Artificial intelligence investing has so far been dominated by one part of the technology ecosystem.
Semiconductors.
GPUs.
Data centers.
Power infrastructure.
And, of course, Nvidia.
But as the AI investment cycle develops, I believe an increasingly important question is emerging:
Could software become the second major wave of the artificial intelligence boom?
That does not mean the semiconductor story is over.
It means the economic value created by AI may gradually begin moving further up the technology stack, from the infrastructure that makes AI possible toward the software companies that actually integrate AI into business processes, decision-making and everyday workflows.
That is why I am watching the software sector closely.
From AI Infrastructure to AI Monetization
The first stage of the AI investment cycle has largely been about building capacity.
Companies have invested enormous amounts of capital in:
GPUs
data centers
cloud infrastructure
networking equipment
energy capacity
AI computing platforms
This was necessary.
Before companies can monetize artificial intelligence at scale, the infrastructure needs to exist.
But eventually investors need to ask a different question:
Where will the economic returns on all this investment appear?
Software could become one of the most important answers.
If artificial intelligence genuinely improves productivity, automates business processes, strengthens cybersecurity, supports decision-making and changes how companies operate, much of that value may ultimately be captured through software.
That is the thesis worth examining.
Why I Am Watching IGV
One way I follow this theme is through the iShares Expanded Tech-Software Sector ETF, IGV.
IGV provides exposure to a broad range of software companies and therefore offers a useful way to evaluate the software sector as a whole rather than focusing exclusively on a single stock.
The companies connected to this theme include businesses such as:
Palantir
Microsoft
Oracle
cybersecurity companies
enterprise software providers
cloud and data-platform businesses
These companies operate in very different markets, but several characteristics can make leading software businesses particularly attractive.
Recurring revenues can create visibility.
High switching costs can strengthen customer retention.
Strong ecosystems can create competitive moats.
And AI may allow some of these companies to expand their products, increase pricing power or create entirely new revenue streams.
But there is an important distinction.
A strong business is not automatically a good investment at every price.
Two Questions Investors Should Separate
When evaluating software stocks, I try to separate two very different questions:
Do I like the sector?
and
Do I like the current price?
These questions are often confused.
Artificial intelligence may be a powerful long-term structural theme.
Software may benefit significantly.
A company may have an excellent competitive position.
None of these facts automatically tells us whether the stock is attractively valued today.
That distinction becomes especially important when expectations are already extremely high.
If a company's valuation assumes years of exceptional growth, even strong operating performance may not be enough to produce attractive investment returns.
This is why I do not want to chase momentum simply because I believe in the long-term AI story.
The price still matters.
Competitive Moats Matter
In software, I pay particular attention to competitive moats.
One of the strongest can be switching costs.
Once a company becomes deeply integrated into a customer's workflows, databases, security architecture or enterprise systems, replacing that software can become expensive and operationally risky.
That can create durable customer relationships.
Artificial intelligence may strengthen this effect.
If AI capabilities become embedded inside core enterprise platforms, customers may increasingly depend not only on the underlying software but also on the data, workflows and AI models connected to that ecosystem.
This could deepen the competitive advantages of some established software companies.
But it could also create disruption.
AI may lower barriers to entry in certain software categories and allow new competitors to build products faster and cheaper.
So the impact will not be uniform.
Some software companies may capture enormous value from AI.
Others may find their existing business models under pressure.
Palantir, Microsoft, Oracle and Cybersecurity
Several companies illustrate the different ways software businesses can participate in the AI economy.
Palantir is closely associated with enterprise AI, data integration and decision-making platforms.
The investment question, however, is not simply whether Palantir has significant AI exposure.
It is whether future growth, margins and cash generation can justify the expectations already embedded in the valuation.
Microsoft approaches the AI opportunity from a different position.
It combines cloud infrastructure, enterprise software, productivity applications and AI products inside one of the world's largest technology ecosystems.
That creates multiple channels through which AI could potentially be monetized.
Oracle is also relevant because enterprise databases, cloud infrastructure and business applications sit close to the data companies need to deploy AI effectively.
Then there is cybersecurity.
As AI systems become more autonomous and organizations deploy increasing numbers of AI agents, digital identities and automated processes, cybersecurity requirements may become substantially more complex.
That could create another important long-term opportunity within the broader software landscape.
The Technical Picture Matters Too
Fundamental analysis is only part of my framework.
I also examine IGV from a technical-analysis perspective.
I look at:
major resistance areas
previous correction zones
trend structure
momentum
support levels
historical drawdowns
potential breakout or rejection areas
This helps create a framework rather than a prediction.
I prefer to think in scenarios.
Bullish Scenario
In a bullish scenario, software leadership broadens, earnings expectations improve and IGV successfully clears important resistance levels.
That could suggest that investors are beginning to price a broader AI monetization cycle beyond semiconductors.
Base-Case Scenario
The sector may also spend time consolidating.
After a strong technology cycle, sideways movement can allow earnings and fundamentals to catch up with valuations.
This would not necessarily invalidate the long-term thesis.
It could simply mean the market needs time to absorb previous gains.
Bearish Scenario
The risk is that expectations have moved significantly ahead of fundamentals.
If earnings revisions weaken, AI monetization takes longer than expected or valuation multiples contract, software stocks could experience meaningful corrections even if the long-term AI theme remains intact.
That distinction is critical.
A secular investment theme can remain valid while individual stocks or sectors experience substantial drawdowns.
What I Am Watching
Rather than trying to predict the exact next move, I prefer to monitor a combination of factors.
These include:
valuation
technical levels
earnings revisions
revenue growth
margins
free cash flow
AI-related capital expenditure
customer adoption
actual returns generated from AI investment
The last point may become increasingly important.
Companies are spending enormous amounts of money on artificial intelligence.
Eventually investors will demand evidence that this spending creates measurable economic returns.
The companies that can demonstrate those returns may be in a stronger position to capture the next stage of the AI investment cycle.
The Real Question for AI Investors
The important question is no longer whether artificial intelligence will transform software.
It almost certainly will change how software is developed, distributed and used.
The more difficult question is:
Which software companies will capture the economic value created by AI, and at what price should investors consider owning them?
That second part matters just as much as the first.
A great structural theme can still become a poor investment when purchased at the wrong valuation.
That is why my approach remains disciplined.
I want to understand the long-term theme.
I want to identify companies with genuine competitive advantages.
But I also want valuation, earnings, cash flow and market structure to support the investment case.
AI may have started with chips and infrastructure.
The next major chapter could increasingly be about software.
Whether the market is already pricing that opportunity correctly is a different question.
Disclaimer
This content is for educational and informational purposes only and does not constitute investment advice, financial advice, or a recommendation to buy or sell any security.
Investors should conduct their own research and consider their individual financial circumstances, investment objectives and risk tolerance before making investment decisions.
