AI software stocks are the broadest basket in the current AI investing conversation, and the Motley Fool’s “One Top AI Agent Stock” pitch sits squarely inside it. The pitch argues that the third wave of AI, after hardware and models, is software that acts, and that the way to play it is a software company that supports all of it. Here is what the AI software category actually contains and how to think about it as an investor. The details are in our DDOG Stock: What Investors Need to Know article.
What Counts as AI Software
The label covers a wide and growing range. At one end are the application builders, companies embedding AI into products their customers touch directly, from coding assistants to customer service tools. At the other end are the platform and infrastructure software companies that make AI possible and keep it running: the data tools, the model-serving platforms, and the observability products that watch it all.
The teased stock in the Fool’s pitch, Datadog, is in that infrastructure end. It is not an AI application; it is the software that monitors and secures AI systems. That is a meaningful distinction, because an infrastructure software company and an AI application company have very different growth drivers and risk profiles, even though both get called “AI software stocks.”
The Shift from Hardware to Software
The pitch’s “three waves” framing, hardware, then software, then agents, is a useful map of how the AI buildout is unfolding. The first wave concentrated value in the chipmakers, because someone had to build the compute. The second wave concentrated value in the model builders. The argument now is that the third wave shifts value toward software, because the models exist and the real work is putting them to use.
There is real logic in this. Models are becoming more of a commodity, and the differentiation, and the profit, increasingly lives in the software layer that applies them. But it is a gradual shift, not a clean break, and the hardware and model layers are not going away. They are just no longer the only game.
The Observability Angle
Within AI software, observability is the sub-sector the promo has chosen, and it is a defensible pick. Every AI system, and especially every agent, generates a stream of telemetry that someone has to collect and analyze. As the number of AI workloads grows, so does the demand for the software that watches them. Our AI agent stock teardown explains how that logic gets packaged into a specific recommendation.
The competitive reality tempers the story. Observability is a mature, competitive market, with Datadog, Dynatrace, New Relic, Splunk under Cisco, and Grafana Labs all fighting for the same spend, alongside the cloud providers’ own tools. The AI tailwind is real, but it is shared across a crowded field, not captured by one company alone.
The Valuation Discipline
The recurring theme across AI software stocks is that the growth is real and the prices are high. The best companies in the category trade at multiples that assume years of continued expansion, which means the return you actually get depends on the price you pay, not just on the quality of the business. A great AI software company at 20 times sales can still be a poor investment if the growth the price implies does not materialize.
That discipline matters more than the label. For a look at the specific stocks in the agent basket, see our AI agent stocks explainer, and for the models that sit underneath the software, our AI models piece is a useful reference.
The Bottom Line
AI software stocks are the broadest way to own the AI buildout, and the observability sub-sector the promo highlights is one of the more durable corners of that category. The label, though, is not a shortcut past the fundamentals. Sort the companies by what they actually do, understand the competition, and check the multiple before you buy.
The Margin Story
The investment case for AI software rests partly on margins. Software businesses scale profitably, the incremental cost of serving one more customer is low, and AI software that automates work can command premium pricing. But AI also introduces a margin wrinkle that older software never had: the cost of the compute underneath it. When an AI product runs, it consumes model inference, and that cost scales with usage in a way traditional software’s hosting costs do not. The best AI software companies are the ones that can pass those inference costs through to customers while still expanding their own gross margins, which is easier for infrastructure providers than for applications that compete on price. Watching gross margin trends, not just revenue growth, is the fastest way to tell which AI software businesses have real pricing power and which are just moving money around.
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