Paul C. Jeffries

This is just incredible. Neal Ghosh's demonstration of how #VentureIQ takes deeply technical and complicated content across a range of industrial use cases and ages/stages through an AI-generated conversational podcast (a la #notebookLM) is really mind-blowing. Check out his post, below, and if you haven't seen VentureIQ, yet, let's get you set up to learn more. You can visit VentureIQ.ai or DM any of us over here to book a demo!

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🧩 One thing we've noticed with #VentureIQ technology is how well it can distill and communicate complex technical material 🧩 Whether it's a #deeptech venture, a novel #AI application, or even a patent submission, we can quickly distill the business and strategic implications for investors...and sometimes even the founders/PIs themselves! 🤯 Based on our work so far, the tech can handle a wide array of research areas and technological sectors -- anything from blockchain algorithms to vaccine development. It handles the nuance of the field, the specific terminology, and extracts the key concepts which matter for venture-building. 🤓 To demonstrate, I took a patent I helped write with Eric Laber on reinforcement learning. I ran it through #Ventureiq to build a commercial assessment (key differentiators, target markets). Then for fun I had #NotebookLM make a podcast out of it. I've uploaded the result it here (DM me if you'd like the actual report): https://lnkd.in/eiTejsny This is more than just automation or streamlining operations - it's about unlocking clarity and insight when it was previously unattainable, and the ability to do it over and over again no matter the sector or vertical. Interested in more info? DM me so we can chat.

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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

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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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Exciting to see startups aiming to use AI to discover chemicals, materials, drugs, etc. Semi companies have taken notice: AMD is investing in and Cadence is acquiring them. Semi/EDA companies have broken out by becoming critical path, whether it’s through critical software, packaging, IP, design kits from foundries, or user communities. Aside from just making it easy to make new materials, these “AI for making X” companies also need to articulate a vision as to how they can establish monopolistic positions in their industries.

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If you work in tech transfer this is a good read-in on how our diligence tech can assess and evaluate assets with minimal inputs. The best way to learn more is to see it live! Reach out to me directly to learn more!

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What do you do when a radical new technology puts your main product right in the crosshairs of disruption? Listen to David Okuniev — co-founder of Typeform | Ask awesomely — discuss the challenges of innovation within existing structures. David shared a game-changing insight: Radical innovation is really, really difficult to do inside your own product. He emphasized the need to break free from the constraints of familiarity and embrace change from outside the box. Henrik Werdelin and I have both seen our fair share of this in our respective careers. What struck us most was how David leveraged structure to overcome the innovator’s dilemma. By creating a culture of experimentation and providing space for bold ideas, he propelled Typeform beyond incremental improvements. What other hacks have you seen or employed to help your organization overcome the innovator’s dilemma? Share your stories below! 👇 And if you want to dive deeper into our conversation, click the link in the comments to catch the full podcast episode!

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From Crypto Ice Age to AI Inferno- Gartner Curves or Extreme Weather Conditions Precipitated by VC? A few thoughts I've been reflecting on recently on learnings from being through many crypto cycles I can apply to this moment in AI investing... https://lnkd.in/d6KSM3EN

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Excited for DCVC portfolio co Alta Resource Technologies to emerge from stealth! The design space of biology is many orders of magnitude larger than inorganic chemistry, and Alta uses it to massively reduce the cost of extracting rare earth metals. https://lnkd.in/g_q5UwRF

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It seems inevitable that as the underlying foundation models become more powerful, the LLM players will seek to justify the enormous investment that has gone into training their models by moving "up the stack", and evolve from an API or chat interface, to async agents. Regardless of whether the foundation model companies end up directly competing with you in your vertical, I believe as a B2B AI startup, you NEED to go through the thought experiment: how do I future proof my company as the underlying models progress? More here: https://lnkd.in/gkdhmVer

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Gary's post below (link in comments) speaks to the AI hype bubble. But it was the same with the Metaverse hype bubble too. It's always the same crap. The main reason I push back against the shameless hype-mongers (even if they resent all critiques) is that some really interesting companies with unique value to offer told the truth (as they should) and lost out on funding. Meanwhile, really stupid ideas that "promised the world in a day" got most of the money. Most of them failed within months or a few years at most when they couldn't deliver on those promises. We have a short memory. Some of those same people are back to the well for more of the same. Let's work past the "self-interested and mostly wrong" folks and stop inflating these bubbles. Support the people who are truly thinking out of the box and expanding the field for everyone's benefit. And yes, I'll say it: AI is a bubble too. Like most bubbles, the tech won't die out, but most of the startups racing in that lane will not exist in two years or less. And FWIW, the spatial understanding that Gary Marcus writes about has been the subject of deep research in the XR domain for the last few decades. AI without proper context is not all that helpful, it turns out.

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If you are in the VC space, you should know one of the early drivers of "Moneyball for VC". That's Gopinath Sundaramurthy, Ph.D Gopi partnered with the Kauffman Fellows Fund on implementing these concepts in their investment process. The results were staggering. 50% of the investments the fund made became unicorns. In Gopi's words 👇🏻 "We knew something was working, so we thought: Let's build this a little bigger." What did that look like in practice? 1️⃣ Gopi's firm uses a single ‘source of truth’ platform to centralize information, improving decision-making and consistency. 2️⃣ They maintain an even 50/50 split between data engineers and investors to ensure they leverage data to gain insights into teams, markets, and products. Want to learn more about what Gopi and the team at Ensemble VC has implemented at their firm? Check out the case study that our sponsor Harmonic published about the firm -->

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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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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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Insightful look into bioplatform strategy, and what it takes to raise a Series A from my partner, Omri Amirav-Drory: https://lnkd.in/dFv7GbF6

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Intersection of AI and humanoid robotics presents one of the biggest opportunities for societal scale impact Perception, Sensors, and LLMs are merging. Looking out for break out ventures working at this intersection. ! #EmbodiedAI https://lnkd.in/emK-nVNx

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New post: The big stack game of LLM poker https://lnkd.in/d5mfqgwk I’m sure you read David Cahn’s excellent and provocative piece "AI's $600B Question", in which he argues that, given NVDIA’s projected Q4 2024 revenue run rate of $150B, the amount of AI revenue required to payback the enormous investment being made into NVDIA each year plus the electricity to power GPUs to train and run large language models is now $600B, and we are at least $500B in the hole on that payback. The numbers are certainly staggering… and are just going to get bigger. Unless the scaling hypothesis falters, this is a contest now of “not blinking first”. If you’re a big stack player like META, MSFT, GOOG, or any of the foundation model pure plays, you have no choice but to go all in — the prize and power of “winning” is too great. If you blink, you are left empty handed, watching someone else count your chips. It’s likely hundreds of billions will be destroyed, and trillions earned. For all of us in the startup ecosystem, among many things, it’s going to create a rolling thunder of AI opportunities. Read more here: https://lnkd.in/d5mfqgwk

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I recently saw this metric from Carta’s 1Q24 VC Fund Report, which is very concerning. DPI... is nowhere to be found in earlier vintages that probably should start showing DPI. Funding early-stage projects is great, but ultimately, these venture dollars need to exit their investments and pay back their limited partners. That’s where the metric Distributed to Paid-In Capital (DPI) comes in. While managing a fund, we get interim measures during the life of the fund (e.g. IRR, MOIC), but ultimately, “you can’t eat IRR.” If you want to build a lasting venture capital organization, you need to start showing DPI for your fund. Keep in mind that this is traditional VC data from Carta, and is not strictly crypto venture. Crypto venture tends to get liquidity earlier (tokens) and things tend to go parabolic sooner (faster, more unicorns) - but I’d wager that the data here is somewhat similar for Crypto VCs… Not as much DPI as there should be from these earlier vintages. Read the full article, as well as a recap of all the crypto fundraising rounds for August, here: https://lnkd.in/g3eVJ-iF

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