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Key Moments

  • Nvidia (NASDAQ:NVDA) is up 40% year-to-date as of 29 December, outperforming the S&P 500’s 17.8% gain.
  • The average 12-month analyst price target of $261 implies a £5,000 investment could rise to about £6,850, assuming stable exchange rates.
  • Nvidia enters 2026 with a $500bn order backlog for its Blackwell and upcoming Rubin systems and trades at 24.9 times forward earnings.

Analysts’ Outlook and Potential Returns

Despite ongoing debate about a potential “AI bubble,” Nvidia (NASDAQ:NVDA) remains on track to beat the broader market this year. As of 29 December, the stock has risen 40%, while the S&P 500 has gained 17.8%.

However, UK-based investors have faced a modest headwind. Over the past year, a stronger pound against the US dollar has slightly reduced returns when measured in sterling.

Even so, Nvidia is likely to post a positive annual return for the sixth time in seven years, barring a sharp year-end sell-off. As a result, investor focus is now shifting toward the outlook for 2026.

Precise share price forecasts remain unreliable due to the many variables involved. Nevertheless, analysts continue to publish targets. Based on estimates from 56 analysts, the average 12-month price target stands at $261, around 37% above current levels.

On that basis, a £5,000 investment made today could grow to roughly £6,850 by next Christmas. This scenario assumes the target is reached and exchange rates remain unchanged, although neither outcome is guaranteed.

MetricFigure
Nvidia share price change (YTD, as of 29 December)40%
S&P 500 return over the same period17.8%
Average 12-month analyst price target$261
Implied upside from current price37%
Estimated value of a £5,000 investment£6,850

AI Bubble Debate and Sustainability Risks

Concerns about the durability of AI spending and valuations continue to grow. For critics, these worries tend to focus on three main issues.

First, hyperscale technology firms such as Microsoft, Google, Meta, and Amazon are expected to spend more than $400bn on AI infrastructure in 2025. Meanwhile, direct AI revenues remain far lower. As a result, some investors fear overbuilding and excess capacity.

Second, funding methods have raised questions. In the past, major tech firms relied heavily on cash flow to finance expansion. Now, companies such as Meta and Oracle are issuing debt to support AI data centre investment.

Third, so-called circular financing has drawn increased scrutiny. Microsoft and Amazon have invested billions in AI startups like OpenAI and Anthropic. In turn, those firms often use the funding to buy cloud services from the same companies, which then place further chip orders with Nvidia.

Taken together, these trends help explain why some investors remain cautious about the long-term sustainability of AI-related investment.

Valuation, Backlog, and Future Architectures

History shows that major technological shifts often create speculative bubbles. The internet offers a clear example, and AI may follow a similar path.

Because of this risk, the author plans to avoid highly speculative opportunities. These include a potential OpenAI IPO in 2026 at a reported $1trn valuation, as well as AI cloud firm CoreWeave and small modular reactor startups such as Oklo.

In contrast, Nvidia’s valuation appears more measured. At 24.9 times forward earnings, the stock does not look excessively priced, especially given its visibility on future revenue. Nvidia enters 2026 with a record $500bn backlog tied to its Blackwell and forthcoming Rubin systems.

Even if new demand slowed sharply, the company would still spend considerable time fulfilling existing orders.

Looking ahead, Nvidia plans to introduce its Feynman architecture in 2028. These chips are expected to deliver major energy efficiency gains, which could strengthen demand from hyperscale customers.

Outlook for 2026 and Investment Approach

Despite strong fundamentals, concerns about an AI bubble may weigh on Nvidia’s share price in 2026. As a result, another 40% annual gain appears unlikely.

Given this outlook, the author is shifting focus toward AI application companies. In these businesses, the technology can directly improve profitability rather than relying solely on infrastructure spending.

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