Managing Director at Berkeley Advisory focused on getting companies and founders "fit, funded, big, and out." Founder of DragonAI: Agentic stock analysis rubric

Sydney
Joined December 2009
Thought of the day: tech stocks that need to invest in AI now have had capex drag FcF and, in the short run, are more like industrials that, by necessity, have regular and recurring capex spend.🤔
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If you know you know 🥷
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all. - Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract. - Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code. - And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while. The funnel is driven by a combination of factors: 1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel. 2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel. 3. Access. Free-tier, paid-tier, internal. So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
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We are about to hear a lot more about neuro-symbolic AI, its benefits, applications and rapid advancement. The future is not LLMs operating alone. It is hybrid systems that combine the language understanding and reasoning capabilities of large language models with structured knowledge, deterministic computation, formal rules and validation. We have already built this architecture for stock analysis. Our neuro-symbolic finance platform combines: • Filing ingestion • Deterministic financial extraction and calculations • A structured financial data layer • Accounting identities and validation gates • Forensic accounting rules and pattern libraries • Evidence hierarchies • Provenance tracking • Agent orchestration • Scoring frameworks • Abstention rules • Auditable outputs • An ontology and semantic layer The LLM can interpret language, identify context and assist with judgment. The symbolic system defines what financial concepts mean, how they relate, which calculations are permitted, what accounting rules must hold and whether the evidence supports the conclusion. That distinction matters. You cannot reliably analyse a company by simply asking an LLM to read an annual report and provide an opinion. Financial analysis requires controlled definitions, reconciled numbers, accounting logic, source evidence and explicit rules governing uncertainty. The future of financial AI is not a bigger chatbot. It is a controlled hybrid intelligence system in which probabilistic models operate inside a structured, testable and auditable analytical framework. That is neuro-symbolic finance and we have already built it.
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Dear @hellostake I have just been on the the telephone with your customer service team regarding not processing a clients withdrawal request. I strongly suggest you reach out and resolve the issue before this matter is escalated to regulators and lawyers.
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Read my tweet from yesterday 💡
Claude appears to be utterly unusable today. I've wasted hours and any task takes half an hour to do. His reasoning is flawed and it's not doing things and it's missing everything it was supposed to do. @AnthropicAI @claudeai opus 5 is a clusterfuck and Fable 5 is a horror story of expense. I'm hardly a heavy user in the general scheme of things but I blow through my weekly allowance of credits by Monday. This has to get sorted out. they need more inference and fast or they're gonna lose a lot of customers.
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You can’t simply ask a general AI model (LLM) for financial analysis. Here’s why: LLMs are next-word predictors. They generate plausible text, not verified analysis. What you actually need is a controlled layer that connects AI to source filings, financial data, accounting rules, evidence, and analyst workflows. That layer requires: • Filing ingestion • Deterministic financial extraction and calculations • A structured financial data layer • Accounting identity and validation gates • Forensic accounting rules and pattern libraries • Evidence hierarchies • Provenance tracking • Agent orchestration • Scoring frameworks • Abstention rules • Auditable outputs And an ontology or semantic layer Without this, you’re NOT doing analysis. You’re generating text and AI slop
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Alister Berkeley retweeted
Too many are focused on the falling cost of tokens and assume OS models are "cheaper" than the frontier But, many open source models use tokens inefficiently The better way to judge model economics is cost per task. On that basis, we see K3 as ~25% more expensive vs frontier
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I’m sceptical of the headlines around the size of Michael Burry’s short position on $PLTR. Why? Because there are way softer targets. To short a stock with conviction in size, I’d want the following full house: 1. Negative market headwinds ♠️ 2. Bad business model ♦️ 3. Bad management ♣️ 4. Bad balance sheet (high debt) ♥️ 5. Stock price > intrinsic valuation 🃏 Palantir only has one of these cards, whereas other stocks have all 5.
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A useful way to think about a neurosymbolic AI system is through Donald Rumsfeld’s famous distinction between known knowns, known unknowns, and unknown unknowns. It’s not the architecture itself; it’s a way of explaining what different parts of the system are responsible for.” Then briefly connect it: Known knowns → deterministic code establishes verified facts. Known unknowns → semantic and symbolic reasoning organise those facts and identify the questions that need answering. Unknown unknowns → probabilistic AI explores plausible explanations, anomalies, and emerging patterns.
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AI system design is evolving in stages, and each one sets up the next.@Jason @levie Stage one: better models. Stage two, where we are now: routers that shop each task across models for cost and capability. Stage three: hybrids with a coding agent that solve the prior question, should this task run on tokens at all, or be compiled into code? What repeats gets crystallised. What’s novel stays reasoning. The endgame: tokens are spent only on questions being asked for the first time. Everything settled runs as code at the cost of electricity, written by the model itself. The model stops being the engine and becomes the foundry, manufacturing the machinery that replaces it, task by task.
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I wonder how many private credit funds advanced capital to marginal businesses and used the owners' personal property as collateral for the loan, and whether that security package is about to be impaired as property prices decline.
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When the stock market is trying to price a new technology and work out if its impact and implications are structural or cyclical, then you can reasonably expect volatility and therefore shouldn't be leveraged up to the gills.
Exclusive: Situational Awareness, the hedge fund whose aggressive AI bets soured badly, is backed by a who’s who of Silicon Valley and Wall Street investors. Some warned the founder that he was taking big risks. on.wsj.com/453JJkL
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My native AI company has successfully integrated deterministic logic directly into the systems run time as well as created an ontological layer in the platforms agentic harness. Last night we scanned every annual report on the ASX into our agentic rubric and can run any finance and accounting ratio at scale. 🐉👁️
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If only there were signs ? $AMZN berkeleyadvisory.com.au/thou…
This quoted post is unavailable.
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Alister Berkeley retweeted
Replying to @toy59496
You missed the part where he finds some illiquid stock > writes some bs thesis > pumps retailers into > thesis falls over > he sells into retailers & pretends nothing ever happened
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American publishers “distilled” Charles Dickens
In other words, “Don’t hate the player, hate the game.”
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Three days with Fabel. Pure delight. It did work that would have taken me weeks in minutes. And this is only the first model trained on Blackwell. What happens when models are trained on Rubin? The future bottleneck may not be intelligence. It may be permissioned access to intelligence.
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You don’t just wake up as a Fortune 500 company, press a button, and become agentic. There’s a world of difference between a chatbot drafting a paper and an organisation changing how work gets done. The AI is usually the easy part. The workflow is the hard part. This will take years.
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The thing people are missing about AI is that executives have limited bandwidth. Every management team faces the same question: Do we use AI to automate existing processes and improve efficiency? Or do we use AI to build entirely new software products? Both may be attractive. Few organisations have the focus and execution capacity to do both well at the same time. The real AI constraint isn’t technology. It’s management attention.
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Alister Berkeley retweeted
fascinating chart AI not longer just about intelligence benchmarks, but intelligence x cost to run Green quadrant is the sweet spot: smart enough to matter, cheap enough to scale
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AI went: novelty → tinkering → and the next phase is the boring one. Integration. It stops being a thing you “go use” and disappears into your tools. Less magic, more plumbing. Everyone’s waiting for the next AI breakthrough. The real next phase isn’t a smarter model, it’s the unglamorous work of rebuilding workflows around the models we already have. The bottleneck moved from capability to organisational change.
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