AJ Shankar

PSA: If you send a founder product nits + feedback with concomitant screenshots/mocks you instantly vault into being an S-tier user. (Friction logs are even better, of course.)

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🎯 “do deals that the firm will sign off on” is by far the biggest problem of the industrialized vc ecosystem of today. this current system has started to essentially drive *only* deals that are likely to be approved by the ic …and is unlikely to create outcomes that vc is meant for. we have observed this closely over 5 years and 100+ follow-on on rounds that we’ve been closely associated with. to each their own… …but in a shallow vc ecosystem like India this means only a certain type of businesses will raise capital — hardware companies struggle (inito, cradlewise), deep tech companies struggle (eeki, praan, mowito), fresh ideas struggle even when there are worse versions of theirs who have raised tons and proved that they don’t work (marketplaces vs full stack models) and of course the elephant in the room - the non-pedigree founders struggle!! good news is … things are starting to change slowly but surely. those names above … most raised top tier global capital and are crushing it.

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Just finished an interview with CNBC at SuperReturn in Berlin, where we discussed opportunities in enterprise software and how GenAI will revolutionize the software sector. At Thoma Bravo, we see AI as an opportunity, not a threat, and software incumbents are in a great position to benefit from this technology. Over the years, we've seen enterprise #software evolve from the limitations of on-premise to the flexibility of SaaS, and now, GenAI is set to redefine business operations. We're actively embracing #GenAI's transformative power and are already seeing products emerge within our portfolio companies. Focusing on #AI is more important than ever, and we're excited and optimistic about what this means for the future of enterprise software. https://lnkd.in/ePHUTjNe

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Authorization is one of the more complex (and, let's face it, thankless) tasks about building an application. Oso has emerged as the leader in abstracting #authorization as an independent service; just like authentication.

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I had an absolute blast with Michel Gagnon and the Stun and Awe crew - THANKS again for having me... Sneak peek of the substance below: > Micro SaaS companies can be just as safe and stable as larger businesses, offering lower valuations and less competition from institutional players. > Founder-product fit is crucial, as founders solving their own problems often create useful products with great retention. > Founder burnout can be a buying signal, indicating a solid business with a founder ready to move on. > The first 90 days post-acquisition should focus on visibility, documentation, transition, and validating diligence assumptions before pursuing growth. > Building transparently and maintaining open communication with users post-acquisition helps preserve the human element and avoid the pitfall of going silent. > Simplicity equals mastery - if it takes a complex explanation to justify an acquisition, you probably don't fully understand the business. > Establishing reasonable incremental growth goals based on baseline performance (systems over goals) is more effective than setting arbitrary targets. > Fostering a culture of care and respect enables honest feedback and allows teams to move faster through a shared commitment to truth-seeking.

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As an economist, I obsess (probably a little too much) about profit, cash flow, and IRR. It's one thing to see gains in paper valuations, but those are just a proxy. Sure investors will vary in their preferences for liquidity, duration etc. but in the end they all require the same thing -- realized returns. There are a ton of companies in limbo right now where they can't quite exit nor are they in a position to generate a dividend. For investors, this is a tough spot to be in, and even more so when it's a systemic trend affecting their whole portfolio rather than a one-off.

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Nice overview of how we're using data to inform how we invest in early-stage founders. We'll be announcing more this summer but we're seeing some really interesting early data. https://lnkd.in/exU_Juf5

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People buy software for 2 main reasons: 1. Increase Revenue 2. Reduce Cost You can hear more form Tomasz Tunguz of Theory Ventures on this week's episode of TFR.

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TWO OR THREE-STEPPING TO SERIES A A lot of software founders are still trying to raise a $4-5M seed round from a pre-revenue standpoint to get to Series A. I get that we all want more buffer nowadays but it's also a harder calculus to go from pre-monetization, to early customers, to giving yourself enough sales cycle time in order to hit $1-2M ARR proxy for a strong A. Which is why inevitably, more founders end up having to raise messy extensions/bridges/Seed II or whatever you want to call them. So my advice is that IF you have the luxury of starting a company today, PLAN AHEAD. Even prior to even raising your first angel round. Break up your path to Series A into 2 or 3 tranches of mission-driven capital. Each tranche has a clear goal that you can rally your team and investors around. For example: First tranche to build the product and get early design partners. Second tranche to get to $300K of ARR. Third tranche to get to Series A. This will help support higher operating discipline and awareness. Incremental purchasing and hiring decisions will instantaneously shorten your runway in a way that a larger $5 million seed round obfuscates. And you'll be motivated to find GTM efficiency faster in the journey. Great habits for long term success. For context, Peter Walker of Carta recently sent an update on the arduous path from Seed to Series A. The survival rate is about 1/2 of what it used to be: "Something like 20-24% of startups that raised a seed round would graduate to their Series A in 24 months or less. For companies that raised their seeds in H1 2022, only 13% have made it to Series A across the 6 industries in the chart."

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Key takeaways from VC Goat - Vinod Khosla podcast. - The firm cares more about working on interesting problems with large technical solutions rather than just maximizing IRR. The team is there because they believe in the mission. - Khosla assumes they've lost the money the day they invest, and then maximizes for the upside opportunity. He calls it "option value investing" rather than IRR investing. - Khosla has contrarian bets in AI (neuro-symbolic computing, probabilistic approaches), biotech, robotics, crypto, and more. - He believes aviation fuels, fusion energy, new transit systems, and other contrarian areas ignored by most investors will be huge opportunities. - Most large innovations come from outsiders, not industry insiders. Khosla looks for founders who can learn a business rapidly rather than have deep domain expertise. The Future Impact of AI - AI will be deflationary and increase productivity growth to 4% annually vs the typical 2% forecast. This will cause great abundance but also increasing income inequality. - Bipedal robots will take over most manufacturing and manual labor jobs within 20-25 years. This will free humans to be more creative and pursue their passions. - Education will shift from job training to creativity. Uniquely human elements like taste and curation will be most valued in an AI-enabled world. https://lnkd.in/gqBsmbks

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New on First Round Capital Review, Will Larson digs into why common management practices have become anti patterns, and which ones engineering execs should lean into to sharpen their team strategy. Always fun to learn from old Stripe co-workers :)

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~40% of Frec’s customers are engineers working in tech, many of whom expect frequent and large capital gains. They also prefer to self-manage their money, which makes Frec an ideal platform. Meet Evan: an engineering leader who upgraded from investing in ETFs to direct indexing. Direct indexing appealed to Evan because it offers comparable returns to his ETFs while saving him money on advisory and ETF fees. “ETFs are true and tested, and giving folks a different way of investing in the market while saving more money is really appealing.” He’s used many other robo-advisors and other fintech tools, but “Frec seems to maximize tax loss harvesting opportunities better than any other product that I’ve used.” He also likes Frec's transparent fee structure.  “I feel like with a lot of other companies, that’s muddled in with the cost of investments,” he told us, “and you don’t really know how much you’re paying or how much you’re not saving as a result. I think Frec does a really good job of laying all of that out and giving you a true understanding of the cost of these investment products.” Read more about his Frec journey here ⬇

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Instacart is a market leader when it comes to leveraging AI tools to boost productivity at work. I had an amazing conversation with JJ Zhuang, Instacart’s Chief Architect, who has shepherded over 50 AI-driven initiatives to market across the org. We explored what catapulted Instacart to the forefront of AI integration, supporting radically diverse use cases across various functional areas. One striking insight (echoed by Ethan Mollick and others elsewhere) is that you often don't need specialized tools for specialized work. Frontier models like GPT-4 can seamlessly adapt to a wide range of functions and use cases. They're not just for engineers; they're incredibly versatile tools used by marketing, comms, and legal teams. From generating code to proofreading and ideation, GPT-4 empowers every department to leverage AI for enhanced productivity and innovation. By integrating LLM’s like GPT-4 into their workflows, teams can streamline processes, automate repetitive tasks, and unlock bandwidth for new creative possibilities. How can you harness the power of AI in your own work to boost productivity? Share your thoughts below 👇 Want to hear more insights from JJ? Check out the full podcast episode by clicking the link in the comments.

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In the AI-first world, the biggest moat enterprises have is their data -- but most enterprise data is a mess. Enterprises have a few options: hire a team to do rote data cleaning tasks, or leverage advancements in AI to automate data labeling. Proud to be partnering with the Refuel team as they develop purpose-built AI for unsexy data tasks!

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Founder <> founder convos are the biggest unlock for company building. I spent the last 2+ weeks in SF. Besides meeting with customers, I spent a ton of time with founders / early stage builders that I deeply respect (shoutout to Abhinav Asthana, Christina Cacioppo, Kevin Van Gundy, Akshay Kothari, Yuhki Yamashita, Tara Viswanathan, Celine Halioua, & Michelle Valentine). I’m extremely grateful to have the chance to spend time with these folks. Some reflections from these brilliant people. 1/ People > everything. A company is a collection of people. Company success comes down to hiring the best people. 2/ Startup problems aren’t *that* unique. Startups are HARD and even if you think your challenges are specific to your company, they’re likely not. Someone else has faced the same thing, and you likely don’t need to reinvent the wheel. 3/ Let some fires burn. Focus > trying to do everything. As a startup, you can’t fix everything at once with limited resources (even if you want to). You need to focus on the stuff that will move the needle the most. Pick the top 1 or 2 problems, fix them, then move onto the next. It’s okay if some fires need to burn for a little in the meantime. 4/ Company operating cadences are key. We’re redoing our cadences now, so it was super interesting to learn how others run their board meetings, leadership meetings, all hands, etc. A consistent theme for board & leadership meetings is to have everyone complete a prompt in written doc, share with each other, then use the time to have meatier discussions. 5/ Be competitor aware, customer obsessed. Competitors cause unnecessary noise. Build products and make decisions based on customer / market need and founder intuition, never based on competition. Are these learnings helpful? If so, can share more founder learning posts in the future!

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Your company's equity can make or break your startup. As a 2x founder, I’ve had the privilege of fundraising from top-tier investors (Craft, Naval, Hustle Fund, 8VC, Loup, and 100+ others), investing early in Notion, Mercury, Rippling, and Pulley, and serving on the founding team & COO @ Forge (NYSE: $FRGE, $2B IPO). I often get DMs from founders fundraising at the early stage, people looking for help with compensation plans, and those interested in pre-IPO secondary markets. All of it boils down to: how do I allocate the equity in my company? How do I apportion a scarce resource to maximize my odds of success? So, I've opened up five 1:1 feedback slots on Intro. Half the proceeds will go to charity (you're welcome to nominate your favorites!). What can we cover together? ✔️ Fundraising strategies for early-stage founders ✔️ Designing effective compensation plans ✔️ Navigating liquidity & pre-IPO secondary markets Honored to be be on Intro alongside world-class experts like Phil Neil (Founder of Neobex (sold), bootstrapped from startup to $70M in rev), Patrick Lor (Managing Partner, Panache Ventures), Adeel Shams (founder of Coolkicks - most followed sneaker resell brand in the world, 2M on Youtube.) and Grace (Fooden) Correy (founder of Biophile Skincare. Ex: SVP at Spanx & VP at Ralph Lauren). Drop me a note in the comments below (e.g., “interested”) and book a chat: intro.co/hariraghavan

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As a General Partner investing institutional venture funds in B2B SaaS at seed stage since the year 2000, I saw a lot of cycles: the "dotcom" crash (started April 2000, bottomed out October 2003), the financial crisis (started August/Sept 2008, bottomed out 2011) and the "mother-of-all-over-hyped-valuation" era that ended in late 2021. Today, we are still seeing 75%-90% markdowns on Unicorns like Carta or worse, shutdown of Unicorns like Olive AI (from $4 billion valuation to ZERO$, $800 million raised--and apparently wasted...). Many more to come just like those. All those great reported returns for venture funds from that era's wild markups? The venture funds are marked down right along with their portfolio companies. Easy come, easy go. I feel very fortunate that Valor VC's first institutional venture fund was invested after that "vastly-over-hyped" period that culminated in 2021. Valor's new fund, Valor Fund 3, is vintage 2024. The valuations for seed stage companies? They have entrepreneurs recalling wistfully 2021. Today, seed stage investors are "licking their chops!". Vintage 2024 should be a record returns vintage year! There are so many VCs licking their wounds from 2021’s peak to today’s trough, there are many not investing in new deals at all, and, for nearly 30% of all VCs, they will not raise another new fund, ever. It is GRRRRRRREAT! time to be investing in B2B SaaS at seed! This next era, AI/ML, is literally the most profound technological revolution in history. It is happening when, at the same time, seed stage is completely rational in its valuation in a way I have never seen it before. Great founders, doing great things and scaling rapidly with rational valuations, rational capital consumption/burn and very clear paths to profitability! I couldn't ask for more! PS Yes, Generative AI is wildly over-hyped and over-valued. However, applied AI in B2B SaaS? It is way undervalued given the triple-digit growth rates of so many applied AI investments in Valor's portfolios!

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Bold prediction for 2025: Investors will pay founders to access their venture data. Founders own their data and grant access to investors for a fee. This shift promotes transparency, trust, and efficiency in fundraising, while giving startups a new revenue stream.

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We see automation as the shift to self-driving software. This means actual task completion, not the creation of more tickets for humans to eventually get to.  One framework we like to use internally is self-driving cars as the mile markers on the road to autonomy are somewhat analogous. Both start with human-assisted automation, then mature to greater levels of automation. Both move from human-in-the-loop, to human-on-the-loop. 🔹 Level 1 is if-then, rule-based automation. While useful, this basic, rigid automation is unable to adapt to uncertainty and thus is limited in scope. 🔹 Levels 2 and 3 are worker-assisted automation. An LLM or foundational model chain can generate outputs stochastically, and eventually piece things together using multiple models (think voice -> text; text -> text; text -> image). But there’s still a human in the loop. 🔹 Levels 4 and 5 approach full automation. Tesla reached this level recently with FSD 12 through imitation learning. They captured massive datasets tracking human drivers of their 5 million cars. With this data, Tesla could instruct the car to self-drive and fall back to imitating the best human drivers (the top 1% of the data). We think AI Agents will similarly mature with auto-critique and validation that defers to and imitates the best human workers. Eventually, teams of Agents may even empower enterprises to have department-specific AI Agents and make every human a manager. We’ll share more Activant Capital Research on AI Agents and self-driving software. Reach out to Jonathan, Nina Matthews, Marc W. , and me for early access! #Automation #AI #Aiagents #AIagent #software

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