AI Investment Could Rebound as Global Tech Markets Recover
Artificial intelligence remains one of the biggest forces shaping global technology markets, but recent volatility has made investors more cautious about how much money should continue flowing into the sector.
Now, analysts at J.P. Morgan believe the recent decline in AI-related stocks may have created conditions for renewed investor interest, particularly in semiconductor companies.
In a September 28, 2026 note, J.P. Morgan analysts said lower valuations, improved investor positioning and continued strength in AI spending could help revive interest in the global AI trade.
The development suggests that the AI investment story may be entering a new phase rather than coming to an end.
Why Did AI Stocks Pull Back?
AI-related companies enjoyed strong investor interest during the earlier part of 2026 as businesses continued spending heavily on processors, cloud infrastructure, data centers and artificial intelligence software.
However, enthusiasm weakened as investors began asking a more difficult question: how quickly will companies generate returns from the enormous amount of money being spent on AI?
Concerns about capital expenditure, competition and the speed of AI monetization caused pressure across parts of the technology sector.
AI-linked stocks also fell sharply earlier in September amid growing discussion about the risks and pace of artificial intelligence development.
Market corrections like this can change investor behavior.
When valuations become extremely high, even strong companies can appear expensive. A market decline can reduce those valuations and potentially make certain companies more attractive again.
J.P. Morgan Sees Opportunities in AI
J.P. Morgan’s latest analysis suggests that the long-term fundamentals behind artificial intelligence remain relatively strong despite recent market weakness.
The bank noted that valuations across many AI-related areas have fallen while investor positioning has become less crowded.
It also expects capital spending on AI infrastructure to remain significant.
Continued earnings growth and evidence that companies are beginning to generate revenue from artificial intelligence products could encourage investors to return to the sector.
However, the outlook is not equally positive for every technology category.
J.P. Morgan appears more constructive on semiconductor companies while remaining more cautious about software businesses facing increasing AI-driven competition.
Semiconductor Stocks Could Attract More AI Investment
Semiconductors are at the center of the artificial intelligence boom.
Large AI models require enormous amounts of computing power. That computing power depends on advanced processors, memory chips, networking equipment and other specialized hardware.
This creates demand across several areas, including:
- AI accelerators
- Graphics processors
- Memory chips
- Data-center networking
- Optical communication systems
- Power management equipment
- Cooling infrastructure
J.P. Morgan maintained a positive view on semiconductor stocks, pointing to healthy fundamentals and tight supply conditions that could continue for several years.
The performance gap between semiconductor and software stocks has already become significant.
According to Reuters, the MSCI World Semiconductors and Semiconductor Equipment Index had gained around 48% during 2026 as of September 28, while the MSCI World Software and Services Index had risen only about 1.3%.
That difference shows how investors are increasingly separating different parts of the AI market instead of treating every technology company as an equal beneficiary.
AI Hardware Is Becoming a Major Investment Theme
The growing demand for physical infrastructure is also attracting venture capital.
Seligman Ventures announced on September 28 that it had doubled its deployable capital from $500 million to $1 billion, less than a year after launching.
The investment operation has already put more than $300 million into 14 businesses across AI hardware, connectivity and cybersecurity.
The firm’s strategy demonstrates how investors are looking beyond AI chatbots and software.
Building large-scale artificial intelligence systems requires a massive physical ecosystem.
That includes processors, networking equipment, electricity, cooling systems, storage facilities and highly specialized data centers.
These infrastructure requirements could create investment opportunities even if growth in certain consumer AI products slows.
Semiconductor Startups Are Receiving More Capital
Private investment in semiconductor businesses is also increasing.
Venture investment in U.S. and Canadian startups reached a record $392 billion during the first half of 2026, according to figures cited by Reuters.
Around $10.7 billion of that total went into semiconductor startups.
This trend reflects a broader shift in Silicon Valley.
For many years, software companies attracted enormous amounts of venture capital because they could scale quickly without building expensive physical infrastructure.
Artificial intelligence is changing that model.
Modern AI systems require billions of dollars of chips, servers and data-center equipment.
As a result, hardware companies are becoming more important to technology investors.
Data Centers Could Remain a Major Growth Area
AI data centers have become another important area of investment.
Training and operating advanced AI models requires huge computing facilities filled with high-performance processors.
These facilities also require enormous amounts of electricity and advanced cooling technology.
Investors are therefore increasingly looking at companies involved in:
- Data-center construction
- AI processors
- Networking
- Power generation
- Cooling technology
- Fiber-optic communication
- Cybersecurity
- Cloud computing infrastructure
Seligman Ventures said it plans to continue searching for opportunities involving accelerators, networking, power, cooling and cybersecurity.
The trend suggests that the AI boom is expanding into industries that support artificial intelligence rather than only the companies developing AI models themselves.
Nvidia Signals Confidence in Long-Term AI Demand
Nvidia remains one of the companies most closely associated with the AI investment boom.
On September 28, Nvidia increased its share-repurchase authorization by a record $150 billion, bringing its remaining buyback capacity to approximately $235 billion through fiscal 2028.
The move comes as investors debate whether the enormous spending on AI infrastructure can continue at its current pace.
Nvidia’s processors remain widely used for training and operating artificial intelligence systems, but competition in the AI-chip market is increasing.
Companies including AMD, Intel and several specialized chip developers are competing for a larger share of the market.
Nvidia had $22.44 billion in cash and cash equivalents at the end of its July quarter, according to Reuters.
The company has also projected strong revenue growth, supporting its view that demand for AI computing infrastructure remains substantial.
Why Investors Are Becoming More Selective
The next stage of AI investment may look different from the earlier boom.
During the first wave of enthusiasm, companies could sometimes benefit simply by announcing an artificial intelligence strategy.
Investors are now paying closer attention to fundamentals.
Important questions include:
- Is the company generating revenue from AI?
- Can its AI products produce sustainable profits?
- How much money must it spend on infrastructure?
- Does it have a competitive advantage?
- Can competitors offer similar technology?
- Is customer demand strong enough to justify continued expansion?
This means AI investing could become increasingly selective.
Companies with strong revenue growth and important infrastructure products may attract capital, while businesses without clear monetization strategies could face greater scrutiny.
AI Monetization Is Becoming More Important
One of the most important issues for the industry is monetization.
Technology companies have already committed enormous amounts of capital to developing AI models and infrastructure.
Investors increasingly want evidence that these investments can create meaningful profits.
Possible revenue sources include:
- AI subscriptions
- Enterprise AI software
- Cloud AI services
- Advertising tools
- AI agents
- Automated customer service
- Coding assistants
- AI cybersecurity platforms
- Data-center services
If companies demonstrate stronger AI revenue, investor confidence could improve.
J.P. Morgan specifically pointed to growing evidence of AI monetization as one factor that could support renewed interest in the sector.
Risks Still Remain for AI Investment
Despite the more constructive outlook, AI investing still carries substantial risks.
Artificial intelligence companies face intense competition, rapidly changing technology and high infrastructure costs.
Several important risks remain.
High Capital Spending
Building AI infrastructure requires enormous investment.
If revenue growth fails to match spending, companies may struggle to justify current valuations.
Increasing Competition
AI competition is becoming stronger across both hardware and software.
Companies may need to reduce prices or spend more on research to maintain their market positions.
Technology Changes Quickly
Today’s leading technology may become less competitive within only a few years.
That makes long-term investment decisions difficult.
Economic Conditions
Higher interest rates, inflation or slower economic growth could reduce corporate technology spending.
Valuation Risk
Even a successful company can produce disappointing investment returns if investors pay an excessively high price for its shares.
These factors mean that renewed interest does not guarantee that every AI-related stock will rise.
Software Companies Face a More Complicated Outlook
While semiconductor companies may benefit from infrastructure spending, software businesses face a different challenge.
Artificial intelligence allows companies to develop new applications much more quickly.
That could increase competition and put pressure on traditional software providers.
AI agents may also automate tasks previously handled through separate software applications.
J.P. Morgan therefore remains more cautious about the long-term software outlook while maintaining a more constructive position on semiconductor stocks.
The difference highlights an important trend: the AI boom could create both winners and losers within the technology industry.
What Could Drive the Next AI Investment Rally?
Several developments could help strengthen investor confidence.
Strong corporate earnings would be one major factor.
Investors will also be watching whether technology companies continue increasing spending on AI data centers and processors.
Other possible catalysts include:
- Strong semiconductor demand
- New AI products
- Rising enterprise adoption
- Higher AI-generated revenue
- Falling infrastructure costs
- Improved chip supply
- New data-center projects
- Greater adoption of autonomous AI agents
Evidence that businesses are earning real money from artificial intelligence could be particularly important.
The market may increasingly reward companies that can convert AI technology into sustainable revenue.
Could AI Investment Keep Growing Into 2027?
J.P. Morgan believes semiconductor fundamentals remain supportive, with pricing growth expected into 2027 and tight supply-demand conditions potentially continuing into 2028.
That outlook indicates that demand for AI infrastructure may remain strong even if stock prices continue experiencing periods of volatility.
However, the industry is unlikely to move upward in a straight line.
Large market corrections are common in rapidly growing technology sectors.
Investors may therefore continue switching between optimism and caution as new earnings reports, AI products and spending announcements emerge.
Final Thoughts
The recent market pullback does not necessarily signal the end of the global AI investment boom.
Instead, it may represent a transition toward a more selective stage in which investors focus increasingly on earnings, valuations and real-world demand.
Semiconductor companies, AI hardware providers and data-center infrastructure businesses could remain important areas to watch as companies continue expanding artificial intelligence systems.
At the same time, concerns surrounding high spending, competition and uncertain returns have not disappeared.
The next phase of the AI market will likely depend less on excitement alone and more on whether companies can prove that artificial intelligence can produce sustainable revenue and profits.
For global technology markets, 2026 may ultimately be remembered not only as a year of rapid AI expansion, but also as the year investors began demanding clearer evidence that the enormous AI spending cycle could deliver lasting financial returns.
