Fast reading
- The tech sector generally performed well in Q1 earnings season.
- The sector remains highly innovative, and recent developments could accelerate AI adoption and monetisation.
- The regulatory backdrop has improved.
The tech sector got off to a tricky start in 2025. Concerns around US technological dominance over China, uncertainties surrounding data centre capex growth and possible overspending by tech companies, and the potential impact of trade restrictions all contributed to a rocky beginning in Q1.
Despite this, we remain structurally bullish on AI over the longer term and are seeing signs of an improving outlook. Below we’ve outlined three key reasons we’re still excited about the sector.
Reason #1: Strong earnings for mega-cap tech
After a moderation in AI sentiment over the first few months of 2025, fundamentals somewhat reasserted over earnings.
Investors approached Q1 earnings season with caution, but mega-cap tech stocks delivered strong results, reinforcing previous excitement around AI.
Microsoft’s results in particular propelled enthusiasm (particularly on Azure) as it meaningfully beat expectations with growth reaccelerating.
We see an opportunity in this broader backdrop as many tech stocks are still trading at a material discount versus recent history, even in spite of their recovery following the recent sell-off.
Figure 1: Q1 earnings season – surprises and upside
- On March 31, the estimated earnings growth rate for the ‘Magnificent Seven’ companies for Q1 was 16%. Overall 86% (6 out of 7) of the ‘Magnificent Seven’ companies reported a positive earnings per share (EPS) surprise, compared to 78% for all S&P 500 companies.
- In aggregate, earnings reported by the ‘Magnificent Seven’ companies exceeded estimates by 14.9%, compared to 8.2% for all S&P 500 companies.
- ‘Magnificent Seven’ companies reported actual earnings growth of 27.7% for the first quarter.
Source: Factset, as at 6 June 2025.
Figure 2: ‘Mag 7’ Q1 earnings versus the rest
Reason #2: The potential of agentic AI to accelerate AI monetisation and adoption
We recently attended the JPM TMT conference in Boston, one of the world’s largest tech conferences. The dominant theme of the event was agentic AI.
Agentic AI differs from previous AI advancements in that it can independently assess challenges and determine the best course of action without human input.
Specific use-cases include software engineering (code generation), customer support, and digital marketing/content generation.
At the conference, management teams spoke positively about:
The productivity enhancements and value-added benefits of agentic AI for both enterprises and consumers.
- Their ability to monetise these advancements.
- Their expectations that adoption will occur sooner rather than later.
For software businesses: The focus is on exploring ways of integrating AI agents to replace or automate manual workflows.
For enterprise customers: We expect to see accelerated adoption of agentic AI since return on investment (ROI) is proving to be greater than expected, and the payback period significantly shorter.
For investors: There is a renewed structural optimism on AI, driven by hopes of greater adoption and effective monetisation of AI agents.
Reason #3: A more benign regulatory backdrop for AI and semiconductors
The Trump administration recently rescinded the AI Diffusion Rule, which had imposed strict export controls on AI semiconductors (see Figure 3). This rule was seen as a barrier to innovation and global competitiveness for American tech companies.
President Trump also announced a deal with the UAE and Saudi Arabia which includes significant investments in AI and semiconductor technology in the US. This incorporates a preliminary agreement to allow the UAE to import 500,000 of Nvidia’s most advanced AI chips per year.
Implications:
- The trade restriction’s removal could serve as a tailwind for semiconductors going forward (given it was previously a big overhang for semis, like Nvidia).
- The Saudi Arabia deal could help to relieve some of the previous concerns around US Tech’s global competitiveness and boost investor sentiment on AI more broadly.
Figure 3: The AI Diffusion Rule
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