John Eng (Right Side Capital)

The promise of America has always been opportunity. America turns 250 this year. One way to honor that is investing in the people who support those who serve. Instant Teams (our portco) is bringing together 25 companies to create 250 military spouse hires with #Mission250 A steady homefront makes a safer front line 25 companies. 250 hires. One mission. Learn more: lnkd.in/gYuQK_aZ #Mission250 #MilitarySpouseEmployment #FutureOfWork

@Chrismccann @Rick_Zullo Absolutely seeing this as well. Where did all the term sheets go in Seed and Series A this year?

The current state of raising Series A rounds in silicon valley: x.com/i/article/2080…

We’ve raised over $100M to create AI customer support that doesn’t sound like AI slop. Introducing Gorgias AI Agent 3.0: the first AI customer support that’s better than any human. How it works 👇

We’re writing 150+ checks this year. First check only. B2B SaaS / AI / Marketplaces. Live product. $5K–$30K MRR. US and Canada HQ'd corps

The pendulum will swing back to a more reasonable position. But when?

If you thought the value of the AI ecosystem was going to only accrue to a few companies, the past few months have represented a turning point in what the future of AI might look like. It's clear that there's going to be incredible innovation and growth coming out of the frontier AI labs, and they will continue to push the limits of what model progress looks like. They have the scale of compute, large revenue streams and customer bases, incredible researchers, large data pipelines, and more, to continue to stay at the forefront. And at the same time, there’s an amazing ecosystem starting to play out to diffuse AI into the real world, taking on a variety of approaches that build on top of these frontier models or offer alternative visions that can credibly work as well. Here are just a few of the categories that seem to be working right now: * There’s an ecosystem of companies that will help enterprises and applied AI companies develop their own models tuned for specific use cases, and run the inference for them. This can drive additional performance gains and cost effective approaches to getting AI into workloads across different domains. * Applied AI companies that are delivering the end-user experience and business process tools necessary to actually driving enterprise adoption - including legal, IT, security, HR, customer support, coding, and more. These companies can work with any model and act as a routing layer, and deeply understand the enterprise workflow, can drive change management, actually get to the data necessary to work with, and more. * New labs are emerging that go deep in particular domains that are either not the focus areas of frontier labs or require a deep level of vertical expertise to stay ahead. Life sciences, financial services, healthcare, and more all have labs that will be able to bring completely new approaches to large enterprises across the economy. * There’s all new infrastructure emerging to run models and agents effectively, protect and govern how they operate, store and secure the data they work with, and help enterprises orchestrate their activities. Multiple layers of this stack all are being built up right now that will help drive enterprise adoption. * New services firms that can actually drive the change management in enterprises necessary to the diffusion of AI. There will be hundreds or even thousands of new firms that emerge in lines of business or specific industries that can enable agents to be adopted. I’m probably even missing a few categories, but this is what an incredible healthy technology ecosystem looks like. Way too early to call the winning architectures, and in reality it’s going to be a heterogenous environment as all other tech markets have become. Very exciting.

You're raising a round. 📈 We're writing checks. ✍️ Let's meet. 🤝

How much GMV do I need to raise my next round? I hear this question all the time from marketplace founders, so I wanted to set out and answer it. Using data from @SiliconVlyBank for general startups in 2024/2025, so not marketplace-specific, unfortunately. I built out implied GMV ranges, which feel fairly accurate and directional to back out to the median revenue to raise the next round. Note this is the median revenue of startups that raised rounds. Obviously lots of variance within this. Let me know if this matches your experience! I hope it's helpful.

2x the hype for @ClayHRInc! Gartner named them in two 2026 Hype Cycles — HR Technology and AI in HR. That's 20+ Gartner mentions now. Most HR systems record what happened. ClayHR surfaces what should happen next. Proud investors. Congrats, team! 🎉

Jobs jobs jobs

@Prime_Asthetic Shunbun spring drive of course

Wrist game:- Which one is you prefer ?

Seattle continues to confound me. So much raw technical talent yet so few startups.

founders think investors absorb every slide and decide at the end. they don't. you get their attention at the start. then you have to keep earning it back. a pitch isn't a data dump. it's a constant battle to keep them in the room.

@tokifyi Vancouver

starting a whatsapp group for vancouver founders 🇨🇦 somewhere to chat, share what you're building, help each other out + maybe do a founders happy hour irl 👀 comment “vancouver” if you want in

Dan Koe casually explained why some founders command a room in ONE sentence than others do across an entire meeting. Full masterclass 👇

@Jason Iced coffee? Sacre bleu!

Another perfect day in Paris

@ColinGardiner I have lots of deal flow for you

Starting a contrarian VC firm that only invests in marketplaces...

@Trace_Cohen I’d say how fast the founder learns and discerns

The biggest advantage of being a startup is not speed. It is the ability to change your mind without asking permission.

At 11k employees, our AI costs are going up. Which model & harness should we use to lower cost but also retain great quality? We didn't want to blindly trust public benchmarks. So we ran a comprehensive evaluation on our tasks, code base, infra. It's been produced by more than 3,000 software engineers, spans 3 hyperscalar clouds and many languages and tasks. The results are surprising. We find that for the SAME mdoel, the choice of harness can significantly save costs (~2x). We also find that GLM 5.2 performs extremely well. We run Omnigent in front of these and can easily multiplex different harnesses and models for different tasks. Check it out: databricks.com/blog/benchmark…