Bin Ren

The financial industry is cautiously embracing GenAI. The competitive edge will go to those who integrate it effectively into their existing business processes.

On March 4, I’ll be speaking at Bloomberg Invest to share insights from our experience at SigTech using our GenAI product to automate high-value manual processes.

We call our product MAGIC (Multi-Agent Generative Investment Copilots) in part because it's a memorable acronym, but mostly because we think what it does is amazing. 

Our clients use MAGIC to do macro research, equity fundamental analysis and commercial banking.

All these use cases share a few characteristics:

1. They require human experts' tacit knowledge, so called “judgement, experience and instincts”.

2. We use inductive learning to distill the tacit knowledge into LLM-powered AI agents, each of which specializes in a type of tasks.

3. AI agents then collaborate to automate complex business processes which until now have been impossible to codify and automate.

4. All these processes are intellectual, yet not highly creative; very manual, yet highly impactful on the revenue.

Global financial services, excluding payment, generate $20 trillion of annual revenue with $3 trillion of profits.

As one of the industries that most rely on human experts, financial services will see a dramatic impact of AI agents, both in terms of the cost structure and the profit margin.

Join me at Bloomberg Invest on March 4 to discuss how AI is shaping the future of finance.

Here is the agenda: https://events.bloomberglive.com/bloomberg-invest-2025/agenda

Infrastructure investment has tended to follow fixed patterns.

What’s happening with AI investment today mirrors the electrification boom of the 1900s with eerie precision.

• When Britain industrialized, steam power was 0.5% of GDP annually.
• America's grid expansion in the 1950s required 1.2% of annual GDP.
• Today, China allocates 0.9% of annual GDP to electrical infrastructure.

These aren't coincidences.

The math reveals a constant - new fundamentally transformative technologies require 0.5% to 1% of GDP in sustained infrastructure investment to create and sustain economic growth.

Generative AI’s projected required investment is tracking this trajectory.

The Big Techs are lining up $300bn in AI spending in 2025, which comes to about 1% of the US GDP, matching the precedents set by previous tech booms.

What's different this time is speed.

Historical S-curves of productivity gains (from electricity, Internet, etc) needed as many as 40 years to mature. 

AI is compressing this ‘realization period’ to a decade.

Two critical facts explain why this infrastructure phase is necessary:

1. AI compute requirements will demand 128GW of additional power by 2029 - equivalent to 15 nuclear plants worth of energy
2. Historically, tech revolutions have NEEDED a backend that can support them - and that backend ends up coming to investment worth between 0.5% and 1% of GDP

The pattern is unmistakable.

Like electricity before it, AI requires robust physical infrastructure before the software built on it can flourish. The UK's £60B grid expansion directly supports this digital transformation, mirroring their industrial electrification a century ago.

Societies consistently allocate between 0.5% and 1% of GDP to transformative infrastructure to fully realize the productivity gains.

So we are witnessing the acceleration of core economic principles which have governed technological revolutions for 250 years:

Infrastructure first, economic growth second.

Nuclear blasts in the mid 1940s spread tiny bits of radiation across our whole planet, blanketing everything in radionuclides.

One consequence was that steel that had been contaminated could no longer be used for highly sensitive tools, such as Geiger counters and space sensors.

As a result, searches began for so-called “low-background steel” produced before the nuclear era. People went to great lengths, such as raising battle ships sunk during World War I and II.

The release of ChatGPT in 2022 is having an analogous impact on content created before the emergence of generative AI.

The new technology has flooded the internet with machine-generated text, forever changing the landscape of online content.

Instead of uncontaminated steel, what's going to become rare is human-written content created before the era of Gen AI.

These "low-background tokens" which have been untouched by AI's influence will be vital for training future Large Language Models (LLMs). It’s a pristine dataset free from the echo chamber of AI's own voice.

As these datasets vanish, AI risks recycling its outputs. Data scarcity thus poses a significant challenge.

Synthetic data is now the deciding factor of which domains LLMs will see the most improvements in.

Domains with structured outputs and cheap automated verifiers are particularly well-suited for generating infinite high-quality synthetic data.

These domains include coding (verification via compiler and run time), math and formal logic (verification via Prover9, Lean, Isabelle), and controlled natural language systems (e.g. Attempto Controlled English) with strict syntax and semantics that automated reasoners can verify.

In these domains, we can generate a billion code samples or math proofs, run them through a verifier, and toss out 95% of the junk. The 5% that remain can be used as high-quality training data.

We can also refine generation strategies over time to produce harder samples as the model improves.

We should expect to see unprecedented progress in these domains in the near future.

I recently spent a few days in AlUla, Saudi Arabia, my first visit to the country. 

It’s a cultural shock, in part because the country only opened its borders to tourists six years ago and got off to a slow start due to Covid.

The government is hoping to take advantage of desert landscapes and ancient rock formations to turn AlUla into a high-end tourist destination. One attraction is the Maraya Concert Hall with its all-mirror design. 

Nearby, there is Hegra, Saudi Arabia’s first UNESCO World Heritage site. Hegra contains tombs and carvings and is often compared with Petra in Jordan. 

The government’s investments highlight a commitment to reshaping the region. It offers a unique opportunity to witness a country in transition, where contrasts abound.

On the one hand, it’s a conservative culture. On the other hand, all the people I met were open and curious about foreigners. I was struck by how friendly everyone was and how that warmth helped bridge cultural and linguistic divides.

Construction machines and vehicles dominate the landscape, reflecting the scale of development. Expatriates perform 95% of the work in AlUla, with men coming from India, Pakistan, and Africa, and women mainly from the Philippines.

There are brand-new roads crisscrossing the desert that link AlUla to nearby projects, including NEOM, the futuristic city along Saudi Arabia’s Red Sea coastline. It’s a massive effort aimed at promoting innovation and sustainability.

If NEOM reflects the vision for the future, Hegra preserves the country’s historical roots.

Some of the challenges of rapid development are evident in the contrast between AlUla’s modern infrastructure and the lack of services. Visitors sometimes struggle to find taxis, for example. 

AlUla’s streets feel safe, making exploration easy. You tend to see Japanese and South Korean cars alongside American muscle cars. I was surprised to see some Saudi women working in visible roles with obvious pride.

The combination of innovation and tradition makes AlUla unique, offering travellers a glimpse into a country balancing its past with ambitions for the future.

It’s a rare opportunity to experience a place in the midst of monumental change. 

I highly recommend a visit.

The announcement that a team of Chinese researchers has created an open-source large language model has caused deep declines in tech shares traded on the Nasdaq.

On the surface, it’s understandable. The new DeepSeek model delivers high-quality results at a fraction of the cost. Investors are concerned that DeepSeek’s ability to process results 10-30 times cheaper could pop the AI bubble.

I expect the opposite to happen.

If anything, DeepSeek's success shows that we can expect AI models to deliver extraordinary results at an affordable price faster than ever before.

I expect every tech firm to copy the "efficiency gains" championed by DeepSeek. It will mean that every $1 billion investment in AI in the U.S. will result in an extra 10-30 fold gain in intelligence output.

We’ve already read reports that Meta now has four "war rooms" working on replicating all the efficiency techniques published by DeepSeek, and we expect to see the benefits in its upcoming Llama 4 or 5 with a crazy jump in performance.

Short-term, this may be negative for Nvidia. That’s because people won’t need to buy as much hardware as originally planned. Instead, they can turn to engineering optimization.

Longer-term I am bullish on the ability of LLM enhancements to boost productivity and encourage new AI fueled innovation. 

Here is how I expect many of the tech giants to be affected: 

⬆️Apple benefits from lessons learnt in optimizing for memory bandwidth in “edge inference”

⬆️Meta benefits because it wants to deploy AI services everywhere

⬆️Microsoft benefits from selling cheaper Azure LLM services

⬆️Amazon benefits because it didn’t build an LLM and can now host frontier open source models

⬇️Google will be hurt as their advantage in TPU hardware is eroded and new AI competition will emerge for Google services such as search

⬇️AI labs that don't open source will suffer from brutal price competition, i.e. OpenAI, Anthropic and Cohere

There’s certainly an irony that China - known as a country that mass produces consumer goods – has emerged as the source of innovation that may mass produce intelligence!!

It’s a good indication that the focus of capital and attention on training LLMs is coming to an end and will shift to products and services that are being built on top of the technology.

The Chinese AI lab recently released several open-source large language models called “DeepSeek v3 and R1” that shocked the world. 

For once, the emphasis was on matching the performance of OpenAI 4o & o1 models but more than 10 times cheaper to train and deploy. 

I expect dramatic declines in costs to trigger a Cambrian explosion of new GenAI applications that start to encroach on traditional software, particularly SaaS products and a wide range of tools for human knowledge work. 

Steep declines in costs will likely shift the focus of the GenAI industry in 2025 to applications, particularly AI agents. Like the early days of the internet, it will be open season for a new generation of GenAI applications to move beyond "chatbots".

The Chinese lab spent an estimated $6 million to train DeepSeek v3 compared with $60-100 million for OpenAI GPT-4.

The inference cost of the DeepSeek v3 is 10x cheaper than the equivalent OpenAI 4o. The inference cost of the DeepSeek R1 reasoning model is 27x cheaper than the equivalent OpenAI o1, even with additional novel features.

If anything, I expect the curve of improvement to get much steeper.

Further improvement in transformer architecture and training algorithms could lead to another 10-fold improvement. New hardware from Nvidia, Cerebras, and Groq could deliver a comparable boost. Combined, that would be a 100-fold decline in costs per unit of performance.

The Chinese open-source LLMs can be hosted on all cloud providers, resulting in brutal competition for AI labs that train foundational models.

With Meta and Chinese AI labs such as Qwen and DeepSeek releasing state of the art LLMs, I expect AI labs like Mistral and Cohere will find it hard to remain independent. 

The large decline in LLM costs and increasing speed gives SigTech the ability to 1) run multiple versions of solutions in parallel 2) tackle increasingly complex jobs via a large team of agents at affordable costs and 3) finetune an open-source LLM to create domain-specific SigTech LLMs.

We believe these developments will transform research in capital markets. 

The moat for research will be driven by access to private proprietary data and analytics.

Any research team that cannot beat this new benchmark will struggle to survive.

A Chinese AI lab just released the best open source LLM: DeepSeek v3. I ran it through my private CFA level 1 exams to compare it against OpenAI o1, gpt-4o and Anthropic claude-sonnet.

The CFA exams were run without access to the CFA curriculum and with zero-shot inference.

Overall DeepSeek v3 delivered solid gpt-4 class performance. It even outperformed both gpt-4o and claude-sonnet in “Quantitative Methods” and “Corporate Finance” chapters.

These are amazing results because DeepSeek v3 is more than 10x cheaper to train and deploy.

DeepSeek v3 was trained on mere 2048 Nvidia H800s at a cost of $5.576M. This is in stark contrast to the 16k GPUs used to train Meta Llama 3.1, the 20k GPUs used to train Grok 2 and the 100k GPUs used to train Grok 3.

It was estimated that GPT-4 was first trained at a cost of $60-70M.

The inference costs of DeepSeek v3 are $0.27 per 1M input tokens and $1.1 per 1M output tokens. These are ~9x cheaper than gpt-4o ($2.5 per 1M input tokens, $10 per 1M output tokens) and ~13x cheaper than claude-sonnet ($3 per 1M input tokens, $15 per 1M output tokens).

We should expect the same Chinese AI lab to release a competitor to OpenAI o1 in the near future. 2025 will be a watershed moment in AI development.

I never got to meet Charlie Munger in person, but I’ve sought to channel his wisdom for decades.

Munger was content to remain in the shadows compared with his more famous partner, Warren Buffett. Together they ran Berkshire Hathaway and made countless investors wealthy.

I was one of the people who benefited, having owned shares since 2008. 

In 2010, I tried to attend the annual Berkshire shareholder meeting in Omaha but ran into difficulties getting the tourist visa I needed to travel from London.

The folks in the American embassy found it impossible to believe anyone would travel to Omaha, Nebraska on holiday. They started a long background check and I never made it.

Unable to see Munger in person, I devoured his writings. I bought two autographed copies of Poor Charlie’s Almanack, a compilation of Charlie’s speeches compiled by Peter Kaufman. I gave one to a friend who later founded a hedge fund in Hong Kong based on the philosophy of value investing. The other copy still sits in my library.

First published in 2005, the book remains as relevant today as ever. Munger’s multidisciplinary thinking and intellectual rigour haven’t aged a day. The book isn’t just about investing. It’s about learning how to think, how to reason, and how to understand the world.

Most of Munger’s famous quotes – even when they are about making money – transcend investing and seem just as applicable to other pursuits in life. For example: “The big money is not in the buying and selling, but in the waiting." or my favourite quote “The best way to get what you want is to deserve what you want.”

Today we are wrestling with generative AI, a transformational technology. Since Munger passed away in November 2023 at the age of 99 we will never know his thoughts about it.

He was famously skeptical and cautious about new technologies, often stressing that “strange things can happen” when change occurs so quickly.

But I tend to think he would have found large language models useful. LLMs have already surpassed humans in many benchmarks. They are, by design, multidisciplinary, trained on vast amounts of knowledge in multiple languages. Munger would have appreciated the breadth.

He often talked about the psychology of human misjudgment. His list of 25 standard causes of human errors was clever and counterintuitive. He knew the consequence of combining these biases to produce what he called a “lollapalooza” effect.

In a way, LLMs sidestep these pitfalls. They have no hidden motives, no self-interest, no job worries. They don’t get offended. They don’t scheme. They are patient, objective critics. With proper guidance, a LLM can help us identify our blind spots, much like Munger did.

LLMs may provide us with the kind of straight-thinking advice Munger would have given.

For someone often labeled an anti-tech investor, Munger’s spirit endures in LLMs. 

I think he would appreciate the irony.

I used OpenAI o1 reasoning LLM to review the EU AI Act.

It took o1 only 18 seconds to think. It took me 2 minutes to convert the PDFs to nicely formatted text.

My prompt is “Give me the top 3 logical contradictions in the EU AI Act I've pasted here: TEXT”

Here is the o1 response:

1. A "risk-based" approach that isn’t always risk-based:
The proposal emphasizes a proportionate, risk-based regulatory model, claiming that requirements scale with the potential harm of an AI system. However, the classification of high-risk AI systems is often triggered by the sector or intended use listed in Annex III—regardless of actual, demonstrated risk levels. This means certain AI applications are subject to stringent obligations purely because they fall into a predefined category, contradicting the idea of truly tailoring requirements to the real and context-specific risks posed by the technology.

2. Encouraging innovation while imposing costly compliance burdens:
The Act states it aims to foster innovation and support small and medium-sized enterprises by providing regulatory sandboxes and other guidance. At the same time, it imposes stringent conformity assessments, continuous monitoring, and record-keeping obligations for high-risk AI systems. These demands can become a heavy financial and administrative load, especially for smaller providers. This tension between claiming to nurture innovation and simultaneously mandating complex, resource-intensive compliance contradicts the stated objective of reducing barriers and encouraging market entrants.

3. Harmonizing the single market but layering multiple standards and bodies:

The proposal seeks to create a uniform framework to prevent market fragmentation, ensuring that AI systems can freely circulate throughout the EU. Yet, it references and depends on numerous existing and future sectoral regulations, the involvement of multiple notified bodies, and potential reliance on a patchwork of technical standards and common specifications. As a result, developers and users may navigate a complex web of overlapping rules and authorities. This complexity undercuts the promise of a clear and harmonized single market for AI, creating, in practice, a contradictory situation where the pursuit of uniformity may instead lead to confusion and uneven application.

I’d love  to run this prompt in o1 with all government documents.