For nearly two years, artificial intelligence (AI) functioned as a single, powerful market story. If a company was even loosely linked to AI, investors rewarded it. Valuations rose together, stocks moved
in sync, and a small group of US technology giants came to dominate global equity markets. That phase is now ending.
Narender Agarwal, CEO, Wealth1, said that the AI-led rally of the past two years initially lifted the entire group of the so-called ‘Magnificent Seven’ as investors treated them as a single proxy for artificial intelligence-driven growth. “The launch of generative AI in late 2022 created a powerful narrative that every large US technology platform would benefit. Between 2023 and early 2024, the Magnificent Seven accounted for over 60% of the S&P 500’s total returns, driven by passive ETF flows and momentum investing, which pushed these stocks higher almost in unison,” he says.
Only two of the so-called ‘Magnificent Seven’ — Alphabet and Nvidia — managed to outperform the broader S&P 500 in 2025. The rest lagged behind, despite continuing to invest billions into AI infrastructure, data centres, and model development.
This divergence is not a market blip. It signals a structural change in how investors are evaluating AI. The question has moved from “Who is exposed to AI?” to something far tougher: “Who is actually making money from it?”
For Indian investors, who are tracking US tech stocks, global funds, or India’s own IT majors, does this shift matter? Let’s find out.
Who Are the ‘Magnificent Seven’, And How Did They Grow?
The term ‘Magnificent Seven’ refers to seven US mega-cap technology companies that came to dominate global stock-market returns over the past decade: Apple, Microsoft, Alphabet, Amazon, Nvidia, Meta Platforms, and Tesla. The term was coined by Bank of America analyst Michael Hartnett in 2023 when commenting on the seven companies.
When the AI boom took off in late 2022 and 2023, these seven stocks surged almost in unison. Investors treated them as a single trade — a bet on the future of artificial intelligence reshaping productivity, consumer behaviour, and corporate profits.
According to Investopedia, Nvidia has led the pack with a massive return of over 1,300% in the past five years. Alphabet had the next-highest showing, returning almost 260% to investors. Most others had five-year returns of about 130%, except for Amazon, which returned about 47%.
Though that logic worked for a while, but AI excitement, so to say, overlooked traditional metrics such as earnings visibility, margins, or near-term costs.
Why AI Lifted All Boats In The Beginning
In the early phase of the AI boom, markets behaved thematically rather than analytically. Generative AI arrived with enormous promise. Tools like ChatGPT sparked expectations of productivity leaps, automation at scale, and entirely new business models. Investors feared missing out on what was framed as the next internet-scale revolution.
This fear of missing out, combined with passive investing and index concentration, created a powerful feedback loop. As money flowed into AI-linked stocks, their index weight rose, pulling in even more capital.
At this stage, being “AI-adjacent” was enough. It did not matter whether a company was selling chips, running cloud platforms, experimenting with chatbots, or simply talking up AI in earnings calls. The market rewarded participation in the narrative.
The Turning Point: From AI Promise To AI Proof
By late 2024 and into 2025, investors began asking harder questions. AI spending was exploding, but profits were not rising at the same pace. Costs were mounting, data-centre investments were ballooning, and power consumption was becoming a constraint.
Most importantly, markets began demanding proof of AI monetisation. This is where the divergence began.
Nvidia stood out because its business model is directly tied to AI demand. It transitioned from being a component supplier to an indispensable, full-stack AI infrastructure provider, with nearly 90% of its revenue directly tied to the exponential demand for AI data centre hardware.
“In 2025, only Nvidia and Alphabet managed to outperform the S&P 500, while companies such as Apple, Tesla and Meta have lagged broader indices as investors reassessed growth visibility, capital intensity and return on invested capital,” Agarwal notes. Nvidia, for instance, saw revenues more than double at the peak of the AI infrastructure build-out, supported by strong pricing power in data-centre GPUs, while several other megacaps are still absorbing heavy AI capex that is weighing on free cash flows.
Why Some Tech Giants Are Falling Behind
For companies like Apple, Meta, Amazon, and Tesla, AI remains strategically important, but monetisation is either indirect, delayed, or uncertain. These companies have been investing billions of dollars in AI, but high-margin revenue has been difficult to achieve to excessive capital expenditures, and high operational costs.
Apple’s AI ambitions hinge on consumer hardware and ecosystem lock-in, not immediate revenue expansion. Apple has been slower to release generative AI features compared to other companies. It relies on a strategy of “using AI models, not selling them”. Apple recently partnered with Alphabet (Google) to use Gemini models for its “Apple Intelligence” features. This suggests that Apple relies on others’ technology rather than monetizing its own in-house AI, potentially missing out on higher margins.
Meta’s AI investments support engagement and ad targeting, but returns are harder to isolate against massive infrastructure costs. Amazon continues to pour money into AI-driven cloud services, but margins face pressure from competition and capital expenditure.
Tesla, meanwhile, remains a special case. Its AI narrative is tied to autonomy and robotics — long-term bets that excite investors but generate little near-term cash flow. Tesla has invested over $20 billion in its Full Self-Driving (FSD) software, but in Q3 2025, only 12% of its fleet paid for FSD, and revenue from it was lower than in 2024.
Markets are no longer willing to price all of these futures equally.
What has changed is not belief in AI’s importance, but patience. Investors want evidence that AI spending improves margins, pricing power, or earnings growth, not just long-term potential.
What ‘AI Monetisation’ Really Means Now
Agarwal explains that AI monetisation has become the market’s key filter. “Monetisation means converting AI capabilities into predictable revenues, margins and cash flows through enterprise adoption, cloud pricing, and productivity gains. Narrative-driven enthusiasm alone cannot justify premium valuations once the cycle matures.”
This includes higher revenue per customer, improved operating margins, reduced labour costs, or entirely new revenue streams. It also means showing that AI does not permanently inflate costs or compress profitability.
In this phase, execution matters more than vision.
That is why the Magnificent Seven are no longer moving together. AI has shifted from a story-driven trade to a fundamentals-driven one.
Does This Impact Indian Investors?
Indian investors have been riding a parallel AI narrative, particularly in IT services, digital transformation, and platform-based tech companies.
Agarwal also points out that India largely missed the first leg of the global AI wealth creation cycle. “The initial AI boom was dominated by deep-tech platforms, semiconductor leaders and hyperscalers — areas where India has limited presence. Indian IT firms are predominantly services-led and entered the AI cycle later, focusing on integration and deployment rather than core IP or platforms. As a result, the disproportionate value creation accrued to US megacaps, while Indian tech stocks saw only incremental benefits.”
Thus, the experience offers a cautionary signal.
AI enthusiasm alone does not sustain valuations. Investors will increasingly ask Indian tech firms the same questions they are asking US giants: Is AI increasing deal sizes? Is it improving margins? Is it reducing manpower intensity, or simply adding new costs?
For India’s IT sector, this is especially sensitive. AI has the potential to disrupt the traditional labour-heavy services model. If AI automates tasks faster than companies can monetise higher-value work, margins could come under pressure rather than improve.
Why Stock Selection Will Matter More Than AI
One of the biggest lessons from the Magnificent Seven split is that sector-level optimism is no longer enough. Investors are no longer buying “AI” as a category. They are buying companies that can turn AI into profits.
The takeaway for Indian markets, Agarwal says, is “forward-looking”. “India has not missed AI altogether, but the opportunity now lies in execution — winning large enterprise contracts, embedding AI into core business processes, and demonstrating tangible revenue and margin expansion. Investors are no longer buying AI as a buzzword; they are backing proof of delivery.”
What Comes Next
As the AI trade becomes more selective, capital is beginning to spread out. Some investors are rotating into sectors like healthcare, where AI applications can improve diagnostics, drug discovery, and operational efficiency with clearer revenue paths. Others are doubling down on semiconductor infrastructure, betting that demand for compute will remain strong regardless of which applications win.
This redistribution of capital signals maturity. AI is no longer speculative novelty; it is infrastructure. And infrastructure rewards scale, efficiency, and monetisation, not just ambition.
For Indian tech firms, the message is clear. AI strategy must move beyond capability-building to commercial outcomes.
Clients will pay for AI if it delivers cost savings, productivity gains, or competitive advantage. They will not pay simply because AI is fashionable.
