SETH LEVINE's VC ADVENTURE

How Much Should We Tell People When We Use AI?

Sun, 19 Apr 2026 09:00:00 +0000

I use AI a lot. If you’ve been reading this blog, you know that already. A few weeks ago, I wrote about running my business from a terminal window. That post doesn’t do justice to just how much of my business life is managed through iTerm and Claude. And, of course, Brad has been writing about building CompanyOS - the system that powers a lot of this. I’m all in on this stuff, and I’m not subtle about it.

But something happened recently that made me think about an aspect of AI use that I hadn’t considered carefully enough.

The Deck Review

Like most VCs, I get ten or more new pitches a day. The vast majority aren’t a fit, and I try to send a kind note back when they’re not. For the ones where someone asks me for feedback on their deck - which happens more often than you’d think - I used to have two choices. Spend 30 minutes writing detailed feedback, which I rarely had time for. Or send back a “thanks but I don’t have the bandwidth” note, which felt dismissive when someone was genuinely asking for help.

So I built a third option. I trained a skill inside CompanyOS specifically for reviewing pitch decks. It’s not a generic “summarize this PDF” prompt. I fed it my feedback on a few dozen actual decks, taught it what I look for as an investor - narrative clarity, whether the metrics are stage-appropriate, how the financial model holds together, what questions I’d ask in a partner meeting. When I run a deck through it, the output reflects how I actually think about these things. It’s me, at scale.

A few weeks ago, someone asked me for feedback on their deck after I’d passed on the deal. I ran it through the skill, reviewed the output, made a few edits, and sent it along. Substantive stuff - what was working, what wasn’t, specific suggestions on a few slides.

The response I got back was unexpected. The founder told me he’d run my email through an AI detection tool. It flagged the message as likely AI-generated. And he asked me point blank - is this actually your opinion, or did AI write this?

The Irony

It was a valid question (and he was correct - as I explain below, I heavily relited on AI in my response to him). But here’s the thing that made me laugh. He zeroed in on one specific paragraph about a slide that needed work. That paragraph was one I’d written myself. I’d noticed the slide when I was screenshotting the deck to load into Claude (it can’t read Docsends, sadly) and added my own note about it. The rest - the AI-generated part - he didn’t focus on (although I’m sure that’s in part what caused the note to be flagged by whatever AI detection tool he used).

But his broader question was fair. And it stuck with me.

I wrote him back and explained what I’d done. That yes, AI drafted the initial feedback. That I’d spent significant time training the skill on my actual thinking. That I review and edit everything before sending. And that a year ago, his alternative wouldn’t have been a hand-crafted email from me - it would have been no substantive response at all.

The Podcast Conversation

The next day I was on a podcast with George Samuels, who a a deep thinker about the intersection of AI and human behavior, and I told him this story. We ended up having a long conversation about something that I think a lot of people are navigating right now - the etiquette of AI in professional communication.

George’s suggestion was simple: just be open about it. Not in an apologetic way. Not with a disclaimer that undermines the content. But with a straightforward acknowledgment - something like “I used AI to help draft this, but I’ve trained it on how I actually think about this topic and I review everything before sending.”

That framing resonated with me because it addresses the real concern head-on. People aren’t worried about AI because it’s AI. They’re worried that what they’re reading doesn’t represent the thinking of the person who sent it. That it’s generic filler, not real engagement. Transparency solves that problem. It says: yes, AI was involved, and here’s why you should trust the output anyway.

The Other Side

Around the same time, I had an exchange with my _Capital Evolution _co-author Elizabeth that got at this from the opposite direction. She asked me if I had been using AI to respond to her in our interactions (she was worried that I had) and flagged a specific text conversation where she thought my responses felt AI-generated. They weren’t. But more than that one exchange, she told me she’d started to wonder more generally whether it was always me on the other end of our conversations.

It was more of a feeling but one that I think is quite valid - in this case not by my actual responses to her, but to an world in which people are increasingly using AI to aid (or in some cases substitute for) their work. This is especially concerning to Elizabeth, who works in an industry that’s being significantly disrupted by AI (by some estimates 50% of content is now being generated by AI), and I think that sensitivity was coloring how she read everything I sent. When you know someone is deep into AI tools, it’s natural to start questioning whether any given message is “really them.”

This is another argument for being explicit about when you are and aren’t using AI. If you’re transparent when AI is involved, it’s easier for people to trust that the times you don’t flag it are genuinely just you. Without that transparency, the suspicion bleeds into everything - even the stuff that’s 100% human. It’s also a reminder that AI is best used in many instances as an aid and not a substitute. Context matters, of course. But so does taking the time to make sure that things that go out under your name (this blog, as an exmple - which was drafted by Claude but then significantly revised by me) actually reflect YOU.

What I Actually Think

Here’s where I’ve landed on this.

I’m not going to stop using AI (obviously). The deck review example is a good one - the founder got substantive, personalized feedback on his pitch that reflected 25 years of looking at investment opportunities. His alternative wasn’t a better version of that email written by hand. His alternative was a polite brush-off.

But I am going to start being more explicit about it. Not as an apology. As context. The same way one might say: “I had my analyst pull these numbers” or “my team put this deck together” - because of course you did, and that doesn’t diminish the substance.

The distinction that matters isn’t whether AI was involved. It’s whether the output represents real thinking or is generic fill. I’ve spent real time building and training these tools to reflect how I actually approach problems. That’s meaningfully different from pasting something into ChatGPT and forwarding whatever comes back.

I think we’re heading toward a world where AI involvement in professional communication is assumed, and the interesting question becomes not “did you use AI” but “how well did you use it.” We’re not there yet - clearly, based on the reactions I’m getting. In the meantime, transparency seems like the right bridge.

The AI Gap Between Startups and Enterprise is Larger Than You Think

Sat, 28 Mar 2026 10:00:00 +0000

There’s no shortage of hype around AI right now. Every pitch deck mentions it. Every company claims to be using it. Every conference has a keynote about how it’s going to change everything. Plenty of hype, but also plenty of real work getting done daily by those truly leveraging the power of AI tools.

Like every venture firm, we’ve been deep in this at Foundry. We’ve been working with our portfolio companies on how they’re integrating AI into their products, their workflows, and their operations. It’s been a major area of collaboration and learning across our portfolio. And what we’re seeing across startups (at Foundry and beyond) - not in the hype but in the actual numbers - is a striking contrast between AI adoption in startups and what’s happening in large enterprises.

Is AI the startup superpower? At the moment it appears to be so.

The Enterprise Struggle Is Real

The data on enterprise AI adoption is stark. A widely cited MIT study from 2025 found that roughly 95% of enterprise AI pilots fail to deliver measurable ROI. That specific number has been debated - the sample was small, and the definition of success was narrow - but even the more conservative research tells a similar story. RAND found that over 80% of AI projects fail, which is twice the failure rate of non-AI IT projects. BCG reported that 74% of companies have seen no tangible value from their AI investments, and only 4% have achieved enterprise-wide AI capabilities. S&P Global found that 42% of companies abandoned most of their AI initiatives in 2025 - up from 17% the year before.

The reasons are consistent across every study. Enterprises pick the wrong problems to solve with AI - choosing projects based on technical novelty instead of business value. Their data is siloed and messy. Projects that work in sandboxes stall when they hit the reality of compliance workflows, authentication systems, and organizational processes that weren’t designed for this. And critically, they try to bolt AI onto existing processes rather than rethinking how work gets done. That rarely works.

None of this is surprising if you’ve watched large organizations try to adopt any transformative technology. What makes this moment different is the other side of the equation.

Startups Are Running With It

This is where it gets interesting. The top AI-native startups now average $3.48 million in revenue per employee - roughly 5.7 times higher than leading traditional SaaS companies. Cursor, the AI code editor, hit $300 million in ARR with about 20 employees. Lovable reached $17 million in ARR with 15 people, three months after launch. And across the board - if Foundry’s portfolio is representative (and I believe that it is) - startups of all kinds are seeing massive leverage from their AI investments.

Nimbleness Has Always Been a Superpower. AI Amplifies It.

Startups have always had the edge when it comes to speed and adaptability. Fewer layers of approval. No legacy systems to work around. A willingness to rethink how things are done rather than protecting how things have always been done. That’s been the case through every technology cycle I’ve watched over 20+ years in venture.

What’s different about AI is how dramatically it amplifies that advantage.

AI doesn’t reward scale the way traditional software does. You don’t need a large engineering team to build a product. You don’t need a large marketing or go-to-market team to sell. You don’t need layers of management. Individuals and small teams can be as productive as entire divisions of larger companies. Powerful models are available to everyone willing to take the time to learn how to leverage them. The APIs are the same whether you’re a Fortune 500 company or three people in a coffee shop. What matters is how quickly you can integrate AI into your workflow, how willing you are to rethink how work gets done, and how fast you can iterate. Startups win on all three counts.

Enterprises, meanwhile, are stuck in what I’d call pilot purgatory. They run a proof of concept, it shows promise, and then it dies in the gap between “this demo looks great” and “this is now part of how we actually operate.” That MIT I cite above tells an important part of the story - companies are choosing projects based on technical novelty rather than business value. They’re solving interesting problems instead of important ones.

The World Economic Forum noted that AI is making resources that were once exclusive to large companies - advanced technology, specialized expertise, sophisticated analysis - broadly accessible to small teams. OECD data shows AI uptake in small firms grew 72% in a single year, with 91% of adopters reporting efficiency gains. Mid-market companies are getting to value in 6 to 9 months, while large enterprises take 12 to 18.

In previous technology cycles - cloud, mobile, SaaS - startups had advantages, but large companies could eventually catch up by throwing resources at the problem. With AI, the traditional advantages of large companies - vast data, deep pockets, big teams - seem to matter less (or, at least, most seem unable to harness these to their advantage). In some cases they’re actually liabilities, because they come with organizational complexity that makes it harder to adapt.

What We’re Watching

The companies in our portfolio that use AI most effectively aren’t treating it as a bolt-on feature. They’re using it as an operating model. They’re building companies that are structurally leaner and faster than what came before - generating more value per person than would have been possible even a few years ago.

That’s not hype. That’s what’s actually happening on the ground, in real companies, with real revenue. And it’s happening while many of their larger competitors are still trying to get their pilots to work.

Startups have always punched above their weight. AI is making the punch a lot harder.

Capital Evolution - T+3 Month Check-In

Sun, 22 Mar 2026 10:00:00 +0000

It’s been about a quarter since Capital Evolution hit the shelves, and I thought it would be worth stepping back to take stock of what Elizabeth and I have been doing to get the book’s ideas out into the world. It’s felt like a lot — and seeing it all written down confirms that is indeed the case!

From the start, our goal was to make sure that the ideas in the book — Dynamic Capitalism and the concepts around it — didn’t stay on the page. We wanted to get them in front of as many people as possible: business leaders, policymakers, students, investors, and anyone thinking seriously about the future of the American economy.

In Print and Online

The conversation around Capital Evolution has shown up in some incredible places. We wrote an op-ed for The New York Times arguing that copying China’s economic playbook is a terrible idea for the U.S. We had a piece in the Los Angeles Times with Congressman Ro Khanna on reviving America’s innovation economy. Forbes featured our framework for global leadership in a system of Dynamic Capitalism. The International Economy published our essay “The Frankenstein Economy,” which distills the book’s core argument about how neoliberalism’s creation has turned on its creator. And we’ve been covered in Fortune, Inc., the Washington Independent Review of Books, and many others.

On the Air

We’ve appeared on dozens and dozens of podcasts (full list here), reaching audiences across business, technology, investing, and public policy. Highlights include the Conscious Millionaire Show, Michael Covel’s Trend Following, School for Startups Radio, Keen on America with Andrew Keen, and The Full Ratchet. Each conversation has been different — some focused on venture capital and AI, others on inequality and the middle class — but they all come back to the same question: how do we build an economy that works for more people?

In Person

In-person events, while more time-consuming and often involving travel, have been by far the most rewarding part of our book-promotion efforts. We kicked things off with a launch party at my wife’s bookstore, Composition Shop, that set the tone for everything that followed. We packed the house at Politics & Prose in Washington, D.C., with over 120 people for a conversation that could have gone on all night (special thanks to Lori Montgomery, former Politics editor for The Washington Post for moderating). There were many others as well, including a memorable event at the Tattered Cover here in Denver (I spent many days at Tattered as a kid - it was special to be back to host an author event there). Those were only two of a bunch of bookstore events we’ve hosted since launching. And we’ve done a series of corporate events and private discussions with organizations including JP Morgan, KPMG, Cooley, CapRock, MichaelBest, CU Boulder, and the Colorado School of Mines — conversations with executives, investors, attorneys, and students who are grappling with questions related to the future of our economy every day. These events spurred not just interesting discussions, but also pushed us to think more deeply about our work, challenge our ideas, and provided the opportunity for others to take up the mantle of Dynamic Capitalism.

Looking Ahead

The ideas in the book seem very durable, and we continue to have events scheduled as questions around the future of capitalism remain extremely relevant in today’s political and social environment. The conversations we’re having aren’t partisan. They’re practical. And they give me real optimism that the evolution we describe in the book is already underway.

Migrating My Blog from WordPress to Hugo

Tue, 10 Mar 2026 12:00:00 +0000

I’ve been blogging on WordPress for close to twenty years. It’s been a great platform, but I’d been thinking about a change for a while. WordPress had gotten heavier over the years - more plugins, more updates, more things to manage. I wanted something simpler. Something where I could just write in a text file and push it live.

Brad recently migrated his blog - Feld Thoughts - from WordPress to Hugo and that was the kick I needed to finally do this. He gave me quite a few tips and pointers that were incredibly helpful. Seeing him go through the process gave me the confidence that this was both doable and worth doing.

Why Hugo

Hugo is a static site generator. You write posts in Markdown, run a build command, and out comes a set of plain HTML files. No database, no PHP, no server-side processing. Just files.

That simplicity is the whole point. My blog doesn’t need a content management system running on a server somewhere. It needs to turn my writing into web pages. Hugo does that extremely well, and it does it fast. I also like the simplicty of the template I choose - the focus is more about the content (but still with a place to include my books and other stuff I’m working on).

The Stack

Here’s what the new setup looks like:

Hugo generates the site from Markdown files
GitHub stores the source code and content
Vercel builds and hosts the site - auto-deploys every time I push to GitHub
Cloudflare handles DNS and CDN
Kit (formerly ConvertKit) handles email subscriptions via RSS

Publishing a new post is literally: write a Markdown file, commit it, push to GitHub. Vercel picks up the change and deploys in about 30 seconds. No logging into an admin panel. No clicking through menus. Just text files and version control.

Choosing and Customizing a Theme

I went with the Notepadium theme as a starting point. It’s clean and minimal - exactly the aesthetic I wanted. But “starting point” is the key phrase. I ended up doing extensive customization.

I worked with Claude to adjust the CSS, tweak the layout, get the typography right, and handle things like dark mode support, pagination, and how lead images display. We went back and forth on the design until it felt right. The beauty of Hugo themes is that you can override any template file by placing your own version in the layouts/ directory, so customization doesn’t mean forking the theme.

The Migration Process

Exporting from WordPress

The first step was getting all my content out of WordPress. I used the standard WordPress export tool, which gives you a big XML file with all your posts, pages, and metadata. From there I used a conversion tool that I had Claude create for me to turn those into Markdown files with the right Hugo frontmatter.

URL Mapping

This was critical. I’ve got almost two decades of posts, and there are links all over the internet pointing to them. I needed every old URL to keep working.

WordPress uses a URL structure like /archives/2024/05/some-post-title.html. I configured Hugo to use the same pattern, so every existing link still resolves to the right post. No redirects needed - the URLs just work. (side note - let me know if you find any broken links that I missed!)

Media Library

WordPress stores images and other media in its own upload directory structure. I had to port all of that over, making sure image paths in posts pointed to the right locations. This took some patience - going through posts, finding broken images, and fixing paths. We adjusted the media directory structure to work cleanly with Hugo’s static file handling.

One thing Hugo doesn’t give you out of the box is search. WordPress has search built in. I had to think about whether I needed it and, if so, how to implement it. Same with things like category pages, archive views, and pagination. Each of these required some template work.

The Email Subscription Migration

This was honestly the trickiest part.

I use Kit for bulk email for most of my work, but my blog was connected to MailChimp. I wanted to change that. The idea is simple - when I publish a new post, it shows up in my RSS feed, Kit detects it, and sends an email to subscribers with the post content. Simple in theory.

In practice, we ran into a few issues.

The Missing Content Field

Kit expects the full post body to come through in a specific RSS field called content:encoded. Hugo’s default RSS template only includes a description field with the post summary. So my first test emails went out with just a snippet instead of the full post.

The fix was creating a custom RSS template that includes content:encoded with the full post content. Straightforward once we figured out what Kit was looking for.

There were a bunch of other challenges as well. I spent more time troubleshooting this part of the migration than anything else.

The XML Parsing Bug

This one was sneaky. After adding the content:encoded field, we initially wrapped the content in CDATA tags - which is a standard XML technique for including HTML content. It looked correct. But it turns out that if your post content happens to contain the character sequence ]]> anywhere, it breaks the CDATA wrapper and produces invalid XML.

The RSS feed would load fine in a browser but fail for automated parsers. Kit silently failed to pick up new posts. We burned through several test-post cycles - publishing, checking, unpublishing, tweaking - before spotting the actual XML error.

The fix was simple: use HTML entity encoding instead of CDATA wrapping. But finding the problem took real debugging.

Test Post Cycles

We went through probably half a dozen rounds of publishing test posts, checking whether Kit picked them up, looking at the email output, and adjusting. Each cycle meant creating a post, pushing it live, waiting for Kit to poll the RSS feed, checking the email, then taking the test post down. It wasn’t glamorous work, but it’s the kind of thing that has to actually be tested end-to-end.

Working with AI

I should mention - I did this entire migration working alongside Claude in my terminal. Not as a chatbot answering questions, but as an active collaborator. Claude made the code changes, ran the git commands, debugged the RSS issues, helped customize the CSS, and handled the DNS configuration.

This is the kind of project where AI collaboration really shines. There are a hundred small technical decisions - CSS tweaks, template overrides, XML formatting, DNS records - that individually aren’t hard but collectively take hours. Having an AI partner that can read code, write code, and execute commands in real time made the whole thing dramatically faster.

The Result

The site is faster. The publishing workflow is simpler. The content is stored as plain text files in a git repo, which means I have full version history of every post I’ve ever written. And there’s no server to maintain - Vercel handles all of that.

If you’re thinking about making a similar move, I’d highly recommend it. The Hugo ecosystem is mature, the documentation is solid, and the combination of Hugo + GitHub + Vercel is a genuinely great stack for a blog.

And if you’ve been reading this blog for a while, the transition should be invisible to you. Same URLs, same RSS feed, same email subscriptions. Just a cleaner engine underneath.

Let me know what you think!

Running My Day From a Terminal Window

Tue, 24 Feb 2026 16:50:47 +0000

Brad Feld built something called CompanyOS. He wrote about it on his blog – the philosophy behind it, the architecture, how it works. I’d encourage you to read those posts for the technical backstory. Brad set me up on it yesterday, and what I want to write about here is what it actually feels like to use it. One day in and I’ve already shifted a surprising amount of my daily business operations into it. It’s been mindblowing.

What CompanyOS Is

The short version: CompanyOS is a git repo full of markdown files that teach Claude Code how to run your business. There are “skills” – markdown documents that describe how to draft emails, manage calendars, do research, handle customer communications. There are “rules” that encode your company’s context – who works here, what systems we use, how we communicate. And there are agent definitions that let Claude dispatch specialized sub-tasks when the work gets complex.

Brad’s framing is “running a company on markdown files” and that’s exactly right. There’s no app to install, no platform to configure, no orchestration layer or workflow engine sitting in the middle. It’s plain text files in a repo, a smart model that reads them, and MCP connections to the systems you already use – Gmail, Google Calendar, Slack, Asana, and others. That’s the whole stack.

iTerm2 as Mission Control

Here’s the part that surprised me. My primary work interface is now a terminal window. I have iTerm2 open with Claude Code running, and from that single window I can draft and send emails, message colleagues on Slack, look up my calendar, research topics on the web, analyze documents, manage tasks – all without opening a browser tab or switching between apps.

I realize how that sounds. A terminal window as a business operating system is not an obvious pitch. But the thing I didn’t anticipate is how much cognitive overhead disappears when you stop context-switching between eight different applications. I’m not logging into Gmail to draft an email, then switching to Slack to message someone, then opening a PDF viewer to review a document, then jumping to Asana to check my task list. It all happens in one place, in one conversation, with one system that has the full context of what I’m working on.

Asana as the Task Controller

One of the first things I set up was a dedicated CompanyOS project in Asana. The workflow is simple: I load up Asana with the tasks I need done – draft this email, research that topic, follow up on this thread. Then inside Claude Code, I tell it to go grab the next task from the CompanyOS project. It reads the Asana item, figures out what needs to happen, executes it – drafts the email, does the research, whatever the task requires – and marks the Asana item as complete when it’s done.

This turns out to be a powerful pattern. I can batch-process work. I queue up a dozen tasks in Asana during the morning, then let Claude work through them one by one while I focus on things that actually require my direct attention – calls, meetings, thinking through strategy. When I come back, the tasks are done and sitting in my Gmail drafts waiting for a final review before sending.

What I Actually Did on Day One

Here’s a sampling of the things I ran through CompanyOS today, to give you a sense of the range:

I needed apartment research for my daughter who is looking at places in two different cities. Claude did the research, compiled the results with links to each property, summarized pricing and amenities, and saved the whole thing as a Gmail draft. When I later asked it to filter those results for pet-friendly buildings, it did a second round of research and sent the updated findings as properly threaded reply emails to the original messages.

I had a legal email to draft – notifying outside counsel about an ownership change in an LLC entity. Claude found the existing email thread with the law firm, drafted a reply with the correct details, and saved it as a draft in the right conversation thread. No copying and pasting between systems.

Someone sent a fee proposal for something I’m working on (weeks ago in email). I described it generally, Claude found the email, figured out that the document was actually sent in a link, found the document in my downloads folder, extracted the document, and gave me a structured summary of the entire fee schedule. I had a meeting with them later that afternoon and walked in fully briefed.

I messaged Brad on Slack about a small bug I’d found. I checked my calendar to confirm a meeting time. I pulled up and summarized coaching documents that had been sent as Word attachments. Each of these took seconds to minutes, all from the same terminal window.

I kicked off a few research threads, wrote detailed responses to a few emails, analyzed some company information. Again, all from a terminal window. Oh, and I wrote two blog posts (including this one). And by “I wrote” I mean that I had Claude write the drafts, which I then gave feedback on; I uploaded them to my GitHub as a draft, where I made some edits, and then published it. I’m working on having it auto-post to LinkedIn and X as well (that should be set up later today).

What I’ve Learned So Far

A few things stand out after day one.

First – the voice profile matters more than I expected. One of the setup steps is building a profile of how you actually write, based on your sent emails and other documentation. Claude uses this to draft messages that sound like you, not like a robot. The emails it produces are close enough to my natural voice that I often send them with minimal edits. This is a big deal. If every AI-drafted email reads like it was written by ChatGPT, you’ll never trust the system enough to use it.

Second – skills compose in ways I didn’t anticipate. The same system that knows how to draft emails also knows how to do web research, analyze PDFs, and manage Asana tasks. When a task requires combining these capabilities – find an email, extract its attachment, summarize it, then draft a reply – it just works. There’s no integration step because everything runs through the same model with the same context.

Third – and this is the big one – the value is compound. No single capability here is revolutionary on its own. Email drafting, web research, document analysis, task management – these all exist as standalone tools. The revolution is having them all in one place with shared context and zero switching cost. It’s the difference between having a dozen specialized tools and having one system that understands your entire workflow.

Where This Goes

It’s early. One day in. But the trajectory is clear – my terminal is already my most-used business tool, which is a sentence I would not have predicted writing six months ago. Brad built CompanyOS and wrote about its philosophy – I’m one day into experiencing how mind-blowing it is. The interesting question now isn’t whether AI can handle routine business operations. It’s what happens when all of your business context – your communications, your decisions, your company knowledge – lives in one place and compounds over time.

Why Your Board Meetings Aren’t Working

Tue, 24 Feb 2026 15:08:32 +0000

I’ve sat in hundreds (thousands?) of board meetings over the past two decades – as a board member, as a board observer, invited guest, etc. Some were excellent. Many were not. And the gap between a well-run board meeting and a poorly run one is enormous – not just in terms of the value the board provides, but in the trust it builds (or erodes) between management and the board.

This is something I’ve spent a lot of time thinking about. Brad wrote extensively about it in Startup Boards, and a few years ago, I wrote a five-part series – Designing the Ideal Board Meeting – that went into the mechanics in depth. That series covered everything from what to do before the meeting to how to build your board package to running the meeting itself to handling board conflict. But the core problems haven’t changed, so it’s worth revisiting.

The most common mistake I see is that board meetings become status update sessions. The CEO and exec team spend hours walking through slides that board members could have read in advance. Board meetings are expensive – in preparation, travel, and the opportunity cost of getting senior people in the same room for half a day. If you’re using that time to read aloud from a deck, you’re not getting your money’s worth.

The fix starts well before the meeting. As I wrote in Before the Meeting, most of what determines a board meeting’s quality happens before anyone sits down. Quarterly is the right cadence for most startups – monthly is a real burden on everyone. Between meetings, communicate regularly: email updates, KPI dashboards, quick calls when something material happens. The goal is that board members arrive informed. Nobody should be surprised by anything in the meeting. Surprises kill trust.

Send the agenda 10 to 14 days ahead – not the night before – and solicit input from board members. You want them thinking about the hard questions before the meeting, not reacting in real time.

On the board package: start with a CEO letter – not a formal memo, but an honest look at what you’re thinking about, what’s keeping you up at night, what’s going well. Follow it with consistent reporting, the same KPIs and format meeting after meeting, so board members can see trends without decoding a new layout every quarter. I’ve seen 160-page board decks. They’re counterproductive. Bullets and dashboards beat prose and appendices every time.

Be intentional about what you discuss in the room. Pick one or two deep-dive topics that genuinely benefit from outside perspective. Don’t rehash what’s in the materials. Use the meeting for conversations you can’t have asynchronously. Include your executive team so the board sees management in action, but the CEO has to own the agenda and the clock – don’t let it devolve into a VP-by-VP status meeting where strategic discussion never happens.

Be explicit about which topics need a board decision versus which are for input. Build in breaks – three to four hours is the right length, and fatigue kills quality. Always hold an executive session, even if it’s brief. Making it routine normalizes it so it doesn’t feel like an alarm bell. Follow it with direct feedback to the CEO.

And follow up. Clear notes on action items, who owns them, and what the board expects next. The number of board meetings where the momentum simply evaporates afterward is staggering.

One more thing – and something I devoted an entire post to: disagreement in the boardroom is a feature, not a bug. If your board always agrees with you, you’re either not bringing them hard enough questions or they’re not doing their jobs. As a CEO, distinguish between one dominant voice and genuine board consensus. And if after reflection you still believe you’re right – make the call and communicate your reasoning.

If you want a practical, tactical companion to all of this, the team at Domestique recently published a great piece – Why Most Board Meetings Are Useless and How to Fix Them – on the most common board meeting problems and how to address them. It’s a thorough overview of best practices – including a board meeting template that I think nails the why behind each element, not just the how. Worth a read for any founder or CEO who wants to get more out of their board time.

Board meetings don’t have to be a chore. A well-run meeting builds the kind of trust between the CEO and the board that compounds over time. And that trust is worth more than anything that happens in the meeting itself.

More Capitalists, More Equity: Revisiting Dr. King’s Economic Bill of Rights

Fri, 23 Jan 2026 06:08:03 +0000

My Capital Evolution co-author, Elizabeth MacBride, and I have been reflecting this past week on the words of Dr. Martin Luther King and how they continue to resonate deeply as we face both economic and social challenges. We’re committed to broadening the call to use the tools of Evolved Capitalism that give all Americans, and importantly, the companies they work for, the opportunity to thrive. Below is a piece we wrote in honor of Dr. King’s birthday.

“When machines and computers, profit motives and property rights are considered more important than people.”

“A true revolution of values will soon look uneasily on the glaring contrast of poverty and wealth. With righteous indignation, it will look across the seas and see individual capitalists of the West investing huge sums of money in Asia, Africa, and South America, only to take the profits out with no concern for the social betterment of the countries, and say: ‘This is not just.’”

These words are not Bernie Sanders on the campaign trail demanding universal healthcare. Nor were they spoken by Marjorie Taylor Greene, arguing for President Trump to focus less on foreign wars. They were not heard from New York City’s mayor, Zohran Mamdani, making the case for affordability.

They are the words of Dr. Martin Luther King Jr., shared nearly 60 years ago.

The bipartisan frustration with capitalism is not new. In the final years of his life, Dr. King advocated for an “economic bill of rights” designed to address the root causes of poverty by guaranteeing a meaningful job and wage for everyone. King wanted systemic change, not one-off government programs that addressed things like homelessness, mental healthcare, or food assistance. We’re in the midst of an era like Dr. King’s, in which divisiveness threatens to overcome optimism and progress.

Congress is not the answer– interesting proposals like a Universal Basic Income, which were once supported by staunch conservatives like Milton Friedman and progressives like Dr. King, seem out of reach in today’s politics. Worst of all, the Trump administration shows us government programs can be cut after every election, with a stroke of the pen.

We need a new movement for an economic bill of rights – a new consensus that will drive America forward to a more just future. The change has to come – and already is beginning – in the business world. When we researched our book, Capital Evolution, we found a new movement toward broader ownership to restore economic security. Giving people equity in the economy they help create is the best way to secure their position in it.

We are living through a time when fundamental assumptions about our economy and role in the world are being called into question. The current version of capitalism that we practice has only widened the inequalities Dr. King identified. An emblematic tale for the past 50 years is General Electric, which was one of the first 12 companies to join the Dow Jones and a business that delivered tremendous value to society over the first part of the 20th century. When Jack Welch became the CEO in 1981, he was singularly focused on GE’s stock price. He fired over 250,000 people during his tenure, the bottom 10 percent of GE’s workforce annually, regardless of the performance of the business. GE saw historic gains in stock price, but its pursuit of short-term profits led it to adopt misleading and, at times, nefarious accounting practices. After struggling for many years, GE was removed from the Dow Jones in 2018, and few people can name a big innovation from the company since. Shareholders got rich; our society suffered.

In the 1960’s a child born into the bottom quarter of wealth had roughly a one-in-four chance of reaching the top quarter by adulthood. Today, that chance has collapsed to one in twenty. The average first-time homebuyer is now 41 years old. Young Americans can’t afford to purchase a home or pay off their college debt.

Wealth concentration mirrors the late 1920s. The wealthiest 10 percent of Americans own 92.5 percent of all stocks. About two-fifths of Americans don’t own any stock. Meanwhile, CEO compensation has soared 900 percent over fifty years, while worker pay rose just 12 percent. It is no wonder the share of Americans with a positive view of capitalism has declined eight points to 57 percent in just five years.

The good news is business leaders are starting to evolve. For example, former PayPal CEO Dan Schulman surveyed his 23,000 employees in 2019 and discovered something alarming: a customer service representative in Omaha was selling his blood plasma twice a month to make ends meet. On paper, PayPal was paying at or above market rates. Schulman realized that benchmarking against competitors didn’t mean his workers were okay. He raised wages, switched to weekly pay, and boosted healthcare contributions. The company continues to thrive and Schulman got the equivalent of a promotion in the world of CEOs: he was recently named to lead Verizon.

Private Equity firm KKR co-head Pete Stavros grew up watching his father operate a road grader in Chicago for forty years. His father complained about misaligned incentives: work too fast and your hours go down, your paycheck goes down. Today, Stavros has made employee ownership central to KKR’s strategy. Line workers receive multiples of their annual salary when companies are sold. At Optimax, founded by two former Kodak line workers, employees receive 25 percent of monthly profits.

Businesses have a clear choice: cling to an outdated, extractive form of capitalism that fuels inequality, or embrace a model of shared prosperity by expanding ownership. They can eliminate workers with machines, or give them some stock, employee ownership trusts, and equity participation. The rest of us have a choice too: consumers should continue to choose companies that share prosperity with workers and communities; educators must choose to teach the next generation on the value of ownership, and investors must demand their portfolio companies create more owners.

Dr. King wrote that Black Americans “expect their freedom, not as subjects of benevolence but as Americans who were at Bunker Hill, who toiled to clear the forests, drain the swamps, build the roads, who fought the wars and dreamed the dreams the founders of the nation considered to be an American birthright.” Ownership will deliver equality, and we must demand it.

Composition Shop and the Revitalization of Downtown Longmont

Tue, 20 May 2025 11:25:20 +0000

Over the past decade, my wife, Greeley, has been restoring buildings along Main Street (and a few adjacent streets) in Longmont, helping lead a transformation in our local downtown. I’m really proud of the work she’s doing and the vision she has for how these projects can lift up downtown Longmont.

If that weren’t enough, she also recently opened a bookstore in one of the buildings she remodeled. Composition Shop is truly special. It’s beautiful, warm, and welcoming. In addition to a great selection of books, the shop also sells chocolates (really, really good ones) and stationery. As an added bonus, our dog Timber is the store greeter.

Timber

It’s been really gratifying to watch Greeley work. She has a long and deep background in construction management. Through her company, I-Beam Colorado, Greeley has focused for the last 4 years on renovating century-old buildings, preserving their historical character while adapting them for modern use. She currently owns six properties downtown, housing local businesses such as Juniper Goods, Flower Wild, and Bakewell, along with office tenants on the upper floor. Two additional projects are currently in development.

The work is meticulous and often underappreciated, but its impact on the downtown area is significant. A recent article in the Denver Business Journal highlighted Greeley’s efforts, noting how she’s restoring original tin ceilings, historic storefronts, and other long-overlooked details across several buildings on Main Street. This kind of reinvestment is helping reshape downtown Longmont, bringing renewed energy to the area.

The Composition Shop is a particularly meaningful part of this broader vision. Greeley has approached it not just as a developer but as a reader, designer, and community member. If you’re in the area, I encourage you to visit – 356 Main Street. If you’re outside of Longmont you can order directly from her site. AND, if you listen to audio books (like I do), you can use Libro.fm, a platform that supports independent bookstores, and designate Composition Shop as your local store. I’ve switched to Libro.fm from Audible – it’s been an easy transition. Side note: if you’re interested in what I’m reading, I recently signed up for Goodreads and have recorded my current reading as well as books I’ve read in the past 2 years or so. You can also follow Composition Shop on Instagram or leave a review on Google or Yelp.

Lastly, I wanted to flag for you that on May 28, my partner Brad Feld will be hosting a “Random Day” at The Composition Shop. He’ll be available for 15-minute meetings throughout the day and will be signing copies of his upcoming book, Give First: The Power of Mentorship, ahead of its official release. It’s a great opportunity to engage with Brad and support the local community. For more details, visit Brad’s announcement on his blog: Random Day on 5/28 at The Composition Shop.

It’s been a privilege to watch this project come to life—and even more rewarding to see how it’s already starting to shape the next chapter of downtown Longmont.

Valuation Policies Are A Mess

Tue, 18 Mar 2025 13:50:32 +0000

Every venture firm reports the “value” of each of its underlying investments. Typically, this is updated quarterly and sent to each of the fund’s investors. The idea is that investors will then have a definitive view of the value of the firm’s investments. Simple, right? But what is the “value” of a private company? Turns out the answer to that question is not so easy to determine, and, as a result, valuation reporting in venture is a mess.

Prior to 2007, most firms held company valuations at the price of the most recent round. This was relatively straightforward and generally pretty consistent across funds. The rationale for this approach was that the best indicator of value was the last “market” price someone was willing to pay for the asset (this approach has some limitations, which I describe below). Under this methodology, GPs did have some discretion to write companies up or down (meaning to say that their value was more or less than the last round

had indicated) due to performance or changing market conditions – especially if the last round was a while ago and the valuation mark was “stale.” Typically the threshold to do this was quite high, meaning that something with the business, the market, or the company’s prospects had to have clearly and materially altered to warrant a change in valuation. While definitely not perfect, this methodology made for relatively uniform reporting (it was uncommon for companies held in different portfolios to be reported at different values – that typically only happened in edge cases with businesses that hadn’t raised in a while, although quite a few funds had policies that held all companies at a maximum of the last round price). There was certainly some incentive for perverse behavior – raising money to establish a new valuation benchmark, or unjustified discretionary mark-ups (say around the time a fund was going out to maket with a new fund), but these were actually relatively uncommon, and most funds only had discretion to write down assets, not write them up.

But there was a concern that this methodology wasn’t rigorous enough and – especially in the wake of the Enron scandal, where the company manipulated its balance sheet by misrepresenting the underlying asset value of various entities it had interests in (among many other things) – there was a desire to make sure that assets of all kinds were valued “accurately.” And some LPs didn’t like that high-performing companies that didn’t have a need to raise capital were “artificially” being held a lower valuation based on their last round price.

Enter FAS 157 (Fair Value Measurements), adopted in the fall of 2006 and enforced for reporting periods after November 15, 2007. The idea behind FAS 157 made a lot of sense: GAAP provided varying methodologies for establishing the fair value of assets and investors and regulators wanted greater consistency and transparency in reporting. FAS 157 (and subsequent modifications) established an “exit value” concept for valuations – basically asking reporting entities and their auditors to establish the price at which an independent 3rd party would purchase an asset. In 2009, FAS 157 was integrated into ASC 820 (Accounting Standards Codification) which classifies assets based on their liquidity (level 1 assets have publicly quoted market prices; level 2 assets aren’t directly quoted but can be relatively easily valued based on market prices; and level 3 assets are those whose value cannot be compared to market prices and which rely on a firm’s internal methodologies for establishing a valuation benchmark). To be clear, FAS 157 and ASC 820 (from here, I’ll start referring to this as ASC 820 as that’s the standard currently in effect) cover many different types of assets – the reporting by venture and other private funds is just one type of asset that is subject to this standard.

However, inside the venture industry, the adoption of ASC 820 caused significant changes in how portfolios were valued. Funds could no longer rely on their existing valuation policies and instead had to establish the “fair value” of each underlying position using varying methodologies approved under the new standard. This was expensive, but more importantly, it caused surprising divergences between how the same asset was reported from one fund to another, depending on the methodology used, the inputs to the valuation model, and the requirements of the audit firm that had to sign off on the fund’s valuations. This only highlighted that company valuations are more art than science. But the accounting standards treated them as the latter and fund LPs required more and more “rigorous” back-up and reporting. All of this, in my view, has made fund reporting less accurate and more subjective, despite its intention to do the opposite. And while valuation reporting used to be more uniform across the venture industry, it has now become much less so.

I recently helped a fund with their valuation policy, and we asked several of their LPs for a “best-in-class” valuation policy. No one could come up with one. The reason is that, under the ASC 820 standard, there doesn’t seem to be one. I understand why LPs want to understand the current value of a portfolio and that they may not have either the technical expertise or access to the right data to go through each of their underlying portfolios and value them in the way they would like. However, in an attempt to create clarity and truth, all we’ve done is obfuscate value even further by introducing more variables, more subjectivity, and more inconsistency into the process. I sincerely believe that most venture firms do their best to come up with a fair value for each asset they own (we certainly do at Foundry) but given the number of variables and the different accepted methodologies for valuing private assets, it’s not surprising that FAS 820 has introduced even more variability and subjectivity into the process.

I know this won’t happen but I wish we would just go back to the simplicity of valuation policies that only allowed mark-ups on price rounds and allowed modest discretion for GPs to write down under-performing assets. This would massively simplify valuation procedures and make them much more uniform. It wouldn’t, in my opinion, materially obscure fund performance. The chaos caused by 157/820 has not been worth it. We’re not closer to reporting the exact “value” of our portfolios…we’re further from it than ever.

The Future of Venture

Fri, 24 Jan 2025 13:03:20 +0000

Just before the end of the year, Erin Griffith of The New York Times published an article titled “What Is Venture Capital Now Anyway?” It’s a provocative look at the state of venture capital and, as these sorts of things are want to do, very quickly started making the round in venture circles as VCs tried to map how they fit into the landscape that Erin describes – a split in how VC firms are operating: some firms are keeping to a smaller, boutique style while a handful are becoming behemoths, almost unrecognizable as venture firms. There’s very little in between.

I think Erin’s description of how our industry is evolving is spot on. But this evolution in the venture market isn’t a surprise. In fact, it’s something I wrote about years ago – back in 2010. In that post, I described the bifurcation of the venture industry and predicted that large, muti-stage, multi-sector firms would continue to raise ever larger funds and that firms with smaller, more focused funds would proliferate at the other side of the market. I described the evolving landscape as increasingly barbelled – predicting that there would be relatively few firms in the middle. At one end of the barbell would be essentially asset managers (asset aggregators) and at the other, smaller firms that were more in the mold of what we historically thought of as venture. The former would be seeking beta and predictable, if lower, returns. The latter would be seeking alpha and greater risk. The middle of the barbell would be sparsely populated and firms inhabiting that space would run the risk of being too large to execute the alpha strategy but too small to be considered true asset aggregators.

I wrote a follow-up piece in 2013, parsing some then-current statistics from the NCVA that backed up what I had predicted in 2010. This trend – as Erin points out – is even more true today. In the 2013 post, I highlighted a few firms that were clearly on their way to the asset management side of the venture barbell. All have subsequently gone on to raise enormous funds: NEA (most recent fund: $6.2B), Norwest ($3B), A16Z ($7.2B), and Khosla ($3B), in particular. But also firms like Greylock ($1B) and CRV ($1B).

I didn’t fully understand the ramifications of the trends when I was first writing about them, but the implications of this continued bifurcation are becoming clearer to me. We often say at Foundry (where we have a fund investment practice and make direct investments) that fund size is fund strategy. That’s always been the case but is even more so now. Fund size has implications for both the kinds of deals your firm pursues, how concentrated your portfolio can be, as well as your approach to valuation and structure (not to mention fundraising – many LPs prefer to be able to write large checks to a small number of firms, something that is putting pressure on fundraising for the smaller end of the market as large firms hoover up a meaningful percentage of LP dollars each year).

I’m not at all saying that large funds are a bad strategy. It’s just a strategy different from what smaller firms at the other end of the barbell are pursuing. And the resources that these firms bring to their portfolio companies make them potentially quite attractive to entrepreneurs (and as co-investors in the right circumstances). But it also means that they have a different risk profile than smaller, earlier-stage investors, which can create misalignment across a cap table (earlier investors wanting to take more risk, asset aggregators looking for more modest risks, and assured returns). All of these firms describe themselves as “venture” but investing from a smaller fund, where returns are driven by a very small number of deals, is very different than investing from a larger fund where (whether you admit it or not), you are trying to put larger dollars to work in any given investment and where the dollar return can be more important than the multiple you generate. Larger firms are also generating a more significant percentage of their pay-outs to GPs from management fees than from carry.

The firms mentioned above who have all raised funds > $1B (in some cases many times that amount) are operating a different business than funds at the smaller end of the spectrum. They aggregate massive amounts of capital across a large and diverse set of funds and often build huge organizations to support their work. One of the key signs that a firm is in the asset aggregation business is not just the size and scope of the organization but specifically the size of its marketing and PR groups. These firms are often quite adept at pushing out materials and content (much of it high quality) to stay front and center in the market – not just among entrepreneurs but, importantly, among LPs. They require meaningful fundraising capacity to keep feeding the asset machine. Their funds are typically consistent performers but are unlikely to generate outsized returns. Importantly, they can accommodate very large LP checks, which many LPs like, and they are a brand name that LPs also feel comfortable with (as I often say, LPs are not in the risk business; they’re in the capital allocation business – which is very different). These firms are chasing something more akin to beta than alpha for the most part, although occasionally they’ll be early into a new market (AI or crypto are examples – especially in the case of AI where early investments required massive checks, which smaller firms were not set up to provide).

On the other side of the spectrum are firms that are true venture firms in the traditional sense. Their funds are smaller, and they tend to raise funds that are similar in size from one fund to the next. They take real swings. They make more of their money on carry than fees. Benchmark is a great example, a firm that Erin Griffith mentions in her article, but many other funds also have this model. Founder Collective may be the quintessential fund of this type (having stayed remarkably true to their mission and fund strategy across many fund cycles), but funds like Union Square Venture also come to mind. Because they’re going after alpha there’s more of a risk of a badly performing fund (and bad vintage) but they’re also more likely as a group to have funds that exceed 3x which is nearly impossible for the asset management funds to do.

More in a future post about the operations of funds at each end of the barbell, as well as some commentary about what happens when you get stuck in the middle. I can understand why Erin’s article made the rounds so quickly. The evolution of the venture market – especially in a cycle of decreasing liquidity and tightening fundraising – is important to track.