Carson V. Conant

40%+ of Seed and Series A funding in 2026 is flowing into $100M+ rounds, as Zachary DeWitt recently highlighted. The market is making a big bet on mega rounds. I'm taking the under on their performance--I'd bet heavily on an index of regular-sized seeds vs the megas.   Here's what I think is happening. Ambitious founders surrounded by AI hype see Anthropic's and OpenAI's returns and think they need to do something similar. In their minds, this means raising huge rounds to fund NBA-level salaries for the most legible AI talent at Meta, Google, other AI labs, etc.   The problem is that this approach is fundamentally derivative. This is not how Anthropic, OpenAI, or any of the other most disruptive AI companies started. Disruption requires doing something that experts dismiss. If a startup requires raising mountains of cash to hire legions of expensive experts, it might have some degree of success, but it will not be disruptive.   Look, this is tricky. Anthropic raised giant early rounds and seems very mission-oriented, so I don't want to paint with too broad a brush here. That said, Anthropic's $124m Series A in 2021 was actually very unconventional in many respects, even in the context of a frothy 2021.   Besides being derivative (and thus not disruptive), this approach also selects for mercenary over missionary culture. And all things being equal, companies with missionary culture outperform. Because if you really care about solving a problem, you're willing to make sacrifices and do non-consensus things—not just copy what worked for the last wave.   So, why does this pattern persist despite these concerns?   The most economically dominant point is that the investors doing the big rounds make a lot of money on management fees. When partners at funds enjoy massive compensation from management fees, investment performance becomes a "nice-to-have."   To be fair, many of these partners are post-economic and are arguably more interested in hiring huge teams and building "platforms." The fees justified by mega rounds align nicely with the expense structure of empire-building. On the other hand, the smaller rounds are done by firms where the principals are making much less on management fees and have much more incentive to see a return on the capital via carry.   The mega firms would surely retort that many of the smaller rounds are done by firms that are here today, gone tomorrow. And there's truth to that. Mega firms with billions of assets are inherently more stable. At the same time, those firms are fundamentally targeting much lower returns. It's trade-offs all the way down.   So when I look at that 40%+ number, I see capital flowing into a pattern that selects for derivative thinking and mercenary culture, with an underlying incentive structure that ensures it keeps happening.   And that's why I'm taking the under on the performance of that 40%.

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Most professional services firms operate under a pass-through + annual distribution model. So if you’re a Partner, what’s the best holiday gift? A higher PPP—driven by AI your firm deployed well. That’s how one CFO described their Laurel rollout: “The first cohort alone paid for the entire investment. Twice.” Now scale that across 1,000 professionals. Or for our largest customer today, 5,000+. And by EQ1, 10,000+ The math gets staggering—fast. The firms entering 2026 with proven AI ROI will compound ahead of peers still debating tools instead of outcomes. Happy 2026. Let’s make it count.

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Public software is down 40% in 12 months. The selloff has been indiscriminate. Companies hitting their revenue targets are getting hammered alongside the ones that deserve it. The market isn't distinguishing between well-positioned and poorly-positioned software companies.   That will change. When it does, the companies left standing will be the ones with moats that matter in a world where agents do more of the work.   The Emergence partnership spent yesterday debating a question I think every founder and investor should be asking (and, judging by my LinkedIn feed, it seems they are...):   Which software moats actually survive in an AI era?   Here's the spectrum we landed on (from least to most defensible):   1. Workflow   Pre-Claude, getting humans to do their jobs inside your software was a powerful moat. But if agents are doing the work, who cares about the human workflow? Cursor vs Claude Code is the canary in the coal mine: one owns the workflow, the other just executes—and developers are increasingly choosing execution over process.   2. Integrations   MCPs are rapidly commoditizing the glue between systems. Being the connector used to be a moat. Soon, it'll be a utility.   3. Data repository   Static repositories are table stakes. Owning customer data matters less than understanding what to do with it.   4. System of action   Now we're getting somewhere. If your software touches money, customers, or real-world outcomes (think Gusto or Shopify), ripping it out is genuinely painful.   5. Full context   The most important new moat in AI. Understanding how a full task gets done across systems, knowing what a user needs across many different sources. This is what agents need to be useful, and the companies that own it become indispensable infrastructure. But it's young and unproven. If context becomes portable (the way integrations are being commoditized now), this moat could erode too.   6. Brand   The moat that survives every platform shift. Trust compounds over time and can't be replicated by a new entrant with better tech. When everything else gets commoditized, the vendor you trust still wins.   7. Network effects   The most underappreciated moat in AI, and potentially the most enduring. But not all network effects are equal. Workflow-based ones (more humans in the tool) are vulnerable. Data and trust-based ones (every user makes the product smarter) actually accelerate with agents. LinkedIn, Doximity, and Slack all still have this. Relatively few AI native companies have prioritized it...yet.   --   The bottom line: Seat-based pricing and workflow stickiness were the backbone of the last era. Both are eroding. The next era rewards companies tied to outcomes, context, and trust.   If you're a founder, stress-test your moats against this spectrum. If your primary defense is "our users do their jobs in our software," you have work to do.   Because the world is moving towards selling outcomes, not software.

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Fireside with a VC E131: Discover how Tyson Tuttle, former CEO of $8B Austin-based Silicon Labs and now CEO of Circuit, decided how to come up with a business leveraging AI. How Circuit is leveraging AI to revolutionize manufacturing, supply chain, and enterprise workflows. Main Topics: · Tyson Tuttle's journey from semiconductor leadership to AI entrepreneurship · The application of AI in manufacturing and supply chain optimization · Strategies for go-to-market, customer engagement, and scaling of AI solutions · The role of VC funding, growth plans, and long-term vision for Circuit AI · Insights into Texas's evolving financial and manufacturing ecosystem · How AI transforms complex sales processes, CPQ, and technical data management · The importance of land-and-expand strategies in enterprise AI deployment · Future plans including Series B funding, customer growth, and Texas-based market positioning YouTube: https://lnkd.in/eusr4wYB Spotify and everywhere else: https://www.7bc.vc/podcast If you are in manufacturing or if you are a VC and want to learn more, reach out to me at andrew@7bc.vc. Special thanks to Circuit cofounder Matt Weiss (ex-Anduril) for reaching out to me about Circuit and pulling 7BC Venture Capital into their pre-Seed round. We are honored to work with this team and support their journey. PS Meet us at 7 am for a weight-lifting meeting in their cool office in Austin.

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Data-driven VC reveals: These metrics predict startup success better than pedigree: → Pivot survival rate → Month-over-month traction velocity → Team cohesion index Full metrics breakdown: https://lnkd.in/gEEmsX_M #DataDrivenVC #Startups #Metrics

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Why was the Fivetran-dbt merger all but inevitable? Fivetran & dbt Labs announced their merger yesterday. The all-stock deal combines two companies into an entity approaching $600 million in ARR. The beauty of the modern data stack was the explosion in choice. As the cloud exploded onto the scene, the legacy data warehouse was replaced by a collection of fast-moving platforms. In that era, specialization won. The pendulum is now swinging back towards consolidation. Why? The answer lies in compute economics & revenue scale asymmetry. The table below shows why. There are three different categories of software within this subset of the ecosystem: 1. Ingestion takes data from software & moves it into a cloud data warehouse. Snowflake acquired Datavolo, which commercializes the open source product Apache NiFi, calling it Openflow. Databricks acquired Arcion for ingestion through change data capture, calling it LakeFlow Connect. Fivetran focuses exclusively on this layer. 2. Transformation means reformatting the data within the cloud data warehouse. Snowflake launched native dbt Projects on Snowflake. Databricks offers Delta Live Tables, native SQL, & Python, plus supports hosted dbt through Databricks Workflows. dbt Core/Cloud is the leading independent transformation tool. 3. Compute revenue is generated when we ask questions of our data. Snowflake remains one of the leaders in structured data analysis with their cloud data warehouse. Databricks’ compute is their own as well. Here’s the asymmetry in one number. Compute represents 72% of the overall market ($7.6B of $10.6B). As a result of their massive operations, Snowflake & Databricks exert significant gravity within the ecosystem. They have expanded beyond the compute market to impose their presence & capture marginal revenue within customers, pressuring the competitive ecosystem. That’s not to say these components are independent. George Fraser analyzed Snowflake workloads in September 2024, finding transformation represents 40-45% of total Snowflake compute, which means even at smaller scales, startups can have significant impact on these behemoth businesses. The Fivetran-dbt merger is an inevitable evolution of a maturing market. Two unicorns must partner to compete against two decacorns. They solve two of the three customer problems. But not yet compute. One could surmise this consolidation signals the end of the modern data stack. I view it differently. The MDS has succeeded beyond our expectations. The stakes are higher now. Broad platforms, fast growth, & AI-native architectures define the next phase. Expect more consolidation. Category revenue estimates based on public disclosures & company filings. Ingestion: Informatica ($1.64B FY2024), Fivetran ($300M est.), Talend ($350M), others ($200M est.). Transformation: dbt Labs ($300M est.), others ($200M est.). Compute: Snowflake ($3.6B FY2025), Databricks ($4.0B ARR Aug 2025).

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Chicago's startup scene is heating up! 🎊Starbridge (AI govtech) raised $42M led by David Sacks' Craft Ventures 🥳Zerohash (crypto) closed $104M Coinflow's CEO made it clear: "You can raise a large Series A in Chicago. You don't have to move to SF." With 213 early-stage rounds in just 3 quarters and $3B+ annual VC, the #7 US tech hub is proving you can build billion-dollar companies in the Midwest...

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The AI agency market is not saturated. You are just looking at the same 200 people online. Nick Saraev , Liam Ottley , Nate Herkelman . Aaron shared a stat today that should make a lot of founders rethink the entire market: 1.7M US businesses need AI implementation. Around 1,500 AI agencies exist. That is 1,100+ potential clients per agency before this market even gets close to crowded. So no. You are not late. You are early enough that the biggest advantage still is not capability. It is visibility. Because most founders are confusing internet noise with market saturation. They open LinkedIn. They see AI agencies everywhere. Automations. Agents. Systems. Workflows. And they assume the market is full. It is not full. It is just loud in your feed. The real market is still wide open. Because the actual buyers are not the people posting about AI every day. They are the business owners quietly falling behind. A dentist in Ohio. A logistics company in Texas. A law firm in Miami. A 20-person team that knows they need to “use AI” but has no clue who to trust. They are not in your comments. They are not reading TechCrunch. They just know one thing: Something is changing. And they do not want to be late. That is the opportunity. A massive market with confused demand and very little clear supply. And that is why this stat matters. The bottleneck is not demand. It is not talent. It is discoverability. The businesses who need help do not know who to call. And in markets like that, the winners are not just the best operators. They are the most visible trusted option. The name they have seen before. The founder who already feels credible before the call. That is how early markets get captured. Through repeated exposure. Through trust at scale. Through being known before everyone else wakes up. And this window will not stay open long. Right now, even average agencies can win because the floor is low. But that changes fast. More agencies. More noise. More identical offers. And once that happens, visibility is no longer an advantage. It is the minimum requirement. That is the shift most people miss. They think they can build distribution later. Later usually means after attention is gone. After the market has already chosen familiar names. That is why the market is not crowded. The visible part of the market is. And the visible part is where buyers decide. That is exactly why we built Vision Reach AI Because most agency founders are stuck in delivery. Client work. Operations. Referrals. They are serving the clients they have while staying invisible to the ones who could buy next. So we handle the distribution side: → LinkedIn ghostwriting → Newsletters → EECs → X growth Built to help agency founders become the obvious name in their category before the rest of the market catches up. You are not late. You are just not visible enough yet.

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2025 State of AI Report: The Builder’s Playbook A Practical Roadmap for AI Innovation Unlike Coatue’s EMW Deck or Mary Meeker’s BOND AI report, this document takes a different approach. It’s based on proprietary April 2025 survey results from 300 executives at software companies, along with in-depth interviews with AI leaders across the ICONIQ community. The result is a tactical roadmap for translating AI capabilities into durable business advantage. What stands out is that — across every dimension: from Infrastructure to GTM and Talent — the report highlights that leading AI Builders aren’t defined just by model sophistication, but by their strategic agility, cost discipline, and ability to experiment fast. 1. Moving from Hype to Hands-On Execution The difference between AI-Native & AI-Enabled companies is crucial. ✅ AI-Native companies are businesses that are built from the ground up with AI at their core. AI isn’t just a tool they use — it’s embedded into their products, services, and operating models from day one. ✅ AI-Enabled companies, on the other hand, are more traditional businesses that integrate AI into existing products, services, or workflows to enhance performance. AI helps these companies improve efficiency, decision-making, or customer experience — but AI isn’t the foundation of the business itself. 2. AI-Native Firms Are 3× Faster - 47% of AI‑Native companies are in full scale vs just 13% for AI‑Enabled counterparts. - These firms invest heavily in agentic workflows (autonomous, multi-step agents) – ~80% do so. - OpenAI’s GPT models continue to be the most popular model; however, many companies are increasingly adopting a multi-model approach to AI products across use cases. They deploy multi-model architectures, averaging 2.8 models per customer-facing product. 3. Pricing Models Reflect AI’s Economics - AI features typically bundled into premium tiers today, but 37% plan to introduce usage-based or outcome-based pricing within 12 months. 4. Talent: The Bottleneck - High-growth firms allocate 20–30% of engineering staff to AI (up to 37%). - However, hiring AI/ML engineers takes over 70 days, with 54% saying they’re falling behind. 5. Budgets Shifting: Cloud > Talent - R&D budgets now allocate 10–20% to AI development, rising across all revenue brackets. - Early-stage costs are dominated by talent, while mature products incur higher cloud, inference, and governance costs. 6. Internal AI Adoption: Access ≠ Usage - Around 70% of employees have access to internal AI tools, but only 50% use them regularly. - Organizations with high adoption support 7+ internal use cases, achieving 15–30% productivity gains. 👋 Connect + Follow me for post like this: Rafael Igual 📩 Subscribe to my Substack rafAIgual HUB (link BIO Profile) you're interested in #AI #native #strategy #design #innovation #execution #growth #leadership #investment in just 5 min. for each issue. 📌 Source: ICONIQ's "2025 State of AI Report: The Builder’s Playbook"

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𝐑𝐚𝐢𝐬𝐞 𝐌𝐢𝐥𝐥𝐢𝐨𝐧𝐬 (𝐁𝐲 𝐇𝐮𝐬𝐭𝐥𝐞 𝐅𝐮𝐧𝐝) A practical, founder-first guide to how early-stage fundraising actually works—based on real investor behavior, not pitch theory. Key Takeaways: 1️⃣ Fundraising is a system: Targeting, sequencing, and momentum matter more than a great story. 2️⃣ Signals beat effort: Investors react to traction, clarity, and risk reduction—not hustle or long decks. 3️⃣ Mechanics decide outcomes: Timing, dilution, and investor selection compound far more than valuation. Bottom line? Fundraising isn’t about persuasion—it’s about engineering proof and leverage. P.S. Want a PDF of Raise Millions? Get it free: https://lnkd.in/guuxKa-J ♻️ Repost to help people in your network. 💡 Follow Alejandro Cremades for more strategy insights.

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“TAO changed my life as a builder.”  Seby, the founder of Resi Labs, on why BitTensor is gives builders an unfair advantage. Instead of begging for hiring, compute, and distribution, you plug into a network where incentives recruit the people, and people compete to give you the cheapest compute.

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5 fundamental operating system upgrades for tech founders trapped under $3m ARR. • Hero ball → Structured plays • Daily firefighting → Quarterly planning • Personality-dependent → Process-driven • Founder decisions → Documented frameworks • "Checking in for approval" → "Here's how we decide" The biggest unlock is building a company that can grow faster than your personal capacity. I see early-stage tech founders repeatedly treating symptoms not causes. • Work longer hours (with diminishing returns) • Launch more features (increasing complexity) • Hire more people (who need more management) • Raise prematurely (adding dilution and investor management) But the bottleneck remains: everything flows through one person. That's why I'm running Elevate CEO Retreat again Sept 8-11. It's 2.5 days of identity and operational reconstruction ↳ To stop thinking like a $500k founder ↳ When you need to execute like a $5m CEO (In a beautiful beachfront location alongside peers at the exact same crossroads) • We X-ray your business in public • Map your specific founder dependencies • Install decision-making systems that scale without you Together, we'll construct the tactical blueprints to rebuild your role, and delete or delegate the drag holding you back. If you're serious about becoming a more leveraged, disciplined and fundable leader, this is your next move. Your next stage needs a different you. 📅 September 8-11 📍 Saltwater Resort, Surf City, North Carolina 🫡 Guest speaker Rt Gen / CEO Joe Anderson 💲 1,500 (includes accommodation, breakfasts, lunches, team event) 🔗 Apply here → https://lnkd.in/ePTcpgWE

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Another missive from Chuck Cotter and Daniel Faierman. Thanks guys for continuing to help to ecosystem. If you've not seen their posts, peek at https://lnkd.in/gPiu9Z4z. Topics include: · CPG Co-Manufacturing Contracts · Early Stage VC Terms to Rarely Accept · More Dividends (and Dividend Recaps?) · 5 Raises, 5 Takeaways · A Founder's Guide to Secondaries · Standing Out Pre-Traction · The State of VC · Corporate Governance 101 · Anti-Dilution Provisions · Primer on QSBS · 4 Fund Raising DON'Ts · Liquidation Preferences · Understanding SAFEs · Understanding Convertible Notes · Understanding Prorata Rights · Founders & Athletes · Employee Stock Options Demystified · The Art of Start-Up Valuation · What Is A Term Sheet Anyway? Check it out ...

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Supervision is the hidden tax on your growth. If you have to be in the room for the business to function, you own a job, not an enterprise. The HHCE standard follows the AI General Doctrine. We prioritize architecture over advice. True leadership is not about oversight. It is about the design of systems that operate with speed, leverage, and intelligence in your absence. This is the core philosophy of The AI General. We build engines that replace the need for constant supervision. When the system is sound, the leader is free to scale. HHCE specializes in creating high performance business infrastructure across healthcare, real estate, and strategic acquisitions. Move from operator to architect. Secure your 30 minute architecture session. https://lnkd.in/eHUaQf4w

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Skilled labor is the bottleneck in industrial ops. Elevāt's platform basically tries to “productize the expert” using machine signals + docs + prior fixes. Now they have $12M to scale. Congrats to them. In industrial AI, the tech is usually the easy part....adoption is the battle. Curious what you think is the real gating factor:  -- trust (techs believe it’s right and won’t get them hurt),  -- workflow integration (it fits into CMMS/ticketing/parts + works offline/on-device), OR  -- incentives (the field team is rewarded for using it vs “just fixing it their way”). Which one kills deployments most often in your experience?

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AI isn’t killing venture, SaaS-era valuation math is, the “AI margin tax,” why $100M ARR can be a mediocre outcome at 25–60% gross margins, and the one metric (contribution margin per action) founders and investors should obsess over. https://lnkd.in/gpiR6APY

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While everyone was debating whether AI would replace consultants, OpenAI just became one. Their new consulting arm charges a minimum of $10 million per engagement, embedding Forward-Deployed Engineers directly into client organizations to build custom GPT-4o implementations. This isn't just another enterprise sales play. It's vertical integration disguised as consulting. Despite OpenAI reaching $10 billion in annual revenue and serving over 500 million weekly users, they're still burning through approximately $5 billion annually. API subscriptions alone aren't cutting it. If OpenAI, which started the ChatGPT revolution, needs to move into services, what does that mean for smaller AI startups? Google, Anthropic, Meta - they're all building comparable models. The differentiation isn't in the core technology anymore. It's in implementation, integration, and delivering measurable results. So what's the play for everyone else? 𝗕𝘂𝗶𝗹𝗱 𝘁𝗵𝗲 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 → 𝗢𝘄𝗻 𝘁𝗵𝗲 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 → 𝗗𝗲𝗹𝗶𝘃𝗲𝗿 𝘁𝗵𝗲 𝗼𝘂𝘁𝗰𝗼𝗺𝗲. OpenAI is shifting from the first to the second. The real money is in the third. The future belongs to vertical solutions that understand specific industries deeply enough to deliver turnkey outcomes. Not APIs. Not platforms. 𝗥𝗲𝘀𝘂𝗹𝘁𝘀. This is why I'm betting on micro-SaaS in specialized verticals. When AI becomes commoditized infrastructure, domain expertise and implementation capability become crucial differentiators. Pure technology plays are getting harder and harder to defend. APIs are becoming commodities. Implementation is the new IP. I share more musings on my substack >> https://t2m.io/4aDyNCpu For the love of the game 🏴‍☠️⚡️ #ai #futurism #chatgpt #openai 𝘐𝘮𝘢𝘨𝘦 𝘊𝘳𝘦𝘥𝘪𝘵: 𝘚𝘪𝘭𝘪𝘤𝘰𝘯 𝘝𝘢𝘭𝘦𝘳𝘪𝘦 𝘉𝘦𝘳𝘵𝘦𝘭𝘦 

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