Joe Magyer

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Joe Magyer

Joe Magyer

@Magyer

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

Austin, Texas Katılım Şubat 2010
368 Takip Edilen10.1K Takipçiler
Joe Magyer
Joe Magyer@Magyer·
Uber's pricing journey is a good proxy for Anthropic and OpenAI, both of which have massive latent pricing power. Uber's early critics said that demand would melt once they stopped subsidizing rides and yet, despite take rates increasing from 20% to 30% in the 8 years since the IPO, trips on Uber have almost tripled. No stake in Uber, Anthropic, or OpenAI. I just think investors are seriously underestimating the frontier AI labs' pricing power.
Haley Bryant 💛@haleymbryant

the shift to programmatic plans make sense esp on the road to ipo. uber couldn’t subsidize rides forever.

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Joe Magyer
Joe Magyer@Magyer·
Just recorded a banger of a conversation with @PeterJ_Walker about seed valuations, liquidity, solo founders, and knowing when to quit. Drops Wednesday on @InvestNStartups.
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micro1
micro1@micro1_ai·
Today we’re releasing Realm Warren, part of the Realm benchmark series for measuring frontier AI models on real-world expert workflows. Each task tests whether a model can produce a legal work product and adapt it as circumstances evolve. We evaluated Claude Opus 4.7, GPT-5.5, and Gemini 3.1 Pro across federal and state law, scored through IRAC: issue spotting, rule identification, factual application, and legal conclusion. Here’s the results (mean score): -Claude Opus 4.7: 0.358 -GPT-5.5: 0.351 -Gemini 3.1 Pro: 0.219 The sub-40% result shows where models break down on long-horizon legal work. Three failure modes drive it: the IRAC chain breaks after issue spotting, models front-load their effort and fail to revise, and skipping visual exhibits leads to invented facts. Full report linked in the comments.
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Joe Magyer
Joe Magyer@Magyer·
@johnfelix123 It actually pattern matches because the LPs who wait for the final close aren't the type who optimize for upside.
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John Felix
John Felix@johnfelix123·
A lot of LPs wait for final close so they can see a few deals first. But that also means no QSBS treatment on anything the fund did before they showed up. Comes up less often than you’d think.
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Ariel Winton-Jones
Ariel Winton-Jones@arielrwinton·
Investing in software when "software is dead" and building a hyper-concentrated early-stage fund - got to share some unintentionally hot takes with @Magyer 🙃
Joe Magyer@Magyer

Excited to share my conversation with @arielrwinton. We talked about services as software, what B2B software looks like in the age of AI, and why intentional, high-conviction investing can be a real edge. Here's the longer breakdown of our conversation: AI is changing what “software” even means. Ariel’s core insight is that the most interesting new B2B companies are no longer just tools that help customers do work themselves; they increasingly deliver the work itself, with AI acting as the intelligence layer. Her “services as software” lens is really about backing products that solve the problem, not just support the workflow. There is no single “correct” software business model anymore. Ariel pushes back on rigid thinking around pricing and monetization. Rather than forcing every company into seat-based, usage-based, or any other fashionable model, she starts with how the customer experiences value and how that customer is actually comfortable buying. The takeaway is that good pricing is market-matched, not ideology-driven. At the earliest stages, insight can matter more than speed. One of her strongest points is that pre-scale companies often produce rich signals before they produce rich spreadsheets. Ariel’s process is built around hypothesis-driven diligence on qualitative evidence: customer behavior, founder learning velocity, market pull, and the first real signs of product-market fit. That is a different kind of rigor than simply moving fast. Relationship-building is part of diligence, not separate from it. Ariel wants to meet founders well before a financing process, partly to avoid “pitch mode,” but also because time reveals how people think, learn, and respond under pressure. The deeper point is that winning great deals consistently often comes from showing your value over time, not trying to out-sprint everyone once a round is live. B2B remains attractive because it is unusually resilient. Ariel’s case for staying focused on B2B is not just familiarity. She argues that B2B software businesses are harder to kill, more capital-efficient, and governed by clearer operating playbooks than many other models. Her conviction comes from seeing that resilience firsthand across both focused and generalist environments. Concentration can start earlier than most investors think. Ariel rejects the idea that concentration only makes sense once growth-stage metrics are obvious. Her view is that there is an undervalued middle ground: once real qualitative signs of product-market fit emerge, an investor who knows how to read those signals can begin concentrating earlier and more intentionally. Follow-ons are emotionally harder than they look. One of the sharper insights in the episode is Ariel’s skepticism toward heavy reserve strategies. Her argument is not just mathematical; it is psychological. Once you are already partnered with a founder, it becomes harder to stay objective, and later rounds often happen at prices you do not control. Her solution is to concentrate where she believes she has the clearest edge and keep reserves relatively disciplined. “Founder friendly” should not mean automatic yeses. Ariel makes an important distinction between being supportive and being indiscriminate. She does not see loyalty as writing every follow-on check; she sees it as being thoughtful, honest, and acting from conviction rather than social pressure. That is a useful correction to a phrase that often gets used too loosely in venture. Solo GP does not have to mean solitary decision-making. Ariel talks about changing her mind on building alone. What stands out is her realization that independence on the investment side can coexist with a broad, trusted village of mentors, peers, and supporters. The lesson is that you do not necessarily need a formal partner to pressure-test thinking if you have intentionally built the right network around you. The most valuable help in venture is often bespoke, not scalable. Her critique of platform teams is one of the episode’s clearest contrarian takes. Ariel believes the best founder support is highly contextual, trust-based, and specific to a moment, not something easily turned into a repeatable menu of services. Her alternative is a dense network of excellent people she can connect to founders at exactly the right time. A founder’s “why” is not soft stuff; it is core diligence. Ariel starts first meetings by asking why the founder is building the company. She treats that as foundational context, not biography filler. The insight is that understanding motivation, origin, and market intimacy helps interpret everything else you learn later. Hope you enjoyed this episode of @InvestNStartups!

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Joe Magyer
Joe Magyer@Magyer·
Excited to share my conversation with @arielrwinton. We talked about services as software, what B2B software looks like in the age of AI, and why intentional, high-conviction investing can be a real edge. Here's the longer breakdown of our conversation: AI is changing what “software” even means. Ariel’s core insight is that the most interesting new B2B companies are no longer just tools that help customers do work themselves; they increasingly deliver the work itself, with AI acting as the intelligence layer. Her “services as software” lens is really about backing products that solve the problem, not just support the workflow. There is no single “correct” software business model anymore. Ariel pushes back on rigid thinking around pricing and monetization. Rather than forcing every company into seat-based, usage-based, or any other fashionable model, she starts with how the customer experiences value and how that customer is actually comfortable buying. The takeaway is that good pricing is market-matched, not ideology-driven. At the earliest stages, insight can matter more than speed. One of her strongest points is that pre-scale companies often produce rich signals before they produce rich spreadsheets. Ariel’s process is built around hypothesis-driven diligence on qualitative evidence: customer behavior, founder learning velocity, market pull, and the first real signs of product-market fit. That is a different kind of rigor than simply moving fast. Relationship-building is part of diligence, not separate from it. Ariel wants to meet founders well before a financing process, partly to avoid “pitch mode,” but also because time reveals how people think, learn, and respond under pressure. The deeper point is that winning great deals consistently often comes from showing your value over time, not trying to out-sprint everyone once a round is live. B2B remains attractive because it is unusually resilient. Ariel’s case for staying focused on B2B is not just familiarity. She argues that B2B software businesses are harder to kill, more capital-efficient, and governed by clearer operating playbooks than many other models. Her conviction comes from seeing that resilience firsthand across both focused and generalist environments. Concentration can start earlier than most investors think. Ariel rejects the idea that concentration only makes sense once growth-stage metrics are obvious. Her view is that there is an undervalued middle ground: once real qualitative signs of product-market fit emerge, an investor who knows how to read those signals can begin concentrating earlier and more intentionally. Follow-ons are emotionally harder than they look. One of the sharper insights in the episode is Ariel’s skepticism toward heavy reserve strategies. Her argument is not just mathematical; it is psychological. Once you are already partnered with a founder, it becomes harder to stay objective, and later rounds often happen at prices you do not control. Her solution is to concentrate where she believes she has the clearest edge and keep reserves relatively disciplined. “Founder friendly” should not mean automatic yeses. Ariel makes an important distinction between being supportive and being indiscriminate. She does not see loyalty as writing every follow-on check; she sees it as being thoughtful, honest, and acting from conviction rather than social pressure. That is a useful correction to a phrase that often gets used too loosely in venture. Solo GP does not have to mean solitary decision-making. Ariel talks about changing her mind on building alone. What stands out is her realization that independence on the investment side can coexist with a broad, trusted village of mentors, peers, and supporters. The lesson is that you do not necessarily need a formal partner to pressure-test thinking if you have intentionally built the right network around you. The most valuable help in venture is often bespoke, not scalable. Her critique of platform teams is one of the episode’s clearest contrarian takes. Ariel believes the best founder support is highly contextual, trust-based, and specific to a moment, not something easily turned into a repeatable menu of services. Her alternative is a dense network of excellent people she can connect to founders at exactly the right time. A founder’s “why” is not soft stuff; it is core diligence. Ariel starts first meetings by asking why the founder is building the company. She treats that as foundational context, not biography filler. The insight is that understanding motivation, origin, and market intimacy helps interpret everything else you learn later. Hope you enjoyed this episode of @InvestNStartups!
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Joe Magyer
Joe Magyer@Magyer·
Excited to share my conversation with @itsdanieldart on Future Titans, authenticity, and systems thinking. Here are the key takeaways and from the conversation: Authenticity is a real competitive advantage, not just a personality trait. Daniel’s view is that in venture, authenticity builds trust, trust compounds over time, and that becomes a durable edge with founders, LPs, and peers. He draws a hard line between honest feedback and performative bluntness, arguing that the best investors pair candor with empathy. The best networks are discovered, not manufactured. One of the strongest ideas in the episode is Daniel’s belief that you should spend time finding believers rather than trying to convince skeptics. His “no name tags, no pitch tags” approach at Future Titans reflects a broader philosophy: the highest-quality relationships usually come from shared values and trust, not forced transactionality. Good investing starts with good systems. Daniel comes back repeatedly to being input-focused rather than outcome-obsessed. Whether he’s talking about building a conference, supporting founders, or constructing a fund, his core idea is that strong systems, repeated over time, create the conditions for strong outcomes. Founder support is not “value-add theater”; it is trust-building. His post-investment cadence with founders reflects a bigger belief that company-building is lonely and that investors earn the right to matter by being consistently useful, reliable, and emotionally steady. The insight is that being hands-on is less about control and more about becoming a trusted source of truth. Tier 1 ambition is really about relevance, not branding. Daniel is very open about wanting to build @RockYardVC into a top-tier firm, but he frames that ambition operationally: can he become someone who is consistently in the conversation for the best deals in his areas of focus? The takeaway is that elite status should be earned through repeated market relevance, not borrowed prestige. Venture works best when you invest for upside, not survival. A memorable throughline is Daniel’s rejection of playing defense just to preserve optics. He argues that venture is an upside-capture business, and that emerging managers can get trapped by trying too hard to avoid failure instead of underwriting for asymmetric returns. In frontier markets, proximity beats false certainty. On AI and quantum, Daniel’s stance is not “I can predict the future perfectly,” but rather “I want to get close to the smartest builders and learn from them.” The deeper point is that an investor’s edge often comes less from pretending to know and more from developing informed conviction through proximity to exceptional people. Long-term thinking expands what’s possible. Daniel repeatedly frames decisions on a 5-, 10-, and 15-year basis, arguing that a longer horizon raises your tolerance for short-term imperfection and makes room for ambitious bets, deeper relationships, and better firm-building decisions. Tactical questions often reveal more than abstract advice. His favorite question to ask experienced investors — what they would want him to do in the first 90 days if they were backing Rock Yard — captures the episode’s broader mindset: learning should translate into concrete actions, not vague inspiration. Please enjoy!
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Ali Ansari
Ali Ansari@aliansarinik·
there’s never been another type of company that brings together doctors, lawyers, finance experts, and world-class AI researchers into one tight team working toward the same mission. if you build a health AI company, you might have doctors and AI researchers. if you build an AI-native law firm, you’ll have lawyers and AI researchers. but if you build a data engine that orchestrates the nuanced depth of human expertise into AI models—and those models create immense abundance for the very humans behind them, you bring the best of humanity, from all walks of life, under one roof. that’s the micro1 research team. we have AI researchers who design the orchestration and structure of human expertise, and domain researchers who bring the nuance and complexity of their fields. together, they train frontier AI models, improving the lives of the same humans who trained them. there are 4 products that allow for this: -AI Recruitment to source & vet expertise -Data platform to rapidly create highly quality data -Data pipeline performance tracking to assure throughput, quality, and effiency -AI automation to allow for synthetic data generation for data efficient model training The micro1 research & eng team is more than 50% of our team. In the limit, research & engineering will be 99% of our team. come build freely toward answering a fundamental question: where should humanity spend its time? as we orchestrate human expertise, we decide which work humans should focus on. as we build automations that generate data through AI, we define where humans shouldn’t spend time. and as we create abundance through AI model improvements, we shape how humanity evolves its time. join us in answering this very question.
Jamie Quint@jamiequint

On Mercor, Handshake, Surge AI, Turing, Micro1, etc. If you're considering making a career move I would not go work at any of these companies. These businesses are a race to the bottom. There is no structural defensibility in outsourced research/training data for a set of 5-10 very large customers. Once the frontier labs have to start caring about margins, one of two things will happen: they will drive vendor margins down until these businesses trade at 0.5–1x revenue, like every other staffing company (potentially worse because of the customer concentration risk), or they vertically integrate. All arguments about how these are important outsourced research companies that the labs really value are cope.

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micro1
micro1@micro1_ai·
The micro1 referral program has now surpassed 1,000,000 referrals 🚀 We're hiring experts in medical, legal, finance, STEM, coding, and more to help train AI models. Know someone who might be a good fit? Send them our way and earn $100–$3,000 per successful hire. Link to register in the comments.
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Joe Magyer@Magyer·
First Masters trip and able to bring my boy along. Amazing.
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Joe Magyer@Magyer·
Happy to share my conversation with @gjain about momentum, moats, how vibe-coding impacts pre-seed investing, and the importance of both great product AND great distribution. The @AforeVC Cofounder explains why momentum may be the new moat for AI startups, and why the best products win by becoming magnets for users instead of relying on classic switching costs. We also did a deep dive into pre-seed investing: how Afore helped institutionalize the category, why pre-seed used to be seen as a negative signal, and how the market has evolved over the last decade. Gaurav also shares what he looks for in founders at the earliest stage, including speed, customer obsession, technical depth, grit, and willingness to iterate quickly. We had a practical discussion of how AI coding tools, Cursor, and code generation are changing startup formation, reducing burn, and helping AI-native companies reach traction faster with less capital. Gaurav also appreciates it takes more than a great product to win. We talked about why distribution matters earlier than most founders think, especially as product development becomes easier and more democratized in software and AI. Gaurav unpacks contrarian views on market size, product vs. go-to-market, dilution at pre-seed, and why backing exceptional people often matters more than betting on hot categories. The episode also covers how Afore thinks about founder-in-residence programs, pre-seed fund strategy, and building a durable venture firm for the next 10 to 20 years. Thanks for Gaurav for joining for this episode of @InvestNStartups -- the listenership data on this one is excellent -- and to @conor_ai for the introduction!
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Joseph Zhang
Joseph Zhang@sohan_zhang·
recently onboarded to @verialabs (F25) to help with our security Highly recommend working with them! Super professional, clean UI, and well worth the investment The product lives within our CI/CD, and it's a super easy github integration 🥂
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