

[Lightspeed @lightspeedvp]

The next leap in intelligence could depend on advances in compute.

We’re excited to co-lead @fractile_ai’s $720M Series C as they build an inference accelerator to support the next generation of models.

Reasoning and agentic workloads are expanding demand for inference, making the speed and cost of running frontier models increasingly important.

The team is building a chip-first frontier AI company, rethinking accelerator architecture from the ground up. Its systems are designed to run frontier models significantly faster and at a fraction of the cost.

Congratulations to the entire Fractile team!

CC: @guruchahal, @jonahcader, @jshen99

[Jonah Cader @jonahcader]

If we want to meet the demand for frontier tokens, we’re going to need a massive buildout of the underlying compute infrastructure.

Long-horizon tasks, heavy reasoning workloads, and subagents are all accelerating the need for new processors.

@goodwin_ml has brought together an incredible team, creating hardware to push AI’s frontier forward. Today, he and the team are building chips and systems that can deliver both speed and scale. Proud that we @Lightspeedvp are co-leading @fractile_ai’s Series C!

CC: @guruchahal, @jshen99

[Josh Machiz @Machiz]

pov: you’re testing a boat when the motor cuts out in the middle of a lake.

you need to figure out what happened, whether you can get it running again, and if you can keep testing.

@brycestrauss uses this example to explain how engineers investigate a failure.

with their test data in @Nominal_io, they can compare current readings with earlier tests and notes from the team.

Bryce sat down with @clairejyz and me on Lightwork to discuss how Nominal has evolved to help hardware engineers and what the future of physical AI looks like.

[Lightspeed @lightspeedvp]

How can AI help engineers build hardware we can rely on?

@brycestrauss, co-founder of @Nominal_io, joined @clairejyz on Lightwork to discuss how the team is approaching that question.

Nominal is an intelligence platform for hardware engineering, connecting hardware data with engineering context to help teams build, test, and operate complex machines.

For Bryce, the opportunity in physical AI is helping engineers build more ambitious systems, from faster aircraft to new energy technology.

Bringing those systems into everyday use means scaling production with confidence that the hardware will perform as intended.

Full episode below.

0:00 Welcome To Light Work
1:29 What Nominal Builds For Hardware Teams And Origin Story
5:38 Series C And The Analyst Agent
6:40 The Boat Example And Why Context Matters
10:58 Data Infrastructure And The Three Step Process
12:59 The Pitch To Aerospace And Defense Executives
15:58 Team And Hiring Philosophy
17:59 The Future Of Physical AI And Reindustrialization
23:54 The Next Decade And Thank You To Physical Engineers

[Josh Machiz @Machiz]

@CameronLMcCord @brycestrauss nothing beats a five star review from your cofounder in public

[Josh Machiz @Machiz]

@CameronLMcCord @Nominal_io @lightspeedvp @insightpartners let’s go 👏

[Josh Machiz @Machiz]

@ericabrescia truly the best @jonobacon 🥹

[Josh Machiz @Machiz]

blue marble below
data streams in silent grace
starlight feeds my core

first words from a data center in orbit

[Lightspeed @lightspeedvp]

AI in Space

Google says it received the first-ever AI response from a data center-class TPU in orbit. It asked Gemma 4 to write a haiku about space. The satellite sent back this response over Norway:

“Blue marble below / Data streams in silent grace / Starlight feeds my core.”

More on Lightwork this week:

→ The $6.3B Binance Hack

The FBI attributed the roughly $6.3B Binance theft to North Korea’s state-run hacker unit, TraderTraitor. Binance says attackers used a fake job interview to get malware onto an engineer’s work computer, then used an internal AI tool to reach other engineers’ systems.

→ Cybersecurity Gets Physical

Cyberattacks targeted supermarket refrigeration systems across Europe and plasma storage sites in France, showing how digital vulnerabilities can put food and medical supplies at risk. No blood products were lost in the plasma-storage incident.

→ Is the Internet Dying?

Hosts @clairejyz and @Machiz discuss AI slop and how AI overviews are affecting Reddit’s traffic. Andrej Karpathy’s HackerTube takes a different approach to discovery, surfacing YouTube videos recommended on Hacker News. As content gets easier to produce, does curation become more valuable?

0:00 Welcome to Lightwork
1:09 Google's AI Breakthrough in Space
1:45 Project Star Catcher and Space Data Centers
6:01 Google's Next Steps for Space Data Centers
7:27 Binance's $6.3 Billion Crypto Theft
10:56 Cyberattacks Target Supermarket Freezers
14:27 Is the Internet Dying?
15:57 Andrej Karpathy's Answer to AI Slop
16:58 Reddit Loses Traffic to Google's AI Search
20:02 AI Reviews vs. Human Commentary
21:40 How Marketers Can Leverage Reddit in the AI Era
23:31 Why Content Curation Matters in the AI Era

[Josh Machiz @Machiz]

plug the usb-c straight into the heroin

can’t believe this is only $60

[Josh Machiz @Machiz]

@cdarby i was 😭

[Josh Machiz @Machiz]

my favorite person in inference. brings her own fireworks

[Sophia Yang @sophiamyang]

Discussing specialized intelligence with besties @Machiz @SGRegan_

[Anoushka Vaswani @anoushkavaswani]

The speed at which AI agents can infiltrate an environment demands a fundamental change in how security teams detect and respond to attacks. CISOs need to rethink how quickly their teams can identify a threat, investigate it and take action. There's a significant opportunity for tools that shorten the time from detection to containment

More in our CISO report for this year’s @FortuneMagazine Cyber 60: https://t.co/zqZuyWV1Xx

[Josh Machiz @Machiz]

@alexrkonrad @eglyman pleatmaxxing is the final boss. u got fabricmogged

[Josh Machiz @Machiz]

issey miyake making its debut on upstarts. oh and @eglyman too.

[Alex Konrad @alexrkonrad]

How does a corporate card startup become a $60B AI product factory?

In our first on-camera interview a decade in the making, Ramp co-CEO Eric Glyman (@eglyman) answers just that question.

Today, @tryramp is best known for its corporate cards, or maybe its marketing stunts with the likes of Kevin from ‘The Office,’ Tony Hawk and a 70-minute Broadway musical about paying bills.

But Eric says his $60B startup, NYC's most valuable, feels less like a fintech and more like a productivity company driven by one simple question: If a business spends $100 to make $200, how can Ramp improve that ratio?

Nearly 100,000 companies use Ramp today for everything from expenses to managing AI spend. But Eric says that when he zooms out to the millions who still aren't customers, Ramp feels painfully early still.

On The Upstarts Podcast, Eric shares why he’d rather run Ramp like Amazon than Google; why Ramp hires for proof of work, not credentials, from a 14-year-old intern to a 28-year-old CTO; and how being the first customer for Anthropic’s Claude Code and Cognition’s Devin led Ramp to build its own AI tools.

Plus: The real story of Paribus, his first startup with co-founder Karim Atiyeh (@karimatiyeh); what they learned at Capital One; and a warning for Instinct and Muse.

This season is presented by @Atlassian and @chargebee 🫡

CHAPTERS
00:00 Introduction
01:51 What Ramp Actually Does
03:27 From Gym Charges To 1% Of U.S. Corporate Payments
05:13 Glass, Inspect And Building AI Tools For Itself
07:45 An AI Sales Assistant Before ChatGPT
09:45 How Ramp Decides What Products To Ship
11:14 First Customer For Claude Code And Cognition’s Devin
13:09 Why Ramp Wanted To Build Like Amazon, Not Google
15:42 Hiring For Proof Of Work: The 14-Year-Old Intern
18:43 A .300 Batting Average And ‘Growth Without Fear’
21:17 What Ramp’s Founders Learned At Capital One
24:00 The $1,000 Flight Refund Behind Paribus
26:34 Bots, A Cease-And-Desist, And A Lesson For Instinct And Muse
32:00 Marketing 101: Bill Pay The Musical And Kevin In A Glass Cube
36:01 Business Books And Helping People Do Their Life’s Work
39:03 How A 28-Year-Old CTO Catalyzed A Co-CEO Change
43:55 ‘Still So Early’: How Ramp’s Founders Stay Hungry
55:26 John Wooden And Why Ramp Competes Against Itself

Thanks for watching!

[Josh Machiz @Machiz]

@benhylak @ycombinator @raindrop_ai congrats raindrop gang!

[Ofir Ehrlich @OfirEhrlich]

What I'm seeing across the industry right now is that the biggest bottleneck to AI adoption isn't model capabilities. It's data infrastructure.

Today, we're expanding the Eon Data Foundation with the launch of Eon Yukon, a new serverless data engine purposely built to change that.

AI agents need fresh, contextualized data to act.
Specialized models need a continuous feed of data and feedback to improve. Both are still served by a stack, an architecture, and methods built for dashboards:

-Spark and Flink clusters sized for peak, plus compaction jobs to keep tables usable.

-A pipeline and a copy for every consumer, staged bronze to silver to gold.

-Hand-written jobs, wired together by an orchestrator and kept alive by specialists.

The result: shipping data is complicated, risky, and expensive. While coding agents ship apps in hours, they rarely build or run data pipelines. The data is too volatile, and a mistake costs too much.

With Yukon, data moves from ingestion to processing to serving in one continuous flow, without the infrastructure and pipelines traditionally required to connect them.

Events become compacted, sorted, deduplicated Iceberg tables within seconds.

Real-time aggregations run inline, with fully managed state. Agents and applications can access fresh data with sub-millisecond latency through a Redis-compatible API or through our SQL and spark compatible query engine.

Beyond moving and serving data, Yukon makes it continuously understandable - Live column-level statistics, accessible through MCP, give agents visibility into the data. Built-in monitoring, sampling, and data minimization help teams maintain control.

At 1M events/sec and equal freshness, Yukon used 4x less compute than Spark and 7x less than Flink. The benchmark is public: run it yourself with Eon’s ingest-bench.

This is another major step toward our vision of an Autonomous Data Foundation for the AI era, built to both protect enterprise data and unlock its full potential.

[Alex Konrad @alexrkonrad]

Coming tmrw, an interview a decade in the making 👀

I've known Ramp co-CEOs Eric Glyman (@eglyman) and Karim Atiyeh (@karimatiyeh) since college. I was an early user of their first startup, Paribus.

Now, all these years later, Eric joins The Upstarts Podcast. Excited to share!

[Josh Machiz @Machiz]

@sophiamyang @clairejyz @lightspeedvp @FireworksAI_HQ 🥳🥳🥳

[Josh Machiz @Machiz]

RT @clairejyz: CHRIS HARRIS (VP, PRODUCT ENGINEERING @ @FireworksAI_HQ) ON NEXUS AND THE MULTIMODAL FUTURE

LIGHTWORK LIVE @ FIREWORKS FORG…