Vivek Vaidya

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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We're launching the inaugural edition of Beyond Benchmarks, a deep dive into the metrics and trends observed across the early-stage enterprise cloud market. This is the first look at some of the business data around GenAI in SaaS. Some initial observations: 1) 60% of SaaS companies have already integrated GenAI into their offerings (60% of these as part of their current product and 40% as a new product), and an additional 20% are planning to do so this year. Almost half aren't monetizing these features. Those that are are primarily experimenting with both usage-based and flat fee pricing models. 2) Most companies are utilizing OpenAI as their primary LLM, but many are experimenting with multiple models - and we are noticing an early trend towards intelligently routing GenAI inference requests to different models based on cost, performance, and security. 3) Though we are in the initial innings of GenAI, we are seeing some positive signals - companies that implemented GenAI had 7% higher NDR than those that did not. Still early days, but we'll be doing this benchmarking report regularly to track and share how things progress in this new era. Link to report in comments.

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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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In vertical SaaS, the very best companies landed with a product solving a specific, burning pain point that horizontal solutions solved badly and then became broad product suites over time (ServiceTitan, Veeva Systems, Doximity, etc). My partner Gordon Ritter calls this “layering the cake”. In vertical AI, the landing wedges are much more brittle. While the painpoints they solve are very real (and often unaddressable by SaaS alone), they’re also much more likely to commodotize in the near term as the tech advances. 👀 Therefore, the need to layer the cake in vertical AI is much more urgent than in vertical SaaS. 👀 The best vertical AI CEOs not only recognize the dynamic, but talk about it openly. Helps those around them keep up the speed of innovation and their eyes on the bigger prize.

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Grammarly just acquired startup Coda (last valued at 1.2bn) with a goal of a possible IPO. This could be a big move in productivity, especially for product teams. Coda CEO Shishir Mehrotra was recently on Lenny's podcast (first comment below). https://lnkd.in/gmgfcm2P

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Open-source models can be a strong lever for increasing gross margins at AI companies, especially those that have hefty cost structures (e.g., enterprise sales orgs that require significant customer success or implementation resources, as well forward-deployed AI services models common in healthcare and life sciences). One of many reasons to maintain a modular and flexible stack with minimal dependencies when building an AI company. More on the importance of flexibility in the AI stack: https://lnkd.in/eA2XATRk h/t Delip Rao #ai #artificialintelligence #generativeai #healthcare

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As we explore a seed round of VC funding for Avnir, a mentor has driven into me to "interview them as much as they'll do their due diligence on you!" When I consider professional etiquette important to me (and probably every serious founder), I am reminded of what I've coached hundreds of executives on over the past two decades: "Your culture is a culmination of behaviors you're willing to tolerate!" So, here is a Top 10 List of Professional Etiquette crucial to long-term strategic relationships between VCs and founders: 1️⃣ Respect for Time—Punctuality, Efficient Communication, and Timely Feedforward are demonstrated and genuinely felt. We would both rather me invest time in building the business. 2️⃣ Transparency—Clear Expectations, Honest Feedforward, and Disclosures on conflicts of interest or resource limitations save everyone unnecessarily wasted cycles! 3️⃣ Professionalism—Politeness, Confidentiality, and Objective Evaluation allow courteous interactions, respect for proprietary information, and avoiding bias in evaluating the founder's meritocracy. 4️⃣ Empathy—Understanding the Journey and Constructive Dialogue acknowledge the uphill battle founders face while avoiding unnecessary criticism and providing supportive guidance, demonstrating a genuine interest in their vision. 5️⃣ Integrity—Fair Terms, Follow Through, and Avoiding Overpromising honor commitments during discussions, and create realistic expectations from the relationship. 6️⃣ Co-Creation—Value-adding Mindset and Team Player Attitude accelerate the founder's ability to gain traction and position the VC relationship as transformational vs. simply transactional. 7️⃣ Humility—Avoiding Arrogance, Condescension, and Open-Mindedness acknowledges that both parties have unique expertise to add and enhance the receptivity to unconventional ideas, approaches, and perspectives. 8️⃣ Listening Louder—Present, Engaged, and Clarity of Intent eliminates distractions, brings out what's not being said, and jointly crafts better questions to help uncover the unknown and the undiscovered. 9️⃣ Encouraging—Supportive Communication, Positive Reinforcement, and Inspiration help celebrate small wins and key milestone achievements. It's ideal to light a fire within the relationship rather than under anyone, which seldom lasts! 🔟 Playing the Long Game—Relationship Nurturing and Post-Investment Care establishes the runway for a lasting partnership rather than simply pitching a deck and closing a deal. After the investment decision, sustaining the relationship will determine the partnership's long-term perspective. Jason M. Lemkin, Peter Bell, Cassie Young, and Ed Sim - you exemplify world-class in the relationships you build with your founders. Did I miss anything? #VCFounderRelationship #AlignedExpectations #RelationshipsMatterMoreThanEver

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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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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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Great Insights from Vinod Khosla's Talk on Investor Value: 🔸 90% of Investors add No value, and 70% actually add negative value. Real value comes from experience, not credentials. 1. Simply having an MBA or working at a venture firm doesn’t qualify someone to guide entrepreneurs. 2. Those who haven't built a large company or gone through the uncertainties and traumas of entrepreneurship are often unqualified to advise. 3. If you’ve never been in the trenches making tough trade-offs, you can’t effectively help an entrepreneur. 🔸 Pick investors who can help – not those just with experience in stability. 4. Investors who are comfortable with uncertainty and first-principles thinking. 5. Look for investors who prioritize learning and adaptability over rigid experience. 6. Do not choose an investor who only understands incremental change, not disruptive innovation. Khosla’s Final Advice to Entrepreneurs: 7. Don’t Overvalue Experience: Just because someone has worked in a big company or has a prestigious title doesn’t mean they’re equipped to handle the fast-moving, unpredictable nature of startups. 8. Look for Problem Solvers: Investors who can deconstruct challenges to their fundamental components and find creative solutions are more valuable than those offering "tried and tested" advice that doesn’t fit your unique situation. 9. Adaptability Over Stability: The best investors will challenge your thinking, help you discover new approaches, and aren’t afraid of making bold decisions—just like you need to be as an entrepreneur. Apply first principles when choosing. Choose wisely!! https://lnkd.in/gV6rG6NJ #FirstPrinciplesThinking #Entrepreneurship#StartupAdvice #InvestorInsights #LearningOverExperience #BoldDecisions

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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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I have nothing much more to add to this - this post on Service-as-Software and Systems of Agents is right on the thesis and most comprehensive #AI #agents stack I've seen so far. I'm thinking about how to translate/apply this for the #accounting profession. Sign up on our website if you're interested in following, I'll be writing more on our blog - https://lnkd.in/giGeTzT5 #AI #AIAgents #Innovation #FutureOfWork #AccountingInnovation #AIinAccounting #FinTech #MachineLearning #DataSecurity #Automation #DigitalTransformation #CPATech #FinanceAI #RegTech #AgentCollaboration #accountingTech #CPA #CPAtech [Day 6 of 30-in-30 LI Posts] Day 1: my transition to #consulting https://lnkd.in/gzWU4jdD Day 2: hidden costs of #innovation https://lnkd.in/guSFwiaV Day 3: Ellen speaking at AT #AI Summit https://lnkd.in/gub5NCfw Day 4: firm m&a integration pillars https://lnkd.in/g2-ya-te Day 5: #AI #agents demo day https://lnkd.in/g3i-pmWp

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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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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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I really like this PMF Framework from Sequoia. https://lnkd.in/gT2nWttx

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🧙‍♂️ My friend Gandalf dropping some real SaaS wisdom in his latest podcast episode: Took the new Runway ACT-1 for a little test drive yesterday. What do you think? 😃 Steps: 1. Open Runway Act-1 2. Record or upload a reference video of yourself 3. Create or upload a reference image (used Midjourney) 4. Click "Generate" (no prompt needed) 5. Final video edits with VEED.IO

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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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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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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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