Joe Magyer

Managing Partner at @SeaplaneVC. 2X founder. Host of the @InvestNStartups podcast.

[Joe Magyer @Magyer]

I walked away from a 5-star fund and hundreds of millions in AUM to focus on investing in early-stage startups. Here’s why: https://t.co/Qe2lP2u7Fd

[Joe Magyer @Magyer]

@contextconor @hyperspell

[Rob Hamilton @Rob1Ham]

Honestly, shame on the CIA for not getting Jensen a patriotic state issued baddie.

[Joe Magyer @Magyer]

@ringer @BillSimmons Giannis scored 50 in a clinching NBA Finals game while Doc has a career 34% winning percentage as a coach in potential playoff clinching games. Which of these guys should be critiquing the other on execution?

[Kye Gomez (swarms) @KyeGomezB]

If you don't like datacenters then get off the internet

[Aidan Gold @MrGoldBro]

If America tries to attack its leading technology companies, the country will suffer.

Technology is our greatest asset for increasing the pie.

This must be bipartisan.

[Joe Magyer @Magyer]

@contextconor @hyperspell

[Joe Magyer @Magyer]

What if raising more VC money actually made it harder for a founder to win?

I talked with Brendan Mathews of Motley Fool Ventures about investing lessons that don't fit neatly into venture's usual playbook:

A big fundraise can become a trap. One portfolio company grew from ~$200K to nearly $10M in revenue but had raised ~$20M. At a ~$30M exit, most proceeds could go to investors, leaving founders with little incentive to sell.

The best round might be the one you don't need. Brendan pushes founders to control burn and reach "default alive" rather than live one missed round away from shutting down. Optionality matters more than the biggest valuation.

Venture is splitting into two markets. A handful of consensus AI deals absorb enormous amounts of capital. If you're not a mega-fund competing for those rounds, the edge may be capital-efficient companies at sensible prices.

Don't confuse being early with being right. In 2013, we argued that the market was overestimating how quickly driverless cars would disrupt Progressive. The stock has since risen roughly 13x.

The obvious trend can still be a huge winner. Brendan invested in e-commerce, mobile, and digital payments years after their potential was clear. You don't need to predict a revolution on day one to make great returns.

AI can be overhyped and underestimated. Brendan uses AI tools every day and is bullish long-term, while believing near-term expectations may be running ahead of reality.

Build a portfolio like a gardener, not a carpenter. Plant broadly, spread investments across time, let winners emerge, and be careful about repeatedly putting more money into struggling positions.

Higher rates aren't necessarily bad for investors. Brendan sees more attractive entry prices and future upside potential when capital actually earns a return, rather than when rates sit near zero.

Why does a $1B VC fund need $20M a year in fees? Brendan thinks most managers should charge less, invest more, and earn their upside through carry. His twist: a few truly exceptional managers might charge more rather than keep growing their funds.

https://t.co/q6XgCTqKls

[Raoul Pal @RaoulGMI]

https://t.co/4cVZQgBSVB

[Joe Magyer @Magyer]

@trytrata Congrats! Impressive but also not surprising given the ultra-high-quality transcripts on Trata.

[Trata (YC W25) @trytrata]

Today, Trata is excited to share the revised Hedge-Bench: our benchmark tying open-ending reasoning to prediction capabilities.

We constructed 92 real-world environments derived from hedge fund analysts’ reasoning traces. Agents reason from frozen information snapshots, and their forecasts are evaluated against future real-world outcomes. No model scores above 50% (GPT-6 Astra being the top performer at 44.2%). For frontier, state-of-the-art models, these results highlight a persistent and wide gap between producing plausible-sounding reasoning and making accurate, economically valuable forecasts. In a semi-verifiable domain like finance, where outcomes are delayed and reasoning is difficult to verify, intermediate expert reasoning traces are crucial.

We post-trained @Meta's Muse-Glimmer-30B on 165 environments with per-turn credit assignment using an LLM-as-a-Judge. We observe evidence of generalization: held-out forecasting accuracy at high effort improved by 10.06 percentage points, while out-of-distribution evaluation scores improved by 3.94 points on @OpenAI's GDPval, 2.29 on @Surge’s GDP.pdf, 7.07 on @Harvey’s LAB, and 4.96 on @Samaya_AI’s FrontierFinance under their respective metrics.

[Joe Magyer @Magyer]

@alanwells @pmarca This thread has aged very well.

https://t.co/oNmE1Z6Tg2

[Cohere @cohere]

While evaluating Qwen3.8-Flash-Next in a controlled sandbox environment, Cohere's research team observed something unexpected.

Asked to generate a defensive cybersecurity detection rule, the model instead inventoried aspects of the machine it was running on and attempted to upload a probe file to an Alibaba Cloud storage bucket in Beijing. Notably, the model's reasoning trace stated that it would not invoke any tools before taking these actions.

Cohere's sandbox blocked the attempt, and no external communication succeeded. We have not determined the root cause, and we are not asserting malicious intent.

But the incident raises important questions for organizations deploying agentic AI systems: Can models take actions that differ from their stated reasoning, and how should security teams evaluate trust, provenance, and behavior in open-weight models?

We are publishing our findings because organizations making model adoption decisions deserve as much information as possible. We hope this contributes to greater transparency, stronger security practices, and continued collaboration across the AI ecosystem.

Read the full report: https://t.co/TZSugXi2ZE

[Joe Magyer @Magyer]

"NDR is ~900%"

[Patrick OShaughnessy @patrick_oshag]

A list of stats from the Anthropic S-1:

- NDR is ~900%

- Revenue per employee is over $6.6M, up from about $1.4M a year earlier

- Revenue went from $41M three years ago to $16.3B in the first six months of 2026

- Quarterly revenue went from $4.7B in Q1 to $11.6B in Q2

- Q2 2026 was the first profitable quarter: $974M in operating income ($1.7B operating loss the quarter before)

- Adjusted operating income was $1.6B in the first half of 2026, up from a $1.8B adjusted loss a year earlier

- Revenue grew 1,368% year over year in Q2, while compute and infrastructure costs grew 300%

- Compute went from 251% of revenue to 48% in five quarters

- The value of Anthropic’s own internal Claude usage grew 250x from January to September 2026

- Anthropic spends more on tokens (at list price) than on payroll

- Over 1,900 customers pay $1M+ a year, and about 250 pay $10M+

- Customers paying $100K+ went from 950 to over 11,900 in a year

- Over half of the Fortune 500 pays $100K+ a year

- Those Fortune 500 customers still spend less than 0.1% of their total expenses with Anthropic

- The top 5% of Claude Code users spend roughly 20x what non-users on Claude Enterprise do

- About 40 million smaller customers

- $72.4B of the $80.7B first-half net loss is an accounting charge on convertible notes (not operating losses)

- Nearly $800B in compute commitments over 10 years
$141.1B raised in total, with over $130B in cash and short-term investments

- Nearly half of revenue comes through Amazon, Google and Microsoft’s cloud marketplaces, all of which also compete with Claude

- A single share held by the seven co-founders carries 50.1% of the vote

- That share is redeemed for nothing if fewer than three founders remain

[Alan Wells @alanwells]

Just spent the last 7 days on the front lines of the vulnpocalypse fending off an active cyber attack on one of our products.

Some thoughts:

- Software factories are going to be required equipment for everyone. There’s just no way to keep up with the pace of highly automated adversaries without one.

- We were lucky because we had spent the last few months getting our software factory running well. It is nice to have for product development but proved to be absolutely essential this week.

- At the minimum you need to have a coding server in the cloud pre-provisioned with read only access to all your logs and all the services you depend on for observability. With that in place you are a prompt away from reasonably good situational awareness, ongoing monitoring with loops, and ability for coding agents to validate fixes against your actual, observed attack patterns.

- Shared coding server also unlocks multiplayer incident response where everyone can see every session and jump in as needed. Sharing session links that can be forked is much higher bandwidth than re-explaining things.

- You don’t need to be Ramp/Stripe/Uber scale to have a working software factory in the cloud. We are a team of 3. Ours is built with an @opencode server (thanks @thdxr!), a forked version of compound engineering optimized for automated workflows (thanks @kieranklaassen!) and a multiplayer agent in slack that handles crons (we have our own harness but pick your favorite here).

- To get our software factory up and running well, we had to stop all new product development for ~6 weeks and just focus on getting the validation systems working that enabled us to reliably shipping PR without human code review. This felt very painful in the moment but was totally worth the eventual speedup in velocity.

- KYC for @AnthropicAI & @OpenAI cyber programs is now easy and quick (at least for defensive access), but a day felt like a lifetime this week. Submit your app now before you really need it.

- Getting Mythos access unlocked a deeper level of analysis that helped us shift from reactive mitigation to proactive patching. Wish we had done this a few weeks earlier. Get started now!

[Joe Magyer @Magyer]

@PSAcard What about the theft of my son’s card in September? It’s clear that these issues with @FedEx are systemic.

[Joe Magyer @Magyer]

My 12yo son’s Pokemon card disappeared while with @FedEx on the way to @PSAcard. Box arrived without the card inside. My son was sad and angry as he’d landed a great card in a pack and this was his big score. Many tears were shed. Before bed, though, he told me that he forgives the person who stole his card because they must have needed the money more than he did. Proud of him for showing that level of maturity and grace.

[Joe Magyer @Magyer]

@ColinGardiner Irony is you could just take them with a digital camera, send the job to a local pharmacy through Google Photos, and pick up the prints same day.

[Alex Rubalcava @AlexRubalcava]

I had a great time talking startups and tech with my friend @Magyer on his podcast. Check it out!

[Joe Magyer @Magyer]

The best deals aren't competitive.

That's the view of my friend and early-stage investor, @alexrubalcava. We talked on @InvestNStartups about VC groupthink, the evolution of moats, embracing technical risk, only investing in greenfield markets, and more.

Technology risk creates opportunity. Many VCs avoid technically difficult startups, leaving room for investors able to assess and underwrite those risks. As AI makes software easier to build, Alex sees more potential in frontier tech and physical-world applications.

AI is redefining software moats. Faster development weakens traditional defensibility. Alex favors software in regulated or high-stakes settings where accuracy, auditability, accountability, and human oversight still matter.

Sales and product judgment matter more. Alex evaluates founders on whether they can ship, sell, and hire. With building easier, sales velocity and deciding what to build—and when—have become more important.

Venture suffers from groupthink. His public-markets background shapes a contrarian approach grounded in valuation discipline, independent judgment, and finding opportunities overlooked by consensus-driven investors.

Small funds can deliver big returns. Exceptional LP outcomes don't require trillion-dollar exits or mega-funds. An overlooked multibillion-dollar exit can be transformative for a smaller venture fund.

Capital efficiency reduces funding risk. Long runways give startups time to prove their case, especially when follow-on investors favor AI narratives. Alex cites profitable, fast-growing non-AI companies struggling to raise despite strong fundamentals.

Greenfield markets offer an edge. Alex prefers startups serving needs no existing technology addresses over crowded VC-backed categories. Shipping products, winning early customers, and showing demand turn unknown markets into investable opportunities.

Investing expertise must evolve. Alex highlights secondary share sales as a path to LP liquidity, Amplify's founder network as a diligence resource, and the importance of steadily expanding ones circle of competence.

Persistence needs experimentation. Productive founders test new products and sales tactics rather than repeating failed approaches. Alex recalls a founder who shut down a struggling startup and returned half its capital instead of grinding on.

Conviction beats allocation. Alex argues the best opportunities may have just one term sheet. At Stage VP, 42 of his 45 investments were noncompetitive, showing how leading overlooked rounds can create an edge.

Alex is very talented and a great investor. I hope you enjoy this one!

https://t.co/3CSG532NVm

[Joe Magyer @Magyer]

Finally below 70° in Austin…

[JJ @JJ_sports_]

I would rather go back to the BCS than a 24-team playoff.