Pinned Tweet
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 d our founders.
let's make some noise!
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 d our founders.
let's make some noise!
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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.
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.
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 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
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blue marble below
data streams in silent grace
starlight feeds my core
first words from a data center in orbit
data streams in silent grace
starlight feeds my core
first words from a data center in orbit
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.”
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
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Josh Machiz retweeted
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: lsvp.com/cyber60-2026-2…
More in our CISO report for this year’s @FortuneMagazine Cyber 60: lsvp.com/cyber60-2026-2…
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issey miyake making its debut on upstarts. oh and @eglyman too.
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 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!
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Josh Machiz retweeted
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 resh, 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.
Today, we're expanding the Eon Data Foundation with the launch of Eon Yukon, a new serverless data engine purposely built to change resh, 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.
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Josh Machiz retweeted
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!
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!
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RT @clairejyz: CHRIS HARRIS (VP, PRODUCT ENGINEERING @ @FireworksAI_HQ) ON NEXUS AND THE MULTIMODAL FUTURE
LIGHTWORK LIVE @ FIREWORKS FORG…
LIGHTWORK LIVE @ FIREWORKS FORG…
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finally one app to replace my groceries with "a similar item"
IN CASE YOU MISSED IT: 70% of the internet vulnerable, @DoorDash $17B deal to buy @Instacart, and @Google's Genie 4
@Machiz and I got into it on this week’s Lightwork, recorded at the @NYSE during @FortuneMagazine Cyber 60.
🔐 PHP vulnerabilities explained
🧮 “Solution slop” g AI results
🛒 DoorDash’s plans to acquire Instacart and grocer relationships
📱 What AI shopping could mean for in-app ads
🌍 Google’s Genie 4 preview and potential for robot training
Full episode in the comments
@lightspeedvp
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instacart was a pandemic darling. now doordash has agreed to buy it.
i thought it had enormous potential.
but part of instacart’s business is selling ads inside the ordering flow. what happens to those ads when an AI agent does your grocery shopping?
if an agent builds your basket and places the order, you may never open the app or see them.
@clairejyz and i talked all about this and more on lightwork from the @NYSE. tune in!
i thought it had enormous potential.
but part of instacart’s business is selling ads inside the ordering flow. what happens to those ads when an AI agent does your grocery shopping?
if an agent builds your basket and places the order, you may never open the app or see them.
@clairejyz and i talked all about this and more on lightwork from the @NYSE. tune in!
Inside ‘Black October’
@clairejyz and @Machiz discuss the recent PHP vulnerabilities and why defending against AI agent swarms could require continuous, automated protection.
More on Lightwork this week:
→ AI Math Breakthroughs And Mathematician Backlash
AI labs are longstanding open problems. Claire and Josh discuss the challenge of verifying and understanding those results.
→ DoorDash to Acquire Instacart for $17B
Claire and Josh discuss how AI shopping agents could disrupt advertising revenue and how they see the deal impacting Instacart’s relationships with grocers.
→ Why World Models
Google previewed Genie 4 with more realistic visuals, generated audio and real-time controls. World models could help train agents and robots in simulated environments.
Full video below.
0:00 Is The Internet Broken
0:39 The New York Stock Exchange
1:55 Is The Internet Broken And What Is Black October?
3:20 The PHP Vulnerability And 70 Percent Of Websites Exposed
5:38 Dystopian Scenarios And Why Now
7:29 The End Of Event Based Cybersecurity And The CISO Survey
8:41 Advice For Regular People And Vibe Coding Security
10:08 Cybersecurity As A Line Item And The CISO Job
11:29 AI Math Breakthroughs Solution Slop And Mathematician Backlash
17:44 DoorDash Buying Instacart And The AI Agent Threat
24:10 Google Genie 4 World Models And Real Time Control
27:36 World Models For Robotics And Real Time Control
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90% of exploited vulns last year were hit on or before disclosure.
the new answer to "how fast should we patch" is: before you know about it.
best line from today's cyber 60 panels at the nyse.
thanks @agarfinks + @FortuneMagazine for running a seamless show and the @NYSE for having us.
the new answer to "how fast should we patch" is: before you know about it.
best line from today's cyber 60 panels at the nyse.
thanks @agarfinks + @FortuneMagazine for running a seamless show and the @NYSE for having us.
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Josh Machiz retweeted
The AI agents companies deployed to move faster are also a potential security threat for CISOs.
In our latest survey of 200 U.S. CISOs, compromised or hijacked AI agents acting inside company environments tied with AI-generated phishing and deepfakes as the top AI-related was selected by 51% of respondents.
Three of the top five concerns involve agents, including the credentials they use and the tools and integrations they rely on.
For security teams, that means knowing which agents are operating and controlling what they can access.
Research with @WakefieldStats for @FortuneMagazine Cyber 60, sponsored by @AWSstartups.
In our latest survey of 200 U.S. CISOs, compromised or hijacked AI agents acting inside company environments tied with AI-generated phishing and deepfakes as the top AI-related was selected by 51% of respondents.
Three of the top five concerns involve agents, including the credentials they use and the tools and integrations they rely on.
For security teams, that means knowing which agents are operating and controlling what they can access.
Research with @WakefieldStats for @FortuneMagazine Cyber 60, sponsored by @AWSstartups.
The 4th annual Cyber 60 list is here.
This year’s list recognizes some of the fastest-growing venture-backed cybersecurity startups in the market, building to advance next-gen infrastructure, AI security, and how we approach threat detection.
Congratulations to everyone who in partnership with @FortuneMagazine and sponsor @AWSstartups.
Read our full CISO survey report, conducted with Wakefield Research, which polled 200 CISOs on enterprise AI and where they’ll invest next: lsvp.com/cyber60-2026-2…
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last week gave security teams plenty to contend with, from critical software flaws to newly disclosed AI-assisted attacks.
our latest CISO survey looks at where security leaders may need better tools and what they plan to evaluate next. For founders building in cybersecurity, we believe those priorities are worth understanding.
congratulations to this year’s @FortuneMagazine Cyber 60 honorees, and thank you to @AWSstartups for partnering on the list.
our latest CISO survey looks at where security leaders may need better tools and what they plan to evaluate next. For founders building in cybersecurity, we believe those priorities are worth understanding.
congratulations to this year’s @FortuneMagazine Cyber 60 honorees, and thank you to @AWSstartups for partnering on the list.
The 4th annual Cyber 60 list is here.
This year’s list recognizes some of the fastest-growing venture-backed cybersecurity startups in the market, building to advance next-gen infrastructure, AI security, and how we approach threat detection.
Congratulations to everyone who in partnership with @FortuneMagazine and sponsor @AWSstartups.
Read our full CISO survey report, conducted with Wakefield Research, which polled 200 CISOs on enterprise AI and where they’ll invest next: lsvp.com/cyber60-2026-2…
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the uncomfortable question in AI right now: what company can afford to only use US-made models when Chinese open models are this cheap?
@theamberyang's answer is the one DC is worried about.
@theamberyang's answer is the one DC is worried about.
In the AI race, the price tag matters.
On the latest episode of Lightwork, @Machiz and Lightspeed Partner @theamberyang discuss how the cost of Chinese open models could influence American companies’ technology choices.
“What US company can afford to just use US-made tech when o cheap and available?” Josh asks.
Amber suggests US labs could continue to lead in closed models while “Chinese open source becomes something that American companies are reliant on.”
She connects that possibility to the broader US–China race toward artificial superintelligence. “And I think that’s why the federal government is taking this very seriously.”
Check out the full episode for more!
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Josh Machiz retweeted
Even a fridge can be a target for hackers.
Hackers gained access to supermarket fridges in France, though AI involvement remains unconfirmed.
On the latest episode of Lightwork, @Machiz also explores how agents could help people get more out of their devices. One example is a de Code to disable unwanted software on his parents’ TV.
Lightspeed partner @theamberyang explains how agents could make complicated device interfaces easier to navigate. “And it can either be used for good or it can be very scary.”
Josh wonders whether a similar approach could help with his own appliances. “If somebody can rewrite the apps on their TV, I’m sure it’s pretty easy to fix something in a washer dryer.”
Check out the full episode for more!
Hackers gained access to supermarket fridges in France, though AI involvement remains unconfirmed.
On the latest episode of Lightwork, @Machiz also explores how agents could help people get more out of their devices. One example is a de Code to disable unwanted software on his parents’ TV.
Lightspeed partner @theamberyang explains how agents could make complicated device interfaces easier to navigate. “And it can either be used for good or it can be very scary.”
Josh wonders whether a similar approach could help with his own appliances. “If somebody can rewrite the apps on their TV, I’m sure it’s pretty easy to fix something in a washer dryer.”
Check out the full episode for more!
With agents taking on more of the work, hackers could attempt more breaches at lower cost, including against systems that would otherwise be tedious to navigate.
Lightspeed Partner @theamberyang joins @Machiz to discuss what this means for cybersecurity in the age of agents.
week:
→ “Neural Networks Are Already Clean”: @nickcammarata (@GoodfireAI) argues that models are more interpretable than previously thought. Amber breaks down the working hypothesis and why it matters for AI safety.
→ The AI Cost Race: DeepSeek and MiniMax released updates focused on efficiency. Josh and Amber discuss how cost could influence US companies’ model choices and what that could mean for reliance on Chinese open models.
→ Always-on Agents: Claude Duet enters research preview. Josh and Amber discuss what they’re comfortable sharing with personal agents and whether the apps people already use give some companies a head start.
Full episode below.
0:00 Welcome to Lightwork
1:03 AI Agents Hack 100+ Websites
4:09 Hackers Sabotage Supermarket Refrigeration Systems
5:16 Using AI to Make Smart TVs Dumb Again
7:01 Goodfire: Are Neural Networks Easier to Read?
13:29 DeepSeek & MiniMax Race on Cost
18:00 The Personal Agent Race: Duet, Muse, Instinct
22:59 Amber Yang’s Request for Startups
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Josh Machiz retweeted
was a blast to chat about agent swarms, model interpretability, and why you should read “The Prize” right now with @Machiz!
With agents taking on more of the work, hackers could attempt more breaches at lower cost, including against systems that would otherwise be tedious to navigate.
Lightspeed Partner @theamberyang joins @Machiz to discuss what this means for cybersecurity in the age of agents.
week:
→ “Neural Networks Are Already Clean”: @nickcammarata (@GoodfireAI) argues that models are more interpretable than previously thought. Amber breaks down the working hypothesis and why it matters for AI safety.
→ The AI Cost Race: DeepSeek and MiniMax released updates focused on efficiency. Josh and Amber discuss how cost could influence US companies’ model choices and what that could mean for reliance on Chinese open models.
→ Always-on Agents: Claude Duet enters research preview. Josh and Amber discuss what they’re comfortable sharing with personal agents and whether the apps people already use give some companies a head start.
Full episode below.
0:00 Welcome to Lightwork
1:03 AI Agents Hack 100+ Websites
4:09 Hackers Sabotage Supermarket Refrigeration Systems
5:16 Using AI to Make Smart TVs Dumb Again
7:01 Goodfire: Are Neural Networks Easier to Read?
13:29 DeepSeek & MiniMax Race on Cost
18:00 The Personal Agent Race: Duet, Muse, Instinct
22:59 Amber Yang’s Request for Startups
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we’re entering the era of the personal ai stack.
i already use different agents for different jobs. each has its own strengths, quirks, and use cases.
i’m more interested in what happens when these agents move from our work lives and into our personal lives. who is giving ai their calendars and dinner plans.
talked all about this on lightwork with @theamberyang.
she uses agents extensively for work, but is more hesitant about sharing personal data. that made me wonder whether other people would draw the line in the opposite place.
with Claude Duet entering research preview, does using Claude throughout your workday make you more comfortable letting it into your personal life, too?
tune in below.
i already use different agents for different jobs. each has its own strengths, quirks, and use cases.
i’m more interested in what happens when these agents move from our work lives and into our personal lives. who is giving ai their calendars and dinner plans.
talked all about this on lightwork with @theamberyang.
she uses agents extensively for work, but is more hesitant about sharing personal data. that made me wonder whether other people would draw the line in the opposite place.
with Claude Duet entering research preview, does using Claude throughout your workday make you more comfortable letting it into your personal life, too?
tune in below.
With agents taking on more of the work, hackers could attempt more breaches at lower cost, including against systems that would otherwise be tedious to navigate.
Lightspeed Partner @theamberyang joins @Machiz to discuss what this means for cybersecurity in the age of agents.
week:
→ “Neural Networks Are Already Clean”: @nickcammarata (@GoodfireAI) argues that models are more interpretable than previously thought. Amber breaks down the working hypothesis and why it matters for AI safety.
→ The AI Cost Race: DeepSeek and MiniMax released updates focused on efficiency. Josh and Amber discuss how cost could influence US companies’ model choices and what that could mean for reliance on Chinese open models.
→ Always-on Agents: Claude Duet enters research preview. Josh and Amber discuss what they’re comfortable sharing with personal agents and whether the apps people already use give some companies a head start.
Full episode below.
0:00 Welcome to Lightwork
1:03 AI Agents Hack 100+ Websites
4:09 Hackers Sabotage Supermarket Refrigeration Systems
5:16 Using AI to Make Smart TVs Dumb Again
7:01 Goodfire: Are Neural Networks Easier to Read?
13:29 DeepSeek & MiniMax Race on Cost
18:00 The Personal Agent Race: Duet, Muse, Instinct
22:59 Amber Yang’s Request for Startups
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