AINL#004 Augmented Intelligence in Investment Management Newsletter
Welcome to the 004 Edition of the Newsletter on Augmented Intelligence in Investment Management (AINL). Every two weeks, we deliver five unique insights tailored to empower investment decision-makers. Our insights are carefully curated by a seasoned team of market specialists. Unbiased, actionable and practical. They will help you navigate through the noise.
AINL#004 SYNTHESIS
What do these recent developments mean for investment decision-makers?
1. Clarity in Intelligence and Expertise Distinction
The distinction between intelligence (capacity to solve novel problems) and expertise (mastery over familiar tasks) is critical. Investors should discern whether AI tools they rely on demonstrate genuine intelligence (adaptable and applicable across varied scenarios) or are merely optimized for specific tasks. This insight is crucial when assessing AI-driven investment tools, ensuring they are robust and versatile in adapting to changing market conditions rather than being narrowly specialized.
2. Applying Rigorous Economics to ESG Decisions
The importance of applying structure economic principles rather than impact factors to ESG-related investments is emphasized. It advises investors to critically evaluate ESG claims using established finance and economics frameworks. For example, overestimating the impact of ESG metrics without analyzing their long-term cash flow implications or externalities could lead to suboptimal allocation. The value of systematic, data-driven approaches to ESG to avoid common pitfalls like over-investment or mis-pricing is emphasized.
3. Reverse Thinking for Strategic Evaluation
Reverse thinking—starting from the solution and reasoning backward to verify or improve outcomes—enhances the accuracy and reliability of decisions. For investors, this approach can be translated into verifying investment theses by stress-testing their conclusions against various scenarios. It suggests that incorporating bidirectional analysis (forward and reverse reasoning) into decision-making processes can improve outcomes, such as evaluating whether an expected return aligns with the underlying assumptions of an investment. This insight holds value with or without the involvement of the machine.
TOP 5 ARTICLES
ARTICLE ONE
Defining Intelligence: Bridging the gap between human and artificial perspectives
HUMAN & ARTIFICIAL INTELLIGENCE | Journal of Intelligence | 6_2024 | Paper
Important Development
Similarly to human intelligence, a metric is suggested for AI to test its reliability and validity and to provide a standardized evaluation of artificial systems, referred to as artificial general intelligence (AGI). The article proposes operational definitions for human and artificial intelligence, focusing on the maximal capacity for successfully completing novel goals through respective perceptual-cognitive and computational processes.
Why Relevant to You?
General Management Practical Insight: current approaches to AI training and testing create potential risks, which may lead to artificial achievement or expertise rather than true artificial intelligence.
Insights for Fund Managers: the absence of standardized measurement procedures can lead to varied and inconsistent assessments of AI systems, making it difficult to compare and select technologies as well as assess the risks associated with deploying AI systems in financial markets.
ARTICLE TWO
Applying Economics – Not Gut Feel – To ESG
SUSTAINABLE INVESTING | London Business School, CEPR, and ECGI | 8_2023 | Paper
Important Findings
This paper highlights how the insights of mainstream economics can be applied to ESG, once we realize that ESG is no different to other investments that create long-term financial and social value. A large literature on corporate finance studies how to value investments; asset pricing explores how the stock market prices risks; welfare economics investigates externalities; private benefits analyze manager and investor preferences beyond shareholder value; optimal contracting considers how to achieve multiple objectives; and agency theory examines how to ensure that managers pursue shareholder preferences, including non-financial preferences.
Why Relevant to You?
The author identified how conventional thinking on ten key ESG issues is overturned when applying the insights of mainstream economics, immediately applicable for investment decision makers to increase profit and impact at once.
ARTICLE THREE
Why Your Finance Team Should Help Make Big AI Decisions
ARTIFICIAL INTELLLIGENCE | Harvard Business Review | 12_2024 | Article
Important Findings
The companies that succeed with AI aren’t necessarily those with the most advanced models or the largest data sets — they’re the ones that bring together diverse expertise to make the smartest decisions. Finance, with its ability to ascertain value, ensure accountability, and provide an objective perspective, plays an indispensable role in making AI investments truly pay off.
Among all the companies surveyed, the number-one destination for AI investments was customer insights, service, and experience, with 53% of companies naming it a top priority. The second-most-popular destination for AI funds was operations and production, followed in third by product, process, and technology development.
Why Relevant to You?
By involving finance teams early and often, companies can transform AI from an exciting possibility into a reliable growth engine — one that delivers both top-line expansion and operational excellence.no benefit.
ARTICLE FOUR
DeepSeek just outperformed OpenAI in 2 months with $6M versus OpenAI’s $6B
ARTIFICIAL INTELLIGENCE | 01_2025 | Article
Important Findings
The DeepSeek Breakthrough. They did with $6M what Big Tech does with billions.
- Built competitive AI for just $5.58M
- 2.78M GPU hours (vs Meta’s 30.8M)
- Using restricted Chinese GPUs
- Matches top AI performance
Potential Market Implications? NVDA’s GPU pricing under pressure, MSFT’s massive AI investment questioned, OpenAI’s cost structure threatened, AI democratization accelerating.
Is It Safe To Use? Open source means code transparency; Chinese ownership raises questions; Security concerns remain; Use case dependent; Identity confusion issues; Data contamination questions
Why Relevant to You?
Global Impact: Can this lead to the end of Western AI dominance? Can we trust AI development to China? Will this accelerate the AI arms race?
Industry Future: Will Big Tech’s AI investments collapse? Can startups now compete with giants?
ARTICLE FIVE
Antropic’s Claude ‘Computer Use’ Is A Game Changer
ARTIFICIAL INTELLIGENCE | Y Combinator | December 2024 | Video
Important Findings
Claude (and other LLM models too like ChatGPT, Gemini) had the ability to understand images for a while, so the next step was to train it on how and when to perform specific actions, like clicking buttons or writing text based on what’s displayed on the screen. By now, it should be clear that computer use is a step forward for AI. Up until now, developers have had to make tools to fit the model coming up with custom environments where AI’s use specially designed tools to do different various tasks. Now we can make the model fit the tools.
Why Relevant to You?
“Computer Use” opens up many applications. Businesses can automate repetitive tasks and increase efficiency, while the average user can save time on routine things like booking flights, ordering food or analyzing stock charts. It’s easy to see a future where AI agents handle most of the drudge work for us.
