AI: Reality, risk and returns in the next tech revolution

Artificial intelligence (AI) has rapidly moved from frontier tech into real-world deployment, with implications rippling across every sector of the economy. But despite the excitement, markets still struggle to price its complexity, cost and global implications accurately.

We view AI not as a monolithic trend but as a structurally rich, multi-dimensional investment opportunity. It’s a transformation that demands deep technological insight, selectivity, and a global perspective.

AI’s inflection point

AI now joins the ranks of general-purpose technologies alongside electricity and the internet. Already, it’s transforming workflows: Microsoft claims AI writes up to 30% of its code; Recursion, backed by Nvidia, has slashed drug target discovery times; and AI-driven customer service has cut resolution times by over 50%.

The real breakthrough lies in large language models (LLMs) and multi-modal AI, which can ingest and generate text, images, audio, and video with human-level fluency transforming everything from legal analysis and insurance underwriting to marketing and design.

This technological leap is translating into widespread commercial deployment and capex growth. Global AI infrastructure spend could reach $800bn in direct AI equipment, and another $300bn in ancillary equipment/spend, leading to $1.1trn in total AI capex annually by 2027.

Just as the internet redefined business models in the 2000s, we believe that AI is reshaping competitive dynamics across the economy.

Capex cycle is real but monetisation is uneven

We see accelerating AI infrastructure spend over the next decade. However, the market continues to underestimate the cost, time, and operational drag associated with this investment wave.

Microsoft, one of the most aggressive investors in AI infrastructure, underscores a broader challenge facing industry leaders as monetization pathways are still maturing. Despite its early-mover advantage and deep integration across cloud and productivity platforms, Microsoft has come under pressure as the earnings visibility around its AI investments remains uncertain, prompting downward revisions to future earnings. This disconnect between technological momentum and earnings traction is likely to persist across parts of the market, particularly for firms building at the infrastructure layer without immediate application-layer returns.

Adding further pressure is the rapid rise of open-source models, dramatically lowering the cost of entry. Chinese firm DeepSeek is a case in point. It claims GPT-4-class performance at a fraction of the cost: 94% lower training time, 90% lower inference costs, and pricing that’s 30 times cheaper per million tokens than OpenAI.

DeepSeek’s model is open-source, accelerating access and eroding assumptions that only the largest Western tech firms can compete at the frontier. Other Chinese firms, including Baidu and Tencent, have pushed costs down even further in 2025, reinforcing this trend.

For investors, this shifts the focus from those building the biggest models to those who control distribution, integrate AI capabilities deeply, or develop differentiated applications.

Commercial use cases are scaling faster in consumer-facing verticals

Much of the mainstream narrative around AI investment focuses on foundational model developers and enterprise AI platforms. While these are important, we believe that some of the most immediate and economically scalable AI adoption is taking place in consumer-facing verticals, where feedback loops are faster, product cycles are shorter, and monetization paths are more direct.
In particular, we are observing concrete deployment and revenue generation in four high-impact domains:

1. Advertising and marketing

AI models are driving increasingly sophisticated content generation and audience targeting strategies. Tools that create dynamic advertising copy, localised imagery, and tailored video creatives at scale are already reducing customer acquisition costs for brands and agencies. Moreover, major platforms (eg Meta, Google) are embedding generative AI into ad-buying interfaces, enabling automated campaign optimization based on real-time performance data.

This translates into measurable productivity gains for advertisers and more efficient monetization for platforms-a rare case where AI boosts both operating margins and revenue simultaneously.

2. Ecommerce and retail

In digital commerce, AI is being deployed to improve search relevance, recommendation engines, and customer support. Large retailers and marketplaces are integrating multi-modal AI to create voice or image-based search experiences, real-time personal shopping assistants, and fully automated helpdesks.

This not only improves conversion rates and customer satisfaction but also reduces reliance on human agents, cutting costs in high-volume service environments. Importantly, these tools are already deployed at scale, making ecommerce one of the most mature AI verticals from an ROI standpoint.

3. Content creation and media

From graphic design and copywriting to music composition and video editing, AI tools are enabling creators to produce high-quality assets at a fraction of the time and cost. This trend is empowering independent creators while also being adopted by large studios, publishers, and platforms seeking scale. As tools become more capable we expect a structural shift in media economics, where the marginal cost of content creation approaches zero and monetization becomes increasingly driven by distribution and IP ownership.

4. Gaming and interactive entertainment

Gaming studios are early adopters, using AI for procedural content generation, real-time character behaviour, and personalised experiences. With high engagement and monetisation rates, gaming may be the first vertical where AI meaningfully increases lifetime value per user.

Key risks

While AI is a transformational trend, close attention to downside risks is imperative

Open-source disruption compressing software margins.
Regulatory volatility around data, algorithms, and chip exports.
Capex overextension, risking oversupply and investor fatigue.
Geopolitical fragmentation, with diverging AI ecosystems in the US, China, and Europe.
The intelligence premium: capturing the real AI upside

History reminds us that first-movers don’t always win. Xerox pioneered the graphical user interface, but it was Apple and Microsoft that captured the value. Similarly, Netscape introduced the web browser, but Google and Amazon monetised the web. The same pattern will play out in AI.

Some firms will ride the AI wave to sustained outperformance-adapting business models, creating new markets, and embedding AI into their core value proposition, others will sink under the weight of inflated expectations.

The emergence of DeepSeek highlighted just how quickly assumptions can be overturned in AI. Just months earlier, upstream infrastructure stood out as the most compelling way to access the opportunity. But DeepSeek’s ability to compress costs reshaped what constitutes scarcity overnight. When technology moves this fast, advantage belongs to the agile. Our edge is the ability to reassess and reposition as value shifts.

AI is no longer hypothetical. It’s reshaping industry dynamics, investment flows, and operating models. But this is not a tide that lifts all boats. Investors must be discerning, agile, and grounded in the underlying technological reality. In a market chasing narratives, real insight and the ability to act on it, is the true alpha.