High Conviction Concentrated Growth Investing, Fallacy Alarm

Fallacy Alarm

High Conviction Concentrated Growth Investing

High conviction concentrated growth investing. Blends single stock, macro and thematic research into powerful long and short ideas. I am convinced that a robust and comprehensive investment process requires macro and micro considerations. And it requires a deep understanding what you are looking for and what you are invested in. Digitization has changed the dynamics both how businesses operate and how investors compete for returns. Ideas can scale much faster and fail much faster than before. The biggest value creation happens before it can be spotted in financial statements. As a result, my approach can be best summarized as high conviction, high concentration growth investing, embedded into a macro framework built on the realization that our current demographic set-up, monetary paradigm and innovation cycle will likely deliver a strong decade for nominal and real returns of productive assets.

Equity Bottom-Up

<ul><li>Anthropic is a reckless and entitled AI start-up, symbolic of a tech industry that is completely out of control and drunk on its own success.</li><li>A judge in California just ruled that they can use copyrighted data without permission to train their models.</li><li>This allows them to make money of other people’s works without compensating them adequately.</li></ul>

Anthropic is a reckless and entitled AI start-up, symbolic of a tech industry that is completely out of control and drunk on its own success.A judge in California just ruled that they can use copyrighted data without permission to train their models.This allows them to make money of other people’s works without compensating them adequately.

Semiconductor & Technology Specialist, Ingenuity

William Keating

Semiconductor & Technology Specialist

Ingenuity

Our focus is exclusively on in-depth research & consulting in the broader semiconductor industry. We focus on the key players, namely Intel, Micron, Qualcomm, Apple, Samsung, NVIDIA, AMD, TSMC & Global Foundries - as well as some of the lesser known, but nonetheless critical, companies such as ASML (for EUV), Mellanox, Cavium, Xilinx etc. We specialise in their products, roadmaps, process technologies, competitive advantages & threats etc. We also help our clients understand the implications of new and emerging technologies, e.g. 3D X-Point, 3D NAND, Carbon Nanotubes, Autonomous Driving, Graphene, Silicon Photonics, Wafer-Level Optics, Artificial Intelligence, FPGA, GPU, ASIC etc. 

Semiconductor Analysis & Research

<ul>
<li>Microsoft has significantly course corrected on their compute capacity build out, demurred on their right of first refusal for OpenAI compute demand and adopted a risk off "fungible" compute strategy</li>
<li>Mr. Nadella thinks AGI as more hype than substance, "jagged" intelligence will remain problematic for a longer, and the true measure of AI success will be measured by GDP growth</li>
<li>Microsoft stopped reporting AI-driven ARR when the number hit $13 billion six months ago, but why? Broadly deploying AI into productivity tools is a marathon not a sprint.</li>
</ul>


Microsoft has significantly course corrected on their compute capacity build out, demurred on their right of first refusal for OpenAI compute demand and adopted a risk off "fungible" compute strategy
Mr. Nadella thinks AGI as more hype than substance, "jagged" intelligence will remain problematic for a longer, and the true measure of AI success will be measured by GDP growth
Microsoft stopped reporting AI-driven ARR when the number hit $13 billion six months ago, but why? Broadly deploying AI into productivity tools is a marathon not a sprint.


Microsoft. Acting Like There's An AI Bubble Without Saying There's An AI Bubble

HarmoniQ Insights

William Mann

Independent Insight Provider

At HarmoniQ Insights, we empower investors to enhance their investment strategies by integrating cutting-edge quantitative research processes and technologies. Our solutions provide access to leading quantitative data providers and services, tailored to align with each client’s unique goals. By bridging the gap between traditional investment approaches and advanced data-driven methods, we help clients gain a competitive edge through innovation in both process and technology.

Cross Asset Strategy

<ul>
<li>A fascinating lunch discussion with LLM engineer sparked an investigation into how AI models process information through vector spaces - revealing parallel but mechanistically different biases compared to human analysts </li>
<li>Built comprehensive framework mapping traditional financial biases against their LLM counterparts - showing how things like confirmation bias manifest differently (emotional investment vs statistical pattern reinforcement)</li>
<li>Result: Effective quant research requires understanding both human AND machine biases - can't just apply traditional risk controls to LLMs. Need tailored mitigations for how biases uniquely manifest in #AI.  </li>
</ul>


A fascinating lunch discussion with LLM engineer sparked an investigation into how AI models process information through vector spaces - revealing parallel but mechanistically different biases compared to human analysts 
Built comprehensive framework mapping traditional financial biases against their LLM counterparts - showing how things like confirmation bias manifest differently (emotional investment vs statistical pattern reinforcement)
Result: Effective quant research requires understanding both human AND machine biases - can't just apply traditional risk controls to LLMs. Need tailored mitigations for how biases uniquely manifest in #AI.  


Understanding LLM Biases in Quantitative Research: From Lunch Conversation to Framework

<ul>
<li>A Comprehensive Guide to Building AI-Powered Investment Systems: The application of Large Language Models (LLMs) in quantitative finance, focusing on the current state of LLM technology in financial applications </li>
<li>I also cover: 1. Comparative analysis of leading models                2. Implementation frameworks for production systems 3. Risk management and quality control considerations </li>
<li>Key Findings: 1. Multi-model approaches outperform single-model solutions 2. Production implementation requires robust quality controls 3. Model selection should be task-specific within the investment process </li>
</ul>


A Comprehensive Guide to Building AI-Powered Investment Systems: The application of Large Language Models (LLMs) in quantitative finance, focusing on the current state of LLM technology in financial applications 
I also cover: 1. Comparative analysis of leading models                2. Implementation frameworks for production systems 3. Risk management and quality control considerations 
Key Findings: 1. Multi-model approaches outperform single-model solutions 2. Production implementation requires robust quality controls 3. Model selection should be task-specific within the investment process 


The LLM Quant Revolution: From ChatGPT to Wall Street

Thematic (Sector/Industry)

<ul>
<li>Microsoft dominated the technology news cycle recently on the back of a report by a TD Cowen analyst that the company is cancelling data centre leases in the US</li>
<li>Dwarkesh Patel's podcast with Microsoft CEO Satya Nadella last week provided a fascinating insight into his thinking when it comes to AI compute build out, AI winners &amp; losers etc.</li>
<li>Mr Nadella's approach to AI compute build out has always been a pragmatic "show me the money and I'll build some more compute" approach. Now we better understand why.</li>
</ul>


Microsoft dominated the technology news cycle recently on the back of a report by a TD Cowen analyst that the company is cancelling data centre leases in the US
Dwarkesh Patel's podcast with Microsoft CEO Satya Nadella last week provided a fascinating insight into his thinking when it comes to AI compute build out, AI winners &amp; losers etc.
Mr Nadella's approach to AI compute build out has always been a pragmatic "show me the money and I'll build some more compute" approach. Now we better understand why.


Microsoft CEO On AI CapEx: Show Me The Money!

United States

Anthropic, PBC operates as an AI safety and research company. The Company builds reliable, interpretable, and steerable AI systems. Anthropic serves customers in the United States.

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Anthropic (1892140D US)

Anthropic's to Every One of Us

Anthropic is a reckless and entitled AI start-up, symbolic of a tech industry that is completely out of control and drunk on its own success.

Fallacy Alarm

Microsoft. Acting Like There's An AI Bubble Without Saying There's An AI Bubble

It could just be me, but I think Mr. Nadella is trying to tell us something about the state of AI. We should probably listen. Let’s see.

William Keating

Understanding LLM Biases in Quantitative Research: From Lunch Conversation to Framework

We are going to provide a full-blown update on LLMs through Q1 2025. In the meantime, we are still finding issues with 🤖 “Hallucinating on Drugs” –...

William Mann

The LLM Quant Revolution: From ChatGPT to Wall Street

Please see the AI Agent Podcast explanation of this paper here: https://youtu.be/11A2tremqm0?si=VUMm6WhWGEQRRkNU If you enjoy this podcast, please...

William Mann

Microsoft CEO On AI CapEx: Show Me The Money!

Mr Nadella's approach to AI compute build out has always been a pragmatic "show me the money and I'll build some more compute" approach. After last...

William Keating

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