Sahil Sahni

AI memory is fundamental for conversational AI and AI agents. It is critical for building personalized experiences and improving reasoning. Check out Letta’s course to understand how to use AI memory to enhance the customization and quality of your AI app.

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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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I first met Mike Maples, Jr in 2010, when Founders Network was just an idea. Over the years, Mike and his team at Floodgate (special shoutout to Arjun Chopra) have been great mentors, sharing their insights and guiding our founders. Most recently, their advice played a pivotal role in helping us launch Founders Network Fund. Today, Mike shared the key ideas with FN'ers from his new book Pattern Breakers (https://a.co/d/buKBd5O ), which explores the insights he’s gained from backing companies like Twitter, Twitch, and Okta -- and which put him on the Forbes Midas List 8x. This is the most important book on startups in the last ten years, according to Steve Blank. My notes on the key concepts of his talk: 1) Inflections – "not a trend but a change event that represents a turning point in our ability to provide empowerment." e.g. The GPS chip in iPhone 4s 2) Insights – "a non-consensus and right insight about the future that's powerful and non obvious and leverages inflections." e.g. Lyft = GPS + strangers driving you 3) Founder future fit – "Living in the future before other people live in the future and then try to build what's missing in the future." If you're a founder, check out the book. Grateful for the impact Mike and Arjun continue to have on both me and the Founders Network community!

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PE - How to Use AI to Unlock Growth in Portfolio Companies All PE firms and portfolio companies should be thinking about unlocking GTM and growth via AI (i.e. not merely ChatGPT or other LLMs but actually implementing apps that are "AI Agents"). 1. What are "AI Agents"? - AI agents are autonomous systems powered by ML and natural language processing, designed to perform tasks with minimal human intervention - They can interpret data, determine the workflow & direction (i.e. make decisions), and execute actions across various functions, continuously improving through self-learning 2. Examples of using AI Agents in PE portfolio companies: - For Sales: AI agents handle lead qualification, respond to inquiries, book meetings, and provide personalized product recommendations, ensuring no opportunities are missed - For Marketing: Generate campaign briefs, segment audiences, create tailored content, and analyze performance to optimize outreach efforts - For Customer Success & Support: Provide 24/7 support, resolve customer issues autonomously, escalate complex cases to human agents with full context, and ensure consistent, brand-aligned responses 3. How PE portfolio companies benefit: - Efficiency: Automate repetitive tasks, enabling teams to focus on high-value activities - Scalability: Handle growing customer volumes without increasing headcount - Personalization: Deliver tailored experiences using real-time data insights - $ Cost Savings: Reduce operational costs by streamlining workflows 4. Business Impact for PE Portfolio Companies: - Accelerate GTM Execution: Faster lead conversion, better customer retention, and enhanced marketing ROI - Improve Metrics: Boost Customer NPS, CSAT, and efficiency - Enable Growth: Scale operations seamlessly while improving team productivity. 5. How PE Firms Must Think About Adoption - Define clear portfolio objectives to use AI agents (not just LLMs) - Ensure integration with existing CRM and data systems - Prioritize high-quality data and ongoing performance monitoring - Use AI agents to complement existing GTM teams, focusing them on automation ------------------ #pe #privateequity #investing #ai

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If your startup competes with a large incumbent, how can you win against their superior resources? More importantly, how can you sustain advantage over time? Most pitches don’t answer these questions at all. I offer a new strategy framework, the Can’ts and the Won’ts, that does. ¶ Can’ts and Won’ts are two types of differentiators. A Can’t is something your competitor isn’t able to do. A Won’t is something they have important reasons not to do. ¶ In common parlance the word “can’t” is used loosely, but for our purposes we mean it strictly. “They can’t match our science because we have the PI and an exclusive license” is a potentially valid Can’t. “They can’t match our price because it would crater their stock price” is not - they 𝘤𝘢𝘯 do it, but they choose not to because of expected consequences. That’s a Won’t. ¶ When you’re up against a powerful incumbent, Can’ts are hard to defend. The larger the market, the more this is true. Won’ts, conversely, tend to be more sustainable. Let’s see why. CAN’TS: Suppose your team of five engineers has built something clever, and the competition doesn’t have it. They can’t meet your customers’ needs the way you can. Yay! But hold on… as soon as you get big enough for them to notice, they can assign fifty engineers and catch up fast. In fact, that may be an optimal strategy for them - let lots of startups try things, then fast-follow what works. Or suppose you have a patent, so the competitor can’t do exactly what you do. Well… many patents can be worked around, especially if the other guys are sufficiently motivated. Ultimately, 𝗖𝗮𝗻’𝘁𝘀 𝗰𝗼𝗺𝗲 𝗳𝗿𝗼𝗺 𝘀𝗰𝗮𝗿𝗰𝗶𝘁𝘆. Ask yourself, how scarce is what we have? and for how long? Try a red team exercise - if you were the incumbent, how would you overcome that scarcity? WON’TS: While Can’ts come from scarcity, 𝗪𝗼𝗻’𝘁𝘀 𝗰𝗼𝗺𝗲 𝗳𝗿𝗼𝗺 𝗮𝗯𝘂𝗻𝗱𝗮𝗻𝗰𝗲. The competitor is holding onto something valuable, and they’d have to loosen their grip on that something to chase you down the rabbit hole. The magic of a Won’t, like the magic of a great poker bet, is that it forces the competitor to make a tough decision. If they can crush you without putting their existing business at risk, that’s not a Won’t; but if you can force them to choose between one and the other, then you’re onto something. Strategy wonks out there may recognize the overlap with other competition frameworks. i) Disruption Theory says the incumbent won’t participate in a lower-margin market until it’s too late. ii) Blue Ocean uses Won’ts to build advantages that can ultimately be turned into Can’ts. iii) Porter’s “What is Strategy” is a system of self-reinforcing choices (ie Won’ts) that together create a defensible Can’t. So, if you’re into that sort of thing, I offer the Can’ts and the Won’ts as a sort of Grand Unified Theory of competitive strategy. If you’re not, just take it, standalone, as a way to make yourself robust against a competitor’s countermoves.

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PE - 3 ideas for the next 3 years - just like every traditional company needs to be a SaaS company, every SaaS company needs to ultimately be an AI company - ramp up revenue growth but always protect the profits - today is the time to innovate (i.e. not re-invent) your business model & strategy - importantly, this applies not only to PE portfolio companies but also to PE firms to get the best IRR --- #pe #privateequity #business #strategy #saas

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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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During yesterday's LibreQoS APNIC webinar, I posed the question: won't more bandwidth solve these problems? As Herbert Wolverson said, yes, more bandwidth is good, but, still these problems remain if queueing is done incorrectly. Here's my take on why bandwidth alone is not as good as bandwidth + good queueing policies: Generally it was believed that 'data is important, so don't throw it away; hang on to it and send it later'. In practice, this has proven to be suboptimal as two issues may emerge: 1) latency increases for some flows due to heavy demand from other flows using the same bottleneck link, 2) even a single flow can have excessive latency due to aspects of typical TCP behavior (referred to bufferbloat) when the buffers grew large enough that the data being buffered was retransmitted anyway. It turns out that not all data is equally important. Active Queue Management is the art of deciding priorities, both in deciding what data to throw away, but also in allowing some later arriving data to be transmitted ahead of data in another connection that arrived before it. These problems have been studied, and good solutions have been found by using certain queueing policies in routers and switches, referred to as “fq_codel’ and “cake”. These track the different flows not by classifying the data, but by watching the behavior. Flows that send relatively little data (DNS lookups), or at a measured pace (video chat) have priority over flows that send a lot of data as quickly as possible (App and System updates, Video and ISO downloads).

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At Forum we look at thousands of deals each year across our Accelerator and Pre-Seed funds but only end up investing in a little over 1% of those startups.  As an early stage VC, I am in the business of saying no to nearly every startup I meet, and while there is nuanced rationale for many of the startups I pass on, a vast majority of businesses that don’t meet the qualification to get funded typically fall into one of two buckets. ▪ First time founders have a tendency to go out to market too early to raise pre-seed capital ▪   I’m sure most founders have heard some iteration of this from VCs that ultimately pass on their business.  In some cases it's a blanket excuse covering up for other pass reasons- perhaps a VC doesn’t believe in a team or isn’t bullish on the market opportunity - but in many cases founders simply haven't proven enough to the market to warrant an investment. I meet many teams with an articulate vision for the product that they want to build, but they either haven't performed enough customer discovery to prove demand for their initial wedge product, or need to raise pre-seed capital to build out an MVP.  In those cases, pre-seed investors are unwilling to underwrite those risks.  To be default fundable, teams need to be able to keep costs low, perform extensive customer discovery, and ship some version of an early product - pilots and early paid customers are key. A huge piece of this is having founding engineering talent on the team. ▪Not every startup is a venture opportunity ▪ By definition, a venture backed business is solving a problem within a market opportunity so massive and so desperately needed, that its growth rate is off the charts.   It spreads like a weed, absorbing almost unlimited amounts of capital to fulfill demand within its customer base.   Each year there are maybe 20 companies launched that ultimately have venture scale outcomes. The bar to reach this rarified air is unfathomably high and the reality is the vast majority of founders simply aren’t building for opportunities that have the potential to be one of these generational companies. Many startup founders view VC as the only viable option for capitalizing their business, but not every great business is a venture business, and most successful founders never take VC dollars.  In order to reach an IPO/acquisition large enough to generate venture returns, a startup needs a credible path to $100s of millions in revenue. This is the standard by which early stage opportunities are evaluated. The flip side to this, and I share this feedback often with founders I meet that are building interesting business in non venture markets, if you are able to scale to $10- $20M in revenue that can be a massively successful outcome for a founder.  There are investors who focus on these type outcomes, namely lower middle market PE shops, and the typical path to those deals is a bootstrapped business or a friends/family capital raise.

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What Really Makes a Founder-Investor Relationship Work? 💡 Engaged Participation: The best investors don't just attend meetings; they immerse themselves in the startup journey. I recall working on our GTM strategy and facing three tough choices. One investor suggested, "Why not try all three and see what works?" That advice broke down mental barriers and led us to new opportunities. 🤝 Building Trust: Trust grows through genuine, agenda-free conversations, empathy, and a true interest in personal connections. I genuinely appreciate discovering the person behind the purpose. 🗣️ Authenticity and Transparency: Absolute openness is crucial. Discussing dealbreakers and having tough conversations early on leads to smoother sailing later. 🌐 Extending Networks: Aligned investors broaden a startup's reach, introducing key partners and customers, strengthening the network. At FEBE Ventures, we aim to be the first call founders make when challenges arise. We take pride in being trusted advisors to the entrepreneurs we work with. How do you think investors can make a meaningful difference in your journey? Would love to hear your thoughts.

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I am always amazed when someone discovers whitespace in B2B SaaS that is eligible for pre-seed funding. It's interesting to see that Chiratae Ventures led Pre-Seed round for Pepsales. Pepsales is trying to help B2B SaaS companies improve their sales by creating personalized product demos using AI and ML. The pain point they are trying to address is that most demos don't impress buyers because they tend to be generic and one-size-fits-all. Congrats Ajay Singh and Abhinandan Sahgal! Source: INC42, Entrackr, VCCircle #VentureCapital #B2BSaas

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The newest issue of The Pragmatic Engineer found a surprise uptick in eng hiring in the typically slow summer season - I'm curious to see how the macro environment impacts this uptick as we head into the fall, but I remain hopeful. I asked Gemini to give me the tldr of the article to share with my team (sorry, Gergely Orosz) and found it missed one of the most interesting pieces of the article. Gemini's highlights - Unexpected surge: Tech recruitment has seen an unusual spike in June and July, contrary to typical trends. Increased demand: Startups, especially AI-focused ones, are driving the hiring uptick. Global phenomenon: This trend isn't just limited to the US; Europe is also experiencing increased recruitment activity. Reasons for hiring: Companies are backfilling positions, investing in growth due to improved financials, and responding to increased VC funding. Challenges remain: While the job market is improving, finding qualified candidates and offering competitive compensation remains difficult for many companies. What I found most interesting - The nature of the backfills: the article finds that a lot of new headcount are actually backfills of folks that were "over-slashed" during rounds of layoffs. Not entirely surprising with the "trim the fat" and "rebuild lean" mentality, but interesting nonetheless. Either way, the uptick is encouraging (and don't always rely on Gemini's tldrs). #hiring #startups #VCbacked #softwareengineering

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A common mistake early-stage VCs make is confusing good decisions with good outcomes. Good outcomes don’t always mean good decisions—luck often masks flawed reasoning. Focus on process over outcome bias. Invest in strong decision-making frameworks, not just duplicating “wins.”

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🔑 The Power of a Written Exercise in Interviewing Revenue Leaders 📝 Hiring the right revenue leader can make or break an organization. As we search for smart folks to drive our revenue growth at Vanta, one approach has consistently proven to be a game-changer: the written exercise. 🔍 Why a Written Exercise? Clarity of Thought: A well designed exercise reveals a candidate's ability to articulate complex ideas clearly and concisely. This skill is vital for revenue leaders who must communicate strategies across diverse teams and stakeholders. Strategic Insight: Through real-world scenarios, candidates demonstrate their strategic thinking and problem-solving abilities. This helps us gauge their approach to challenges and opportunities unique to our business. Analytical Prowess: Written exercises allow candidates to showcase their analytical skills. By dissecting data and presenting actionable insights, we can see how they drive decisions that lead to growth. Cultural Fit: How a candidate approaches a written task can also give us a glimpse into their work ethic, attention to detail, and alignment with our company values and culture. Elliot Goldwater has really nailed this practice, and it's incredible how much the exercise can swing a process towards or away from a candidate. 👥 Have you implemented written exercises in your hiring process? What has been your experience? #RevenueGrowth #Leadership #HiringTrends #Interviewing #TalentAcquisition #Strategy

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Here’s HBR’s take on some of the ways VC and PE firms are deploying an alternative data strategy to gain a competitive edge for their brand and portfolio: 1. Algorithmic sourcing - distilling the novel from the noisy: detecting patterns, trends, anomalies. 2. Due diligence - the old school way is often constrained by time and resources: VCs are deep diving, deploying LLMs to do a full analysis (see pharma example in article). 3. Portfolio company value creation: VCs and talent sourcing, Eg; one VC aggregated Google Scholar, GitHub, Quora, Kaggle, and LinkedIn to identify specialized talent to help assemble strong teams for their port cos to kick (and reach) their goals. Even with AI, effectively harnessing the vast amounts of external data is no small feat. Info is highly fragmented and uneven in quality, and demands dedicated resources and expertise to assess what’s most likely be useful, and navigating the thousands of products on the market. Onwards! #AI #Investing #PrivateMarketInvesting

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https://lnkd.in/gBVzfWkH Databricks raises $10B, capping off a great year for VC funding of UC Berkeley companies! Read all about it at the link below

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AI is reshaping how enterprise software delivers—and prices—value. Companies like Zendesk, Intercom, and Forethought are shifting to outcome-based pricing, charging only when AI autonomously resolves issues. I've been investing in AI for over a decade. The shift to outcome-based pricing in enterprise software is a seismic change. As I told Jon Victor at The Information, software companies now face a critical decision: Does a company decide to change business models at the expense of losing revenue? It's a necessary risk. With cheap access to LLMs, businesses can build their own enterprise apps. Adapt or die. Why this pricing shift matters: → Vendor success ties directly to customer outcomes → Short-term revenue risks enable long-term market expansion → Industry faces widespread pressure to demonstrate measurable value Pitching AI-powered automation software as a clear cost-reduction tool could ultimately lead to greater revenue for vendors. This is because customers may view AI solutions as fundamentally different from traditional enterprise apps, whose business value is often harder to quantify directly. It's a huge market expansion opportunity. Vendors that nail outcome-based pricing could see massive growth as AI transforms enterprise operations. Those that don't? 😬🫣 How do you see this impacting sectors beyond customer support? Leave a comment. And if you're building in this space, send me a message.

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