Leaders of the new technology cycle are already using AI to drive both innovation and profits.
If there has been one burning question on investors’ lips this earnings season, it has been when AI will start to generate meaningful revenues for companies. The $210 billion in capital expenditure (capex) slated for 2024 from just four of AI infrastructure’s biggest spenders (Alphabet, Meta, Amazon and Microsoft) justifies the question. These companies, plus many more, are investing heavily in AI and plan to increase their investments further still in 2025. Why? Sundar Pichai, CEO of Alphabet, summed it up perfectly on the company’s second quarter earnings call: “The risk of under-investing is dramatically greater than the risk of over-investing for us here.”
Two key points are packed into this sentence, the sentiment of which was echoed across Silicon Valley throughout this earnings season. The first is that the expected gains from AI are worth pursuing, and this is based on evidence from early AI use cases which are already driving compelling returns on investment. The second is that this investment risk can be managed through sufficient cashflow generation. Microsoft, for example, has spent a whopping $44.47 billion on capex over the past 12 months, up just shy of 60% year-on-year, yet this has left 63% of operating cashflow untouched. Margins actually expanded last quarter, despite the heavy investment, owing to internal efficiency realisation. Although management has warned of a 1% operating profit margin contraction in 2025 as the company builds out its AI infrastructure, this is a small cost to pay for being a frontrunner in the AI race.
So where is this elusive return on investment (ROI)? You will likely not find it in the financial statements of the majority of companies touting their AI credentials on earnings calls. AI has certainly become a buzzword, but that does not mean that select pioneers in AI deployment are not witnessing outsized gains. OpenAI spent c.$500 million to train ChatGPT4, and the company is already generating close to $4 billion annualised revenues from this model. ChatGPT5 and 6 will improve on this further. Although privately held, OpenAI’s ability to monetise AI in this fashion indicates the size of the prize for those companies adopting AI company-wide.
The AI pioneers
Starting with the big and obvious, Meta stands out among the tech giants. The company’s AI investment in content recommendation and AI ad-tools are already driving tangible revenues: over 50% of the content we see on Instagram is now recommended by AI, while advertisers using Meta’s AI advantage+ tools are seeing a 22% higher return on ad spend. These factors helped power advertising revenues 23% higher year-on-year in Q2. This is the ROI that everyone is looking for, there are just very few companies today capable of deploying AI at scale and generating meaningful revenues.
ServiceNow, the leading enterprise software company for workflow automation, is also in this select minority of companies deploying AI at scale today. After using Now Assist, the company’s Generative AI assistant, internally for 90 days, the company realised over $10 million annualised enterprise savings. Customers using the Gen-AI tool are seeing similarly dramatic productivity gains: BT Group has used Now Assist to cut the time it takes for its agents to write case summaries and review cases by 55%. These savings add up across an organisation as machines are trained to do the low-value work so that employees can focus on the knowledge work. Now Assist has become the fastest growing product in the company’s history and customers are paying up – Gen-AI contracts are being signed north of $5 billion, with this revenue momentum underscoring management’s raised financial guidance for the year.
Beyond the megacaps and beyond technology
When it comes to smaller companies, or companies beyond the technology sector, meaningful AI-related revenues are more elusive but can be found. Take Palantir, whose AI-powered platform, AIP, enhances data analysis and operational efficiency across corporations and defence organisations. By integrating advanced AI with existing data systems, AIP allows organisations to analyse large datasets in real-time, generate actionable insights, and streamline decision-making processes, leading to significant efficiency gains and cost savings. Lowe’s, for example, used AIP to go from “no AI” to using AI for over 1000 customer service agents in the space of four months, resulting in a 75% reduction in overdue tasks, while General Mills has used AIP to drive $14 million in cost savings annually. Demand for the company’s AI solutions is not only propelling Palantir’s topline growth (+30% year-on-year in Q2) but has turned the business profitable over the past seven quarters. At the heart of this achievement is the immediate ROI that Palantir’s customers experience.
Beyond the technology sector we are seeing inroads, but it is early days. Recursion, a tech-bio company which is using AI to accelerate the process of drug discovery has demonstrated the ability to validate drug leads in c. 1/3 the time of the industry and get drugs through pre-clinical development at 60% lower cost. Meanwhile L’oreal is using AI to deliver hyper-personalised beauty service: its generative AI beauty assistant is increasing customer conversion by 60% versus in-store advisers while more than 100 million users have now tried AI- enhanced virtual make-up. We expect to see the diffusion of AI across all industries – AI is a general purpose technology with the potential to transform every sector – but early adopters are already benefiting from enhanced productivity and product innovation, which will likely lead to share gains as these benefits compound over time.
The importance of a world class compute infrastructure
One thing that all these companies have in common is a world class compute infrastructure. CEOs of the world's largest technology firms, from Microsoft to Amazon to Google, all sang from the same hymn sheet on their recent earnings calls in stating that they were capacity constrained for AI compute. Simply put, demand for their AI offerings exceeds their ability to supply. Meta, for instance, has invested heavily in its AI infrastructure, training its most recent large-language-model, Llama 3, on two GPU clusters each comprising 24,000 Nvidia H100s. Llama 4 will be trained on 10x this compute. This is the infrastructure that is necessary to deliver the next step up in model performance, which will enable further revenue opportunities.
Not only do we believe this investment is justified, we believe it is rational capital deployment. For every $1 invested by cloud service providers in Nvidia-accelerated computing infrastructure (the GPUs and related kit required to build and train AI), this will translate to an estimated $5 over a four-year time horizon, as things stand today. This is because AI solutions are bringing customers to the cloud, driving revenue growth for these providers, which will translate to returns on their infrastructure investments. For companies serving AI models (essentially providing the kit for AI inference), the return on investment is even more significant: $1 invested today has the potential to generate $7 in revenue over a four-year time horizon. It is this compelling ROI on AI infrastructure that is leading companies to accelerate their investments in AI infrastructure, despite recent investor consternation.
A disruption – and an opportunity
AI-generated revenues will not be enjoyed by all. AI is a platform technology shift that, like all superseding platform transitions, will entail disruption as well as opportunity. We are already seeing early indicators of shifting competitive dynamics between early adopters and laggards who risk being left behind. It will also take time for most companies to establish a critical mass in AI, for the principal reason that it takes time to build the infrastructure. This is why we believe that the value for investors today lies predominantly in the AI infrastructure layer of the new technology stack; over time the baton will get passed to the AI application layer. We expect this baton to be passed on more quickly than in previous technology shifts, because the gains have potential to be so much larger. With the cloud transition it took Microsoft seven years to reach $3.5 billion annualised revenues; with generative AI the company achieved this milestone in just 18 months.
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