Neeraj Gupta

On the back of the article from The Guardian “Banks, airlines and media outlets hit by global outage linked to Windows PCs,” it got me thinking… What would happen if AI services like ChatGPT, Gemini, etc., went down? Why can’t we use these offline instead of being connected to the Internet? Many organisations are becoming dependent on AI, raising concerns around security and privacy. We would witness significant disruptions across various sectors. Customer service, healthcare, finance, and logistics heavily rely on AI to optimise operations, provide insights, and enhance user experiences. The absence of AI would slow decision-making processes, reduce efficiency, and potentially lead to increased errors. This scenario underscores the critical importance of AI in our modern infrastructure and the need for robust contingency plans to mitigate such risks. https://lnkd.in/e8KFV6QK #AI #Technology #BusinessContinuity #RiskManagement Whitespace

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.

Vishal Sachdev highlights the strategic integration of open source and proprietary tech in architecting tech stacks, developer ecosystems and resulting business models. The world class companies effectively balance value commoditization in open source and value capture in proprietary tech. This is the strategic challenge for companies operating in #blockchain and #AI. I believe those who operate at the intersection of blockchain *and* AI stand to win this strategic battle...

Coming soon on Aug 1: "Transformation to a Fresh Tomorrow." This is theme of our 3rd Annual Web3 Investor Day, as we welcome the innovators and investors convening in Chicago for these discussions around the next generation Internet. Our theme reflects the optimism, determination and commitment investors and innovators have solidified off the depths of the bear markets to drive success in the bull markets. Broader investor optimism suggests that fresh institutional funds into Web3 across both tokens and equity may endure.  Across adoption metrics, customer interest and developer activity, Web3 seems to have normalized. It’s refreshing to see the transformation and next evolution of Web3 and it’s refreshing to meet many of you on August 1.  And yet, even with the improved optimism from last year, there still remain open questions in the development and adoption of our next generation Internet:  1. the balance of speculative versus productive investor capital in this current cycle 2. the productive role of liquid trading capital in fostering Web3 innovation 3. the attractiveness of venture investments in follow on financings 4. the role of policy and regulation that may enhance innovation 5. the continued developer activity to improve the underlying infrastructure 6. other Web3 recovery characteristics in target customer use cases. We remain committed to navigating regulatory shifts, fostering investor optimism, and building the customer use cases for Web3.  Physical spaces enhance our innovation conversations.  In person relationships foster our collaboration.  Relationships drive breakthrough innovations.  Reach out to Rizza Torres at Decasonic if you would like to join us! #web3investorday #web3 #venturecapital

We've mapped the 250 most relevant early-stage startups within the European data ecosystem! 🚀 Our coverage spans categories like Data Collection, Storage, Integration, Security & Privacy, and Platform. Discover how these startups are leveraging AI 🧠, the most active sectors 👩‍💻👨‍💻, and key funding trends 🏦. Don't miss the simple table listing all the companies! 😀 #AI #Europeanstartups #MMCresearch #vcfunding Advika Jalan

This is quite a piece on accusations of unethical pay-to-play within the #cyber #VC ecosystem. -Guaranteed first-year sales for portfolio companies -Formal loyalty program for CISOs -Points awarded within the program for CISO commitment level to portfolio companies (meetings, PoCs, product purchases) -Share of GP fees for CISOs in the loyalty program https://lnkd.in/gDta7Gp7

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

Here’s a question investors are often asked: When evaluating early stage companies, how much time do you spend on due diligence around future exits? It’s not surprising we hear this question a lot. Also not surprising: it’s got a wide range of answers depending on the firm. Some don’t spend much time here at all. Others make it a point to put meaningful time in as part of their process. Our current thinking: take the time to do the work on public market comps. At Alpaca VC, we spend significant time understanding how public market investors will  realistically value a business based on margin profile, product, business model & TAM. In short, we want to know: how will this company be valued at scale when we get taken out? Yes, we can acknowledge that the journey toward exit is a windy road and that there may be pivots along the way, but there are still public market companies that have a business model similar to the early stage company you're evaluating. And you can always look at gross profit multiples if you think the margin profile will change over time. So we still do the work on the comps. Quantitative metrics we look at when making the comparison to public market comps include EBITDA multiple, revenue multiple, Gross Profit multiple or all of the above. As part of this process, it’s also important to factor in the public market company’s year-over-year revenue growth as this will also significantly impact the multiple it trades at. Simple example: if you have two public market companies with similar business models and similar margin profiles, but one's growing 100% year over year, and one's growing 50% year over year, then obviously the DCF (discounted cash flow) analysis is going to spit out a very different valuation for the one that's growing faster. Why this matters: When you take all of that information into account as you evaluate an early stage business, you can begin to create a realistic picture of how this company will be valued in the public markets at exit - or how an acquirer will value the company for an acquisition. Strategic acquirers may, of course, pay a premium, but we won’t underwrite for that. This allows us, for example, to form conviction around valuation based on revenue and gross profit predictions. If we think they can do $100M of revenue five years from now, we use this diligence process to form a thesis about whether the characteristics above (product, margin, business model, etc.) will cause the company to be valued at $200M vs. $500M vs. $1B at exit. Curious how other early stage investors think about underwriting an exit and how much time they’re spending on public market comps even though these companies are in their infancy.

Apple’s Private Cloud Compute and the business of privacy With Private Cloud Compute, Apple has gone to extraordinary lengths to safeguard user privacy with AI-related tasks. In doing so, Apple has surely also incurred an extraordinary expense. How can Apple justify the cost of providing PCC if it doesn’t directly monetize its Apple Intelligence features? Either through the improved brand perception captured in a broader privacy narrative or by monetizing PCC with other non-AI use cases. While the narrative explanation is clear, the more interesting theory about Apple’s motivation in building PCC is that it can be expanded to other use cases that can be directly monetized. The first alternative use case that springs to mind is advertising attribution. PCC sounds conceptually similar to a data clean room, albeit far more sophisticated than most implementations, which merely use IP addresses and other identifiers to match publisher-observed ad interactions with advertiser-observed conversions. While the PCC nodes seem hardware-optimized for processing AI-related tasks, other nodes could be built that aren’t.

ARR abuse, while obfuscating true GAAP revenue is a real issue. In VC pitch decks, I am noticing an increasing confusion in how the founders share the Annualized Recurring Revenue (ARR), Booked Revenue, and the GAAP Revenue. This recent WSJ article on this topic, as applied to #Crowdstrike, makes this point very well. Thanks Jonathan Weil Krishna Kunapuli, Dr. Pallab Chatterjee Rajiv Roy Sumanth Channabasappa #crowdstrike #accounting #saas #ARR #GAAP #Revenues

The AI Demo isn’t easy. Many of the major AI companies have demoed their AI systems, first starting with pre-recorded, & now pushing into live demos. They don’t always work. Multiply Murphy’s Law by a non-deterministic system & it’s not unreasonable to expect AI demos to nearly always hiccup. Demo disruptions aren’t disaster. These systems are early & changing rapidly. They might suggest the system requires work & tuning, not a fundamental challenge. But, they can be problematic in proofs-of concept. Proofs of concept are extended demonstrations of the software. Well-structured PoCs align on success criteria at the outset. These criteria enable vendors & customers to agree on what success looks like. Worflow proofs-of-concept are relatively straightforward. They are deterministic. Can I process a loan application in 5 minutes? Yes or no. But as AI applications shift to selling outcomes implicitly or explicitly, the PoC becomes a testing ground of those outcomes. Non-determinism means sometimes the PoC won’t produce the required wow moment. This also means the PoC criteria must be more flexible. How does a buyer evaluate a probabilistic system? Do we compare it to human performance? Speaking to some practitioners, they’ve shared with us human labelers typically agree on 60-70% of the time. Does a AI robot need to be as accurate as a human assuming it will be much less expensive? Or will we expect more as we do in self-driving cars? If AI systems require human assistance, then the ROI of the system must include some human operating expense - whether explicit or implicit. Some teams will want to benchmark systems in parallel to determine the relative performance. With most startups building atop existing models & setting aside differences in fine-tuning, the ultimate performance should be relatively comparable, provided they use the same data sets. Will startups compete on access to different data sets? Today, there are more questions than answers about how to sell AI agent systems. We’re hosting an event on the evening of Sep 10th in San Francisco to interview leaders in the space moderated by Dave Morse, former CRO at Hebbia & VPS/VPCS at ScaleAI to talk about some of these questions. If you’re interested to attend, see the details here : https://lnkd.in/dA8tJEcz

Solid advice to founders from the co-founder and CEO of Databricks, Ali Ghodsi 👇 Target other startups first. Ali draws on Geoffrey Moore’s book Crossing the Chasm, which identifies five key groups in the tech adoption cycle: 1️⃣ Innovators 2️⃣ Early adopters 3️⃣ Early majority 4️⃣ Late majority 5️⃣ Laggards Ali’s advice is especially relevant for AI-focused founders, given the myriad of risks (security, privacy, explainability, and so on) that come with putting generative AI models into production. It is difficult to target established, traditional companies—the laggards—as an initial market. These large enterprises come with a labyrinth of security requirements, bureaucratic red tape, and drawn-out sales cycles. Beginning with other startups increases the likelihood that a product will be adopted and provides a powerful way to collect early user feedback. Establishing this data flywheel can help an AI startup differentiate their model and iterate their way to product-market fit. Salesforce and Dropbox used this particular playbook. Once you've proven your model with startups, then you can set your sights on enterprise 🚀

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.

This article could not come at a better time for me and my fund (Danta Fund ). After attending a recent Summit where I felt the industry of #startups and #vc has become too generalist and differentation is hard to find, I now know to include Danta Fund as (per the article below) a " Non-Consensus Alpha". 💪 It sounds pretentious, yes, but the description fits sooo well. Danta Fund is a specialized #agtech fund that provides #smartmoney to our founders. We are not only writing checks: we CONNECT, we MENTOR, we BUILD. 💥 As a recent colleague from another specialized Non-Consensus Alpha emerging fund told me (Andres Baehr from SAVIA Ventures ): we, specialized emerging funds need to find a way to thrive among the sea of VCs. It's like going back to when we were thinking about what to study in uni: -what am I great at? -what do I feel passionate about? -how can I make a difference in this world. #vc #truepassion #ikigai

This is what I mean when I say AI x DEEPTECH A revolution is coming. Upending entire swaths of our economy that have been previously ignored. Nearly $10 Trillion of our GDP rebuilt with AI and software.

This is such an instructive interview. Servicetitan and lessons for vertical SaaS cos. https://lnkd.in/ghBXGrk7

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.

Three Proven Strategies to Monetize AI Features in SaaS (Q4 2024) https://buff.ly/3OJaEdp by @PawelHuryn AI Monetization strategies: Strategy 1: Integrate AI Into Existing Tiers Strategy 2: Usage-Based Model Strategy 3: Add-On Model Check out benefits, drawbacks and examples!

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