Dan Moskowitz

ex Google CEO Eric Schmidt spoke at Stanford this week. Lots of nuggets here, but most interesting for the near term, Schmidt believes in the next year AI is set to make a big leap forward. He predicts AI will combine three important features: 1. Vast knowledge retention (1 million token models) 2. Text-to-action capability 3. Personal AI agent fleets While the full impact is unknown, everyone may soon command their own AI teams. Here's an example: Imagine you're developing new quantum computing algorithms. Your AI assistants could: - Continuously scan and synthesize global quantum research papers - Generate novel algorithm ideas based on this knowledge - Simulate and test these algorithms across various quantum architectures - Automatically document findings and prepare draft papers - Identify potential collaborators and draft outreach emails All initiated by a command like "Advance our quantum algorithm research." This could dramatically accelerate R&D cycles and scientific breakthroughs.. https://lnkd.in/eJbg6cnD #frontiertech #AI #futurefrontier

22

8 Comments

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).

10

5 Comments

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.

15

In retrospect, 2024 was a pretty amazing year for innovation out of University of California, Berkeley. My Top 10 highlights below. Any I missed? 1. Databricks secured the largest VC round ever—a $ 10B funding round, led by multiple Berkeley founders. 2. OpenAI raised the 2nd largest VC funding round ever - $6.5B. Co-founded by Berkeley alum John Schulman. 3. Since launching Built By Berkeley (a newsletter that tracks Berkeley founded companies funding) in July 2024: 229 companies raised 27.4B. ~$1.1B per week raised by Cal founders! Wild! 4. Out of the 13 2024 Nobel Prize laureates - 4 had Berkeley connections. 5. Record-breaking Berkeley Olympic performance: 5 Gold, 10 Silver, 8 Bronze medals. More than many countries!  6. Berkeley-founded Perplexity emerged as a genuine Google Search competitor, reaching a $9B valuation (9x increase in a year) with 15M+ active users (probably already much higher than this). Along with some unbelievable execution speed from Aravind Srinivas and team. 7. Chatbot Arena, founded at Berkeley, became the industry standard for AI model benchmarking. 8. Rich Lyons became the new Berkeley chancellor. Rich will be a huge innovation catalyst for Berkeley. 9. Berkeley beats Stanford University 21-24 in the Big Game…not innovation but fun nevertheless.  10. Berkeley leads the PitchBook rankings with 1,642 VC-backed undergrad alumni-founded companies. 245 more than second-placed Stanford. Berkeley is shaping up to be ground zero of the coming AI wave from a company formation and research perspective. Looking forward to a bigger and better 2025.

1,813

71 Comments

LP Tip #20 out of #50: To re-up or not to re-up A re-up refers to an LP making an additional investment in a fund where they previously invested. Before we dive in I want to share that there are reasons an LP may not to re-up which are not in your control. Here is a short list: ➡️ The LP may have evolved their strategy and needs to scale back the #️⃣ of funds they invested in.   ➡️ The LP was never set up to re-up in the first place. ➡️ The LP could be an individual that found themselves in a cash crunch and overly exposed to venture.  I'm assuming that when you got your investment that the LP explicitly told you they do re-ups. Please do not assume they do. My expectation is also that the LP dug in to properly underwrite the fund. 🏴 Re-ups are not automatic. An LP will have to re-underwrite each fund. The speed of the re-up also depends on how much work was done in the initial investment, who led the investment, level of confidence there existed when that investment was done, and current pipeline of funds. There are 4 flags that may cause an LP to walk away. 1️⃣ Partner Breakups/Changes and Team Turnover:  The biggest risk in Partnerships are whether the Partners will stay together. One LP gave me a framework on how to think through this issue that has stuck with me. Partnership is like a marriage. If one feels there is potential for a breakup, does one believe it will be an amicable breakup? Or, does one believe the spouses will never talk again? If it is an amicable split that is manageable. The Partners can find a way to support the fund. Then the question becomes whether the LP wants to back the Partner/s that remain.  If there is high team turnover. The LP will want to investigate because team stability is used as a proxy for firm sustainability and ability to scale. 2️⃣ Fund Does not Execute on what they Sold the LP:  The LP “bought” something specific and typically it is because they were missing this strategy or wanted exposure to a strategy for the portfolio they were constructing. An institutional LP will invest and write a detailed investment memo flagging all the things you said you were going to do and then go back and compare when you come back to ask for money. If for example you pitched a generalist seed stage fund and you show up asking for money sharing that you invested in deeptech at A that will cause alarm. The same can be said for the number of companies you promised to invest in or check sizes you wrote. If there are big differences LPs will want to know why. 3️⃣ Bad Actions  The fund manager/s does something that is unethical. This is a no go for an LP.  What if the situation is not clearcut? This is different for everyone and requires discussions b/w GP and LP, but most LPs don't want headline risk. 4️⃣ Continuous Underperformance  If it's early days an LP will look at some key indicators to see how you are progressing over time.

17

🚨🚨 🚨 Unlocking Innovation: The Power of the Adjacent Possible Innovation programs can stall when the team tries to ideate way beyond their collective perspective and frame of reference. Like a software engineer brainstorming genetic modification of mycelia, or a senior executive ideating process improvements which they haven't been participated in for decades. It's challenging, exhausting, and ultimately self-defeating. Enter the concept of the "Adjacent Possible" - a game-changer for innovation. What is it? The Adjacent Possible, coined by Stuart Kauffman, represents the realm of new ideas within reach based on our current knowledge, resources, and capabilities. Why it matters: ➡️ Realistic Innovation: it focuses on achievable advancements, not far-fetched concepts. ➡️ Incremental Progress: each innovation opens doors to new possibilities. How to leverage it: • Constantly explore the boundaries of your adjacent possible. What's in your line of sight is what you know best. • Combine existing ideas in novel ways. Even small perturbations from status quo can generate large innovation leaps. • Stay curious about developments in adjacent industries. Cross-domain patterns create fresh sources of ideas, while also expanding the adjacent possible. Most of all, it takes the 'genius' pressure off the team. No one knows everything, but each one of us knows a lot about something, and we can all drive plenty of innovation potential by focusing on the spaces we know best. Are you exploring your Adjacent Possible? Share your thoughts below! 👇 #Innovation #innovationsystems #AdjacentPossible

8

2 Comments

He who has seen exponential growth understands and allocates. The ratio of founders or tech employees who are also small angels in SF is compared to European ecosystems multiple times higher. Allocations of pensions funds, foundations or endownments to VC asset class also higher. Sure, there might be legal (SAFEs) and tax reasons but the main point from my perspective is this. Once you experienced exponential growth first-hand, you understand, you can imagine it. And you start to allocate and support. Best place for that was and still is SF. Its harder to imagine exponential compared to linear growth. And there are not many good examples of exponential growth in Europe in last years. We somehow missed on cloud, mobile and also AI now. Europe driven by industry/manufacturing and private equity, US more by tech.

36

4 Comments

Kettleborough VC has been active for ~6 years, invested in ~30 companies, gone through ~80 funding rounds, witnessed ~12 exits including ~4 instances of partial principal erosion, seen ~4 seed to $100M journeys, tracking ~29% XIRR across the portfolio. A couple of IPOs from the portfolio surely seem very much in line ahead.

257

17 Comments

Insightful breakdown from my great Partner Omri Amirav-Drory on the simple factors that really separate the top 1% of investors from the rest, and how more operators should become investors! https://lnkd.in/gEiZdT2h

16

3 Comments

PE - AI & Scarcity of Software Engineers Robert F. Smith of Vista Equity compared the scarcity of software engineers to resources rarer than gold, underscoring their high demand. However, today AI's growing ability to write code will transform Tech PE by accelerating software development and creation. Key impacts include: • More scalable software • Improved code quality • Shift in developer roles • Faster development cycles • Reduced development costs • Faster innovation and prototyping • Custom solutions for larger customers • Risk mitigation with AI-assisted debugging • Competitive advantage for early AI adopters • Lower barriers for scaling software development AI will amplify developer productivity, not replace them, and PE firms integrating AI into portfolio workflows can unlock the next wave of growth. ------------------ #pe #privateequity #investing #ai

7

LLMs are trained on internet-scale data. But to make AI work for work, models need to be grounded in your enterprise data -- a much harder problem than meets the eye! How do you connect to all your data sources and ingest them frequently enough while managing compute/storage costs? How do you make sure that you only show users results from documents they have access to? How do you rank results without internet-scale click and link data? How do you process the content of documents that are often hidden in PDFs and presentations? These are tricky problems, and it's why enterprise search didn't work for nearly a decade, until Glean came along. Turns out it's the exact same set of challenges needed to give LLMs the right context to work their magic on your enterprise content. Glean spent 5 years building the ingestion, search, and ranking infra needed to solve this problem well. This has given them the right to run the "Work AI" race and delight users at work. CEO Arvind Jain shares more on the latest episode of Training Data. Listen here, or wherever you get your podcasts: YouTube: https://seq.vc/ytd Apple: https://seq.vc/pit Spotify: https://seq.vc/jnz 

204

10 Comments

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

11

1 Comment

Ross Haleliuk posted the fantastic article below. This is the world where I live day-to-day. At TechOperators, we invest mostly in early-stage cyber, but we do things a little differently, and we believe there are ways to invest successfully outside of pure Power Law math. The article argues that many security problems are too small for VC. I agree. I often try to convince bootstrapped founders not to raise venture because doing so can turn a successful, slow-growing bootstrapped company into a failed venture-backed company because, despite a large infusion of capital, it couldn't double every year. VC is not monolithic--not by stage, strategy, or style. Venture is often equated with "Tier 1 Venture". Ross argues that VC is not always great for early-stage cyber--and he is right. Bootstrapping AND VC work when incentives are aligned. Does it work for an early-stage VC with a <$200M fund to invest in several early companies at reasonable valuations, setting up the conditions for reasonable exits that pay off for both investors and founders? Yes. Does it work for $800M funds investing in seed stage at $100M+ valuations? Well, that depends! Power law says it does (for VC), but the unfortunate externality is that these rounds destroy companies and founder equity more often than not. There is a role for patient capital in this ecosystem to fuel successful companies that retain exit optionality as they scale--driving exit value for both founders and investors.

35

2 Comments

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.

39

3 Comments

Friends, Q2 was a quarter of progress and learning for us at HawkTower, where we invest in early-stage startups applying breakthrough computing technologies to maximize California's natural resource markets. Our focus is on technology-enabled interventions (e.g., AI-enabled geospatial radars for lithium-ion batteries in the waste stream) for critical, market-level imperatives (e.g., disaster prevention) that can drive rapid commercial adoption (e.g., placed on every recycling truck in the world). Our current investment priorities are: - Agricultural productivity and efficiency - Biomanufacturing - Labor and mobility automation - Waste stream optimization and decarbonization - Disaster prevention and management Here’s a snapshot of what we’ve been up to: Milestones and Investments: - In late April, we officially launched our fund, filing with the SEC. Yay. - By June, we completed our first fund close ahead of schedule, with backing from family offices, foundations, high-net-worth individuals, and organizations aligned with California's future. - Our portfolio now includes three early-stage investments (Farm-ng, Agriful Software, and Nexstera Tech). Strategic Partnerships: - We believe that startups operating closer to their customers and resources, especially in agriculture and biomanufacturing, have a strategic advantage. For instance, the Salinas Valley, a major agricultural hub, is less than an hour from Silicon Valley and serves as a center of thought leadership in the convergence of agriculture and AI (from Western Growers, Taylor Farms, and others). - Similarly, the North San Joaquin Valley is emerging as a biotech and agtech innovation hub, and we are proud to support BEAM Circular, a startup hub for the circular bioeconomy. Key Relationships: - Karen Warner, CEO of BEAM Circular, joined HawkTower as a strategic advisor. - Jackie Cruz, Ed. D. Cruz, Hartnell College’s VP of Advancement, advises on Salinas Valley and workforce development. - Ben Palone from Western Growers, with his technical background in agriculture, became a trusted advisor. - Matthew Hoffman from Driscoll's Global R&D team provides insights on global agriculture, product positioning, and investment strategy. - We collaborated with Monterey Bay DART for workforce development and automation in agtech. - Dominic Milano and the Milano Technical Group from Merced collaborated on key initiatives and advised us on investments. - HawkTower is participating in Santa Cruz Works's accelerator cohort as a mentor and advisor. Thank you Doug Erickson. - We published an overview of entrepreneurship across the UC system with input from across the University of California system. Moving into the fall, we remain deeply committed to supporting founders shaping California's future natural resource markets. Stay tuned and be in touch! : ) Db

95

5 Comments

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.

8

2 Comments