AI Research
A Handbook to Evaluating Global AI Investments

Artificial intelligence has emerged as one of the most powerful and pervasive investment themes of the modern era. But investing in AI isn’t as simple as chasing the latest breakthrough or jumping into a hyped fund. Successful thematic investing requires thoughtful evaluation—of the theme itself, the investment vehicle, and the fund’s execution strategy.
To help financial advisors better assess which funds are the best fit for clients, our 2025 Investing in Artificial Intelligence Funds report shares a practical framework for evaluation—breaking down the investment into three key components.
1. Evaluating the Theme
The first step in assessing a thematic fund is evaluating whether the underlying theme is well-defined, investable, and durable.
Clarity and Investability
AI is clearly investable, with most funds in this space concentrating on large-cap, highly liquid companies. Our holdings frequency analysis shows a strong consensus on core holdings across AI and big data funds. This is important, as it indicates that investors generally agree on what constitutes AI exposure—typically including chip manufacturers and software firms.
Performance and Responsiveness
The Morningstar Global Artificial Intelligence + Big Data Consensus Index—a proxy for the theme—has behaved in line with expectations. It surged after the launch of ChatGPT 3.5 in late 2022 and responded to geopolitical shocks like US export restrictions on AI chips in early 2025. These reactions confirm that the theme reflects real and distinct risk/return drivers.
Durability and Growth Potential
AI has been an investable theme via funds since at least 2015, making it one of the more mature technology narratives. Its use cases—ranging from customer service automation to drug discovery—are expanding rapidly. But the theme isn’t without obstacles.
Inhibitors to Growth
Two major headwinds could slow AI’s momentum:
- Energy Consumption: The computing demands of AI are enormous and growing, straining existing power infrastructure and raising sustainability concerns.
- Regulation: Geopolitical tensions and regulatory actions—such as export restrictions on chips or emerging AI usage laws—could limit global scalability and increase compliance costs.
2. Assessing the Investment Vehicle
Once the theme is validated, the next step is selecting the right vehicle. While buying individual stocks can offer precision, thematic funds offer important benefits.
Why Choose a Fund?
- Stock-specific risk diversification: They reduce stock-specific risk. For instance, Tesla’s TSLA decline in early 2025 due to Elon Musk’s political activity highlights the downside of concentrated bets.
- Exposure to the value chain: Funds can target high-potential segments within the theme, from AI infrastructure to applications.
- Winner-take-all dynamics: In technology themes driven by scale—such as AI and big data—we often see winner-take-all outcomes. By investing in a basket of thematic stocks, investors can ensure exposure to any potential “shooting stars.”
Portfolio Impacts
Two major US funds focused on the AI and big data theme illustrate the diversity in approaches.
Global X Artificial Intelligence & Technology ETF AIQ
This is the oldest and largest US-domiciled AI ETF, following a rules-based index. It allocates across software (for example, natural language processing, AI-as-a-service) and hardware (for example, chips, quantum computing).
- Selection: Uses a proprietary scoring system to assess thematic relevance.
- Weighting: Applies caps (3% maximum per stock) to avoid overconcentration.
- Stability: Rebalances annually, favoring structured implementation over rapid responsiveness.
- Limitations: The indexed approach means the strategy is slow to incorporate changes in the market, important IPOs, and so on. Despite being indexed, the selection methodology is not fully transparent, and fees are higher than many more vanilla indexed ETFs.
Roundhill Generative AI & Technology ETF CHAT
This newer, actively managed fund targets companies focused on generative AI.
- Focus: High conviction, fewer holdings (often less than 40), and concentrated sector exposure.
- Flexibility: Can quickly pivot to new opportunities—like its early investment in CoreWeave, a cloud infrastructure firm that went public in 2025. However, poor timing decisions on investments such as SoundhoundAI SOUN and SenseTime Group have weighed heavily on returns, highlighting how flexibility can work against the fund, too.
- Challenges: Higher portfolio turnover, greater reliance on manager skill, and higher monitoring demands for investors.
- Differentiation: Excludes well-known names like Tesla and Netflix NFLX because of limited generative AI exposure, and maintains a 25% allocation to the Magnificent Seven (compared with 20% in Global X).
Each fund has its own strengths. Global X offers broader, more stable exposure, while Roundhill aims for a higher conviction full active strategy—which places a higher emphasis on manager skill and therefore warrants even more rigorous due diligence before investing.
3. Implementing Thematic Funds Wisely
Even a well-designed fund can disappoint if misused. Proper implementation is critical.
Portfolio Fit
AI and big data funds are typically highly volatile and sit in the high-growth quadrant of the Morningstar Style Box. They’re best deployed as tactical or satellite allocations—not as core holdings.
- Overlap Risks: Many AI funds have exposure to the Magnificent Seven, already widely held in most portfolios. This concentration risk should be monitored.
- Growth Bias: AI funds often exhibit high beta, amplifying broader market swings.
Timing and Behavior
Investors frequently mistime thematic entries, buying during hype and selling during drawdowns. Given their volatility, AI funds are best approached with a long-term, buy-and-hold mindset.
Valuation Awareness
Chasing themes without regard for valuation can lead to underperformance. Using price/fair value metrics may help pick entry points that will give longer-term investors the best chance of success. For instance, investors who bought into AI funds in September 2022—when valuations were lowest—benefited from the subsequent ChatGPT-fueled rally. However, valuation alone isn’t a silver bullet, as sentiment and narrative often drive short-term flows.

Identify the Right AI Funds for Clients
A winning thematic investment entails selecting the right investment, exposed to the right theme, and deploying it sensibly. By knowing how to efficiently assess US AI funds and beyond, financial advisors can find investment opportunities and deliver value to clients.
AI Research
Study reveals why humans adapt better than AI

Humans adapt to new situations through abstraction, while AI relies on statistical or rule-based methods, limiting flexibility in unfamiliar scenarios.
A new interdisciplinary study from Bielefeld University and other leading institutions explores why humans excel at adapting to new situations while AI systems often struggle. Researchers found humans generalise through abstraction and concepts, while AI relies on statistical or rule-based methods.
The study proposes a framework to align human and AI reasoning, defining generalisation, how it works, and how it can be assessed. Experts say differences in generalisation limit AI flexibility and stress the need for human-centred design in medicine, transport, and decision-making.
Researchers collaborated across more than 20 institutions, including Bielefeld, Bamberg, Amsterdam, and Oxford, under the SAIL project. The initiative aims to develop AI systems that are sustainable, transparent, and better able to support human values and decision-making.
Interdisciplinary insights may guide the responsible use of AI in human-AI teams, ensuring machines complement rather than disrupt human judgement.
The findings underline the importance of bridging cognitive science and AI research to foster more adaptable, trustworthy, and human-aligned AI systems capable of tackling complex, real-world challenges.
Would you like to learn more about AI, tech and digital diplomacy? If so, ask our Diplo chatbot!
AI Research
Josh Bersin Company Research Reveals How Talent Acquisition Is Being Revolutionized by AI

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Jobs aren’t disappearing. Through AI, talent acquisition is fast evolving from hand-crafted interviewing and recruiting to a data-driven model that ensures the right talent is hired at the right time, for the right role with unmatched accuracy
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Traditional recruiting isn’t working: in 2024, only 17% of applicants received interviews and 60% abandoned slow application processes
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AI drives 2–3x faster hiring, stronger candidate quality, sharper targeting—and 95% candidate satisfaction at Foundever, from 200,000+ applicants in just six months
OAKLAND, Calif., Sept. 16, 2025 /PRNewswire/ — The Josh Bersin Company, the world’s most trusted HR advisory firm, today released new research showing that jobs aren’t disappearing—they’re being matched with greater intelligence. The research, produced in collaboration with AMS, reveals major advances in talent acquisition (TA) driven by AI-enabled technology, which are yielding 2–3x faster time to hire, stronger candidate-role matches, and unprecedented precision in sourcing.
The global market for recruiting, hiring, and staffing is over $850 billion and is growing at 13% per year, despite the economic slowdown, though signs of strain are evident. This means TA leaders are turning to AI to adapt, as AI transforms jobs, creates the need for new roles, new skills, and AI expertise.
According to the research and advisory firm, even without AI disruption, over 20% of employees consider changing jobs each year, driving demand for a new wave of high-precision, AI-powered tools for assessment, interviewing, selection, and hiring. Companies joining this AI revolution are hiring 200-300% faster, with greater accuracy and efficiency than their peers, despite the job market slowdown.
According to the report, The Talent Acquisition Revolution: How AI is Transforming Recruiting, the TA automation revolution is delivering benefits across the hiring ecosystem: job seekers experience faster recognition and better fit, while employers gain accurate, real-time, and highly scalable recruitment.
This is against a context of failure with current hiring. In 2024, less than one in four (17%) of applicants made it to the interview stage, and 60% of job seekers, due to too-slow hiring portals, abandoned the whole application process.
The research shows how organizations are already realizing benefits such as lower hiring costs, stronger internal mobility, and higher productivity. AI-empowered TA teams are also streamlining operations by shifting large portions of manual, admin-heavy work to specialized vendors.
AI Research
Causaly Introduces First Agentic AI Platform Built for Life Sciences Research and Development
Specialized AI agents automate research workflows and accelerate
drug discovery and development with transparent, evidence-backed insights
LONDON, Sept. 16, 2025 /PRNewswire/ — Causaly today introduced Causaly Agentic Research, an agentic AI breakthrough that delivers the transparency and scientific rigor that life sciences research and development demands. First-of-their-kind, specialized AI agents access, analyze, and synthesize comprehensive internal and external biomedical knowledge and competitive intelligence. Scientists can now automate complex tasks and workflows to scale R&D operations, discover novel insights, and drive faster decisions with confidence, precision, and clarity.
Industry-specific scientific AI agents
Causaly Agentic Research builds on Causaly Deep Research with a conversational interface that lets users interact directly with Causaly AI research agents. Unlike legacy literature review tools and general-purpose AI tools, Causaly Agentic Research uses industry-specific AI agents built for life sciences R&D and securely combines internal and external data to create a single source of truth for research. Causaly AI agents complete multi-step tasks across drug discovery and development, from generating and testing hypotheses to producing structured, transparent results always backed by evidence.
“Agentic AI fundamentally changes how life sciences conducts research,” said Yiannis Kiachopoulos, co-founder and CEO of Causaly. “Causaly Agentic Research emulates the scientific process, automatically analyzing data, finding biological relationships, and reasoning through problems. AI agents work like digital assistants, eliminating manual tasks and dependencies on other teams, so scientists can access more diverse evidence sources, de-risk decision-making, and focus on higher-value work.”
Solving critical research challenges
Research and development teams need access to vast amounts of biomedical data, but manual and siloed processes slow research and create long cycle times for getting treatments to market. Scientists spend weeks analyzing narrow slices of data while critical insights remain hidden. Human biases influence decisions, and the volume of scientific information overwhelms traditional research approaches.
Causaly addresses these challenges as the first agentic AI platform for scientists that combines extensive biomedical information with competitive intelligence and proprietary datasets. With a single, intelligent interface for scientific discovery that fits within scientists’ existing workflows, research and development teams can eliminate silos, improve productivity, and accelerate scientific ideas to market.
Comprehensive agentic AI research platform
As part of the Causaly platform, Causaly Agentic Research provides scientists multiple AI agents that collaborate to:
- Conduct complex analysis and provide answers that move research forward
- Verify quality and accuracy to dramatically reduce time-to-discovery
- Continuously scan the scientific landscape to surface critical signals and emerging evidence in real time
- Deliver fully traceable insights that help teams make confident, evidence-backed decisions while maintaining scientific rigor for regulatory approval
- Connect seamlessly with internal systems, public applications, data sources, and even other AI agents, unifying scientific discovery
Availability
Causaly Agentic Research will be available in October 2025, with a conversational interface and foundational AI agents to accelerate drug discovery and development. Additional specialized AI agents are planned for availability by the end of the year.
Explore how Causaly Agentic Research can redefine your R&D workflows and bring the future of drug development to your organization at causaly.com/products/agentic-research.
About Causaly
Causaly is a leader in AI for the life sciences industry. Leading biopharmaceutical companies use the Causaly AI platform to find, visualize, and interpret biomedical knowledge and automate critical research workflows. To learn how Causaly is accelerating drug discovery through transformative AI technologies and getting critical treatments to patients faster, visit www.causaly.com.
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