AINL#022 Augmented Intelligence in Investment Management Newsletter
Welcome to the 022 Edition of the Newsletter on Augmented Intelligence in Investment Management.
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 noise.
AINL#022 SYNTHESIS
1. Build Decision Quality Advantage Through Responsible AI Integration
Khan, Li, and Cao (2025) show that the intelligent use of large language models (LLMs) in finance requires balancing accuracy, cost, and privacy, turning AI from a tool into a true decision-architecture layer. Yet, as recent research on the “AI Advantage vs. AI Penalty” paradox reveals, even if AI could outperform humans in empathy or precision at one point, trust and perceived authenticity determine acceptance. For investors, this means integrating LLMs not just for analytical leverage but within transparent, explainable, and ethically framed workflows that strengthen decision quality and stakeholder confidence.
2. Access to Infrastructure Is the New Moat
Restrepo (2025) demonstrates that in an AGI-driven economy, value creation becomes linear in compute, not human labor, returns gravitate toward those who own chips, data centers, and energy. Sauerwein’s (2025) AXIOM model adds a counterpoint: smaller, cheaper architectures can democratize access and compress compute costs by orders of magnitude. For professional investors, this shifts competitive advantage from intellectual property toward control of computational infrastructure and energy efficiency, requiring capital allocation strategies that anticipate the revaluation of the compute stack as a core determinant of future growth.
3. Design for Cognitive Diversity to Escape the “Consensus Trap”
Chu and Evans (2021) warn that algorithmic systems, by reinforcing existing paradigms, risk intellectual and market stagnation, an insight increasingly relevant as AI-guided models shape investment decisions. When everyone optimizes on similar data and logic, alpha decays. Investors must therefore design decision processes that preserve cognitive diversity, integrating non-consensus reasoning and human judgment alongside machine inference to sustain adaptive capacity and uncover signals overlooked by convergent, model-driven strategies.
TOP 5 ARTICLES
ARTICLE ONE
Bridging finance and AI: a comprehensive survey of large language models in financial system
ARTIFICIAL INTELLIGENCE | Khan, Li, & Cao | 2025 | Paper
Important Development
The paper provides a structured overview of LLM adoption in finance. It proposes a taxonomy of financial tasks for LLMs, which includes, linguistic tasks, sentiment analysis, financial reasoning and question answering, forecasting, as well as agent-based and decision support applications.
Furthermore, it contains a list of proprietary and open-source models and key training datasets, which have been developed for financial purposes. The authors also compare performance trade-offs across models, examine evaluation metrics relevant to financial applications, and discuss deployment challenges such as data privacy, bias, and explainability.
Why Relevant to You?
Overviews of available LLM solutions as well as taxonomies are useful tools when thinking about AI adoption within one’s own institution. The author’s multi-level adoption framework offers practical guidance for balancing accuracy, cost, and privacy across institutions. Hence, the paper is supposed to help researchers and practitioners to responsibly leverage LLMs for more transparent, effective, and inclusive financial AI.
ARTICLE TWO
AI-Generated Empathy: Opportunities, Limits, and Future Directions
HUMAN & ARTIFICIAL INTELLIGENCE | Desmond C. Ong et al.. | 2025 | Paper
Important Findings
This article conducts a comprehensive literature review and meta-analysis of recent empirical studies. AI-generated messages are now often rated as more empathic than human-written ones, a phenomenon termed the “AI Advantage.” However, when people know the source is AI, they rate the messages less favourably – creating an “AI Penalty.” This paradox is central to emerging psychological research on how empathy is perceived. These findings have practical implications for mental health, customer support, and human-AI relationships. As LLMs increasingly provide emotional support, understanding how people interpret “machine empathy” becomes critical for ethical deployment and user well-being in both clinical and everyday human interactions.
Why Relevant to You?
When designing business practices that incorporate AI, it is essential to consider the critical roles of transparency, trust, and perceived authenticity in shaping user responses to AI-generated communication. Disclosure strategies and message framing can greatly influence user satisfaction and acceptance. This also underscores the ethical responsibilityof using AI in emotionally sensitive contexts.
To build sustainable and effective AI systems, businesses must prioritize responsible AI design, embed cultural and emotional intelligence, and implement continuous monitoring to assess how AI-driven interactions impact user trust, brand perception, and long-term engagement.
ARTICLE THREE
Work and Growth in the Age of AGI: Why Labor’s Share of the Economy Falls Apart
ARTIFICIAL INTELLIGENCE & ECONOMIC GROWTH | Restrepo P. | July 2025 | Paper
Important Findings
Yale economist Pascual Restrepo explains how the advent of Artificial General Intelligence (AGI) would redefine long-term economic growth and labor markets. His model classifies all forms of work into two groups. Bottleneck work are the tasks required for growth—such as pushing forward science, producing energy, or maintaining logistics and infrastructure—and accessory work are activities which, while useful, are not essential for sustainable economic growth, such as arts, hospitality, or therapy.
The analysis shows that with more and more computational capacity, ultimately all the bottleneck work is mechanized. Then economic production is linear in compute: it is growth only by a matter of expanding computational capacity and not human effort. Human labor is economically productive, but only to the extent that it economizes on scarce compute. Wages, therefore, come out at the opportunity cost of re-implementing human work with compute. In this case, workers’ portion of the GDP unravels over time and almost all income accrues to owners of computational infrastructure.
Why Relevant to You?
For investors, the stakes run deep. Economic value in an AGI economy is no longer determined by human capability but by access and control of compute. Even high-skilled workers have their wages stagnate, since wages only recognize the compute cost of copying and not overall economic progress. Certain socially intensive types of ancillary labor will probably survive, but this labor is bound in worth and isolated from overall economic growth.
The principal takeaway is clear: in such a world as this where AGI is made possible, growth is entirely dependent on compute, and returns coalesce around the owners and controllers of computational capacity. Compute infrastructure placement—chips, datacenters, energy, and platforms that manage allocation—is the controlling factor in capturing value as labor is removed from the equation for growth.
ARTICLE FOUR
Rethinking AI architectures: the case for AXIOM
ARTIFICIAL INTELLIGENCE | Sauerwein D. | 09_2025 | Research Insight
Important Findings
According to AXIOM’s benchmark results, it can perform better at a significantly lower cost: 442 times smaller and 39 times less expensive to operate than DeepMind’s top reinforcement models. When coupled with learning that is 7.6 times faster, this establishes AXIOM as an architecture with a radically different compute profile.
Why Relevant to You?
For investors, the economic implications are vast:
- Lower barriers to entry > Corporates, academic labs, and startups outside of Big Tech could have access to frontier-level AI without hyperscaler budgets.
- Edge deployment > Smaller models and lower GPU requirements could render AI agents feasible in resource-constrained real-world environments (e.g., mobile, IoT, robotics).
- Capital efficiency > Reduced cost of compute rewrites ROI formulas for AI investment-which is friendly in a time of rising GPU shortages and regulatory stress on power usage.
Scalable or not, AXIOM can potentially redefine the economics of AI, unlocking a tsunami of new usage and players where cost rendered high-end AI out of reach hitherto.
ARTICLE FIVE
Why a 2021 Study on Scientific Stagnation Matters More Than Ever in the Age of AI
ARTIFICIAL INTELLIGENCE | Chu and Evans | 10_2021 | Article
Important Findings
Although the paper was published several years ago, it has become strikingly relevant in today’s age of AI. As algorithmic systems increasingly guide research, they tend to reinforce established patterns and reward incremental rather than radical advances. This dynamic echoes the paper’s warning that dominant scientific paradigms can stifle breakthrough thinking.
Why Relevant to You?
This paper is particularly interesting for the investment industry because it sheds light on how systemic forces can lead to intellectual and technological stagnation—an issue relevant as AI-driven models increasingly dominate decision-making. The study reminds investors that generating alpha in the age of AI requires deliberately fostering unconventional insights rather than reinforcing the consensus.
