Snehaal Dhruv 🤘🏻

If All We Build Are LLMs, AI Progress Will Stall, Hit a Wall, and Fall Away (Credit: Martin Ciupa) Updates on LLMs from Gary Marcus… 1/ https://lnkd.in/gBv_t8wZ... 2/ https://lnkd.in/gS-_JN9V My take… Yann LeCun’s recent comments reflect what many top researchers are quietly acknowledging: large language models (LLMs) like ChatGPT or DeepSeek are powerful tools — but they capture only a thin slice of what true intelligence is. LLMs are masters of language, not meaning. They weave words with astonishing fluency, yet remain disconnected from the real world — from perception, intention, and consequence. They don’t understand what they’re saying, only how language statistically tends to unfold. In that sense, they’re mirrors of human expression rather than minds that grasp reality. As LeCun, Yoshua Bengio, Gary Marcus, and others have argued, building real, reliable artificial general intelligence (AGI) will require something very different — architectures designed not just to imitate thought, but to generate understanding. That means integrating: 1. World models that can predict and reason about real situations, not just text (LeCun’s Joint Embedding Predictive Architecture, 2022). 2. Autonomous learning that discovers causal structure instead of memorizing patterns. 3. Energy-based or modular systems that reason, plan, and act coherently within physical and ethical boundaries. 4. Embodied sentience and salience — systems grounded in sensory experience, capable of focusing on what truly matters and aligning ethically with human values. 5. Cognitive world models and evolutionary learning modules — hybrid systems that combine: • Common-sense reasoning about space, time, and agency, • Evolutionary and meta-learning algorithms that improve over generations of experience, and • Analog–digital integration layers that bridge symbolic reasoning with continuous perception. Together, these advances would give AI something LLMs entirely lack: a living, adaptive model of the world — capable of understanding, learning, and moral reasoning. Without these deeper ingredients, LLMs will remain syntactic but not semantic — brilliant compressors of language, not genuine thinkers. And when it comes to Artificial Superintelligence (ASI), the gap grows even wider. Safety and reliability demand systems that can explain themselves, stay grounded in context, and correct their own mistakes. LLMs hallucinate, lack self-awareness, and can’t verify truth — traits that make them fundamentally unsuitable as the core of safe, autonomous intelligence. In short, LLMs are the spreadsheets of cognition: quite useful, but not the essence of mind. The next breakthrough in AI won’t come from training on more tokens — it will come from discovering the deeper principles of how analog and digital processes integrate, how reasoning connects to the world, and how coherence emerges from interaction.

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This week, Anthropic launched Claude for Life Sciences, its first real push to make AI part of how science gets done. Why this matters 👇 - Scientists spend hours each week buried in docs and data. Claude aims to give them that time back by summarizing literature, drafting protocols, and analyzing data. And with prebuilt connectors to Benchling, BioRender, and PubMed, it fits well into existing workflows. - Some researchers have been eager to use AI but hesitant to apply it without clear benchmarks. Claude’s most capable model, Sonnet 4.5, shows strong performance in lab protocol and bioinformatics tasks, making AI adoption in the field more credible and comfortable. - Anthropic also introduced Agent Skills, instructions that dictate how Claude tackles specific tasks. The first skill they’ve built performs single-cell RNA sequencing quality control using scverse best practices, a sign they’re prioritizing technical rigor. It's a strong step forward, but the rollout points to where pharma still is on the AI adoption curve. Instead of the self serve model offered to consumers, Anthropic is partnering with consulting firms like Deloitte, Accenture and KPMG to bring Claude to enterprise pharma. I can't help but wonder, if the goal is to meet scientists where they are, why would it take a consulting team to get started? 🤔 WDYT? Image Credit: Anthropic Agent Skills, announcement link in comments

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What will it take to make foundation models 100x better - without spending billions on compute or turning the planet into a giant data center? This is one of the questions I explore with Rohan Taori, a researcher at Anthropic and co-creator of Alpaca - the project that demonstrated how fine-tuning @Meta’s LLaMA could achieve ChatGPT-level performance for under $600. Rohan shares his journey from early work in computer vision at UC Berkeley to his Ph.D. at Stanford, where he focused on making AI more accessible. He breaks down the key innovations behind Alpaca, including self-instruct, a method that uses a stronger model (OpenAI’s text-davinci-003) to generate synthetic data for fine-tuning a weaker one (LLaMA). We also discuss: The potential and limitations of synthetic data - how far can it take us, and where does it fall short? What’s needed to make foundation models 100x better - beyond just scaling compute The future of multimodal AI and the research directions he's most excited about Our full conversation here: https://lnkd.in/ghYaWYnn

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I was talking to my friend Parul Singh about how too much VC money can be detrimental. I have been working with pre-seed/seed/A at and with companies for the last 20+ years and I have observed first hand that the saying “constraints breeds creativity” is true. In my experience, a company with 18 months of runway gets much more done in the second nine months than the first. 🪹 Pre-seed Companies: When you are in the “idea maze” (coined by Andreessen Horowitz) or “drunken walk” having too much money does not necessarily speed up progress. Of course you may need some money for founder living expenses but having a larger team wandering through the "idea maze" together doesn't necessarily make the journey go faster. There is a certain level of founder reflection that has to happen while navigating the “idea maze” and too many distractions (people, money) makes it hard. I teach an entrepreneurship class  (LeanLaunchpad) every year. Out of the eight teams we have navigating the “idea maze”, about half will test and land on a reasonable idea in just ten weeks (including a simple MVP and even some paid POCs). Total budget: zero. With the multitude of AI tools, time to PMF is accelerating. Check out https://lnkd.in/guM7Ymn9 (AI companies revenue >10x SaaS companies). 🌱 Seed Companies: When you are going from the “idea maze” to “product market fit” and are trying to first build a product that a few customers love and second build a way to acquire these customers efficiently, having too much money can actually be counterproductive. Why? Because too much money often translates to wasting time on things that don’t matter: you are not forced to focus on the features that are absolutely critical, you can spend in non-efficient marketing methods (skipping the crafting of the product-growth loop) and you can afford to over hire or to keep the wrong people onboard. 🅰️ Series A Companies: Post product market fit, when it is time to scale, money is absolutely critical. However there is one common failure mode: the “fake Series As”, the As that get overpriced without really having reached product-market fit. Take a seed in a hot space, a small team that has grown revenue to a few $100Ks or maybe even a million in just six months. There is little data on actual revenue stickiness or whether the founder-led sales motion can be replicated efficiently. There is a lot of competition to invest in the company and they end up raising $20M+ series A.  The founder feels confident, they are stretched-thin and they hire a bunch of folks. There is pressure to scale but the machine is not built yet (the product is not quite there, they can’t figure out how to get to customers efficiently) and things unravel. Venture money is like carbs: quality over quantity (not all money is equal); take in moderation (constraints bread creativity) and if you compete in endurance sports, you might need more carbs to perform well (if you are growing, you will need the cash).

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As a #VC who has been closely looking at the India’s #AI landscape, I see this tension play out more often than I’d like: Our #founders are brilliant #engineers. They ship clean #code, obsess over efficiency, and build thoughtful #products. But in a global AI race where the #market rewards speed and scale, thoughtfulness alone isn’t enough. Hence I loved this article where at a recent roundtable, Accel Partners’ Shekhar Kirani and Prayank Swaroop sounded a wake-up call for Indian AI #entrepreneurs. And as Shekhar Kirani rightly put it: “In the Valley, it’s a warzone. Engineers are building, iterating, raising money, and chasing scale aggressively.” Meanwhile, many Indian #AI #startups are still optimizing for capital efficiency and bug fixes—playing peacetime when the rest of the world is in wartime mode. AI-native companies in the US hit $50–100M revenue run rates in under 18 months. Valuations follow. And here’s the kicker—the gap is not in capability, it’s in mindset. If we want to win this race: - We need more breakout ambition - More Bay Area exposure - More founders thinking globally from Day 1 To fellow founders: This is not the time to wait for product perfection. This is the time to compound, not conserve. Let’s go from being technically sound to being strategically unstoppable. #VentureCapital #AIStartups #IndiaAI #Accel #GlobalMindset #TechLeadership #BuildFast #ProductMarketFit #FounderMindset

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India is not behind on talent. It is behind on risk appetite. Building LLMs or foundational AI infra is not about capability. It is about capital. Not just any capital, but capital that is comfortable with long timelines, unclear outcomes, and massive burn. Most of India’s funding still chases near-term traction. SaaS, fintech, consumer. Sectors where returns are easier to model. But AI infra does not play by those rules. It demands belief before proof. The Middle East launching a sovereign fund to accelerate AI through public private bets is not just money. That is intent. India will not build foundational AI by hoping startups will figure it out alone. It needs conviction at the system level. And a willingness to back uncertainty at scale.

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Excited to announce Emergence Capital lead investment in Vesence's $9M seed round!   Legal AI should be a slam dunk. It’s a text-rich domain with tons of training data; well-suited for LLMs. Thus, it's no surprise that AI demand amongst lawyers has been extraordinary.   Unfortunately, in many scenarios, the reality hasn't yet delivered on the promise. Many AI tools fail for the same reason: they prioritize generation over precision.   Hallucinations and inconsistencies turn time-saving innovation into a liability and lawyers have to re-review every clause and citation, which adds hours of work instead of saving them.   Before AI, firms solved this with armies of reviewers scrolling through Word docs line by line.   Vesence is tailor made to bring precision to law at scale.   Instead of generating new text that might be wrong, Vesence uses AI to ensure what’s already written is right.   It enforces firm-wide standards, validates references, flags inconsistencies, and polishes language to perfection — all inside Word and Outlook.   Even more impressive than the product are the founders. Henrik and Ludvig embody the precision and persistence we look for at Emergence.   Henrik built the first version solo, including full Microsoft integrations. And Ludvig, a former pro athlete, brings the discipline of an elite competitor. Indeed, they literally slept at a top Swedish law firm to study the workflows firsthand as they built out the product. Intensity driving precision.   We're thrilled to welcome Henrik and Ludvig to the Emergence family.

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🚀 LLM Access is Easing up —And the Next Startup Boom is Just Beginning Three years ago, using LLMs was prohibitively expensive for any enterprise. Today, LLM inference costs have dropped 1000x since GPT-3’s launch, and with open-source models like DeepSeek pushing boundaries, we're headed toward even lower costs. While training remains capital-intensive (debates still on-going about DeepSeek costs), inference is becoming faster, cheaper, and more accessible—a shift that reshapes the AI startup landscape. For founders, this means critical decisions: ✅ Self-hosting vs. API access – Both have trade-offs. Self-hosting offers control & efficiency at scale, while APIs provide speed-to-market and lower operational complexity. ✅ Choosing the right model – Closed vs. open-source? Proprietary models offer reliability, but open-source is moving fast and increasingly competitive. ✅ Where real value lies – The next great companies won’t be LLM providers but AI-native applications that leverage these models to create defensible businesses. At Capria Ventures, we’re excited about what this means for the Applications Layer. AI-first startups can now build hyper-personalized, adaptive, and scalable solutions at a fraction of past costs. Whether in jobtech, fintech, agtech, or mobilitytech, the next breakout companies won’t just be AI-powered—they’ll redefine industries altogether. This is just the beginning. What’s your take? Are we at the tipping point for AI-native startups? #AI #GenAI #Startups #LLM #DeepTech #VentureCapital #AppliedAI

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