Ron Nachum

🚨🚨 🚨 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

Summary: Silicon Valley VC firm Menlo Ventures and AI startup Anthropic have announced a new $100 million fund to invest in pre-seed, seed, and Series A AI companies. Menlo Ventures will use its investment and close relationship with Anthropic to identify potential startups for future investments. The fund is open for applications and Menlo Ventures will also back subsequent rounds raised by promising companies. Key takeaways: The $100 million "Anthology Fund" will focus on investing in AI-first startups, leveraging Menlo Ventures' investment and relationship with Anthropic. Menlo Ventures recently closed a $1.35 billion fund and will use its proprietary machine learning tool to evaluate potential investments for the Anthology Fund. The fund is open for applications and will provide startups with $25,000 worth of credits to use Anthropic's models. Counter arguments: There is no guarantee that the investments made by the Anthology Fund will be successful, as with any VC investment. Menlo Ventures may face competition from other VC firms also interested in investing in AI-first startups, potentially driving up the valuations of these companies. #venturecapital #startups #vc

3

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

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

There has been so much discussion about the prohibhitive cost of AI Image generation inference. Now there appears to be a breakthrough: Meet VAR - Visual AutoRegressive Modeling 🤯 Researchers from my alma mater (Peking University) Keyu Tian, Yi Jiang, Zehuan Yuan, Bingyue Peng and Liwei Wang have developed a VAR (Visual AutoRegressive modeling), introducing "next-scale prediction" that's revolutionizing how AI creates images. 🔍 Key Innovations: ✅ 20x faster inference speed ✅ Significantly improved image quality ✅ Better data efficiency and scalability ✅ Zero-shot capabilities for image editing tasks 🎯 The Results Are Stunning: ✅ FID score improved from 18.65 to 1.73 ✅ Inception Score jumped from 80.4 to 350.2 ✅ Outperforms current diffusion models What's the most fascinating is how VAR appears to be bridging the gap between language and vision models, showing similar scaling properties to Large Language Models! This could be the breakthrough we've been looking for in visual AI! It is still early, but check out the paper for the technical details: "Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction" #ArtificialIntelligence #ComputerVision #MachineLearning #Innovation #AI #TechNews Thoughts on how this could transform creative industries?👇 https://lnkd.in/eHZZzXK7

17

2 Comments

NVIDIA's Jensen said "ChatGPT democratized computing, Llama2 democratized generative AI" ...and now Llama3 is the next level 🔥 It's fascinating to watch Meta's strategic moves. With Llama3, they prioritized training on a massive 15T token dataset to pack all into a lean 70B param model, instead of building a massive model. This allows Llama 3 to match trillion+ parameter models like GPT-4, but at 1/10th the compute, storage and inference costs! 💰 This technique was published by Google DeepMind a few years ago https://lnkd.in/gx7VU8aA Meta is not a dark horse in AI anymore. They might be the top dog. https://lnkd.in/gRRJYuxt #llama2 #llama3 #llm #chatgpt #gpt4 #ai #ml

16

5 Comments

Success in deep tech, like any other startup, hinges on not running out of money It’s more pronounced in deep tech since business metrics won’t happen until after several funding rounds So founders need to convince investors and employees that whatever they are raising now will generate results (with ample buffer) that will entice investors to put in more money, even though there likely still won’t be a business that traditional investors can underwrite. Pure technical progress won’t cut it. It’s about turning armies of talent, customers, and partners into believers of the future you’re making possible Props to Becky Pferdehirt, PhD and Rajesh Swaminathan for a fantastic convo and Matt Caspari for hosting and moderating!

181

13 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...

3

1 Comment

In March, I left Strac (YC W22) to build something new (more on this soon). Here are my key takeaways from taking Strac from zero to one: While at Strac, my favorite part of the role was always taking an engineering-first approach to our go-to-market motion. I loved tinkering, and frankly there's never been a more exciting time to do so as AI rapidly changes how effective sales teams function. Some of my favorite projects at Strac included writing a script to set up emails optimized for outbound deliverability, building a web scraper to check if a company was SOC2 compliant, and integrating our sales tech stack for seamless retargeting and outreach. The most impactful lessons: 1. Sales is a Numbers Game: Five years ago, sales teams relied heavily on traditional methods like cold calling and emailing purchased conference lists. However, nowadays, mass calling and emailing without the prospect showing any intent is ineffective. Today, with the explosion of data available online, intent signals—such as web activity, social media interactions, and content engagement—are critical for identifying leads. The challenge now is not just gathering this data but integrating and analyzing it to make your outbound effective at scale with the right messaging. 2. Get Hacky with Finding Leads & Continuously Experiment: You should run dozens of experiments on different lead lists and iterate until you find message-market fit. During the first month at Strac, we ran over 50 distribution experiments to find where our product had the most obvious signs of product-market fit and iterated on our outreach approach until we found message-market fit. When building out your ETL for sales, it’s important to build a framework that’s conducive to this. If you build a slow-moving, rigid outreach framework, you will limit how many experiments you can run, and your business will die. With startups, every single day you are just slowly dying—iterate fast. 3. Show Immediate Value to Your Customers: Strac was one of the most customer-obsessed companies I’ve ever seen. The most important thing is to fundamentally understand what problem your customer is trying to solve, as there is likely someone else also trying to solve that problem if it's a real problem. Translating this to structuring outreach: if you just make an ask, it's unlikely you'll get a response—you’re not solving any problem your customer faces and instead creating a problem for them. However, if you show someone how easily they can gain something from talking with you, you'll have a prospect excited to have a conversation. As I transition back to the founder role, I'm excited to build tools that address these challenges and help companies accelerate their go-to-market. I will continue to work closely with Strac as they go from one to ten, and I'm excited to have them use our solutions to get there faster 🚀

132

72 Comments

Silicon Valley Slackers: Myth or Reality? We sat down with Menlo Ventures’ Deedy Das to discuss his viral tweet about underworked engineers earning $300K+ and the phenomenon’s broader implications. Mouse Jigglers & Ghost Engineers: Deedy shares wild stories of remote workers gaming the system, from "working" in Mexico for years to productivity software catching patterns of inactivity. Misaligned Incentives: Deedy argues that middle managers prioritize headcount over productivity, creating an environment where underperformance is overlooked. Measuring Productivity: Lines of code and git commits aren’t enough. Engineering productivity remains complex and difficult to quantify, so there’s no silver bullet. Viral Backlash: Deedy’s anecdotes sparked heated debates in tech, with some engineers admitting to coasting while others criticized him for "narking" on the culture. Should companies rethink how they measure and incentivize performance?

37

5 Comments

Ilya Sutskever confirmed at NeurIPS '24 pre-training scaling law will not be the same. Internet data, the AI fossil fuel, is running out. It is not the end of the AI hype, the rapid rise of foundational AI has outpaced our ability to fully grasp their long-term societal impact. AI agents and applications are poised to transform every facet of our lives, and we will see it happening in coming few years. There will still be improvements in model architecture, and synthetic data creation, so models will improve, just not at the lightning speed they did till now. Looking forward, the single most important milestone in AI is creating systems capable of advancing themselves—AI researchers that can innovate and improve foundational models autonomously. We need to assess how far are we from this stage.

9

A new era of deep tech has emerged. First time funds will raise “unheard of” amounts of capital to fuel next gen deep tech startups - producing outsized, superior returns for LP’s compared to the rest. Prudent investors will act on this data and shift investment strategy as LP’s or risk being left behind savvy wealth managers and CIO’s / FO’s who saw this trend begin 4 years ago.

100

8 Comments

I’ve been fortunate enough to do AI shows with Sam Altman, Yann LeCun & Arthur Mensch. But this show today with David Cahn at Sequoia Capital is the best of all of them: 🧠 AI’s $600BN question 🏃 Why no one will never train a frontier model on the same data center twice 🏗️ Servers, steel & power: The 3 pillars of the future of AI Not one to be missed, my 8 key takeaways 👇 1. The Oligopoly That is Blowing Billions on AI - Microsoft, Azure, & Google now represent $7T market cap. - Of course they will spend billions to protect their oligopoly - What is most interesting is Meta is the only incumbent that is financing this not with cloud being their cash cow. 2. How Startups Could Win with the Cost of Compute Coming Down - Big tech companies are producers of compute & startups are consumers of compute. - Overproduction of compute = lower prices.  - This gives startups higher margins & makes them more valuable. 3. Is Compute the Currency of the Future or Do the Costs Come Down and It Is Commoditised? - We are forgetting “compute” is a euphemism for a $2BN data center in the middle of nowhere. - They have GPUs & liquid cooling systems that need to be constantly optimized & upgraded. - We cannot simply build 15 years worth of compute all at once.  No one's ever going to train a frontier model on the same data center twice. 4. Data Centre Teams and Model Teams Need to Be Coupled - You cannot have separate teams running data centers & building models. - Elon & Zuck control all their data centres. - It will not work as these models get better & better. 5. Why the Data Center Is the Most Important Asset - There is not much difference between these models today. - AI researchers are jumping from one lab to another. - Scaling laws will become more dominant as these models get bigger. 6. Servers Steel and Power: The Three Bottlenecks of AI Three things: 1. Servers: NVIDIA, AMD, Broadcom… 2. Steel: Construction & real estate developers for data centers. 3. Power: All the energy required for AI. - More factories will be built in 12 months for AI and we will see another industrial revolution. 7. You Have to Have a Cash Machine to Play the Big Model Game - Amazon has AWS - Meta has Instagram - Microsoft has Azure - You need a cash machine to compete and it CANNOT be the AI business. 8. There is One Definition of Success in Venture: Are you a Slugger? - If you are generating billion dollar gains, you are a slugger. - Doug Leone, Pat Grady, Andrew Reed are all sluggers. - You cannot be good at this business without being a slugger. (links in comments) #founder #funding #business #investing #vc #venturecapital #entrepreneur #startup #seed #funding #sequoia

190

26 Comments

If you’re a business executive, understanding the LLM ecosystem will be critical for leveraging AI to drive business value in your organization. Awesome insight & resources here from #a16z investing partner Guido Appenzeller. 🚀

16

6 Comments

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.

144

5 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

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.”

70

15 Comments

Cost-aware inference is important, but currently LLM prompts are not interoperable. Prompts that work good on one LLM may show worse performance on the other, even more advanced LLM or even on the different versions of the same LLM. Once interoperability problem is solved it will also be possible to implement cost-aware inference that strategically manages computational resources for prompt processing, ensuring sustainable costs. This approach includes model routing https://www.notdiamond.ai/ - automatically selecting the most appropriate model for each task. Startups in this area are actively supported by the industry veterans.

4

2 Comments