Artificial intelligence investment is creating new sales opportunities for U.S. software companies, while fears of disruption are placing pressure on established business models.
Tyler Radke, Citi’s co-head of U.S. software equity research, discussed these competing forces on the television program Making Money. His focus reflects a central question for investors: Which software providers will benefit from AI spending, and which could lose customers to newer tools?
AI Buildout Creates Two Paths
The current AI buildout reaches several parts of the technology market. Companies need computing capacity, data systems, security tools and applications that can turn AI models into useful products.
Software providers may gain if customers pay for AI features or adopt new services. Established vendors often have large customer bases, valuable data and existing sales channels. Those advantages can help them distribute AI tools quickly.
However, adding AI does not guarantee higher profits. Developing and operating these products can increase computing expenses. Vendors must also show that new features produce measurable value before customers accept higher prices.
Radke’s discussion centered on the tension between the “artificial intelligence buildout” and “disruption fears” across the software sector.
The split makes company-level analysis more important. Investors must assess whether AI produces fresh revenue, protects an existing product or simply raises costs.
Disruption Fears Pressure Valuations
Software companies have long benefited from subscription revenue and high customer retention. AI could test both strengths by making some development tasks faster and lowering the cost of creating competing products.
New AI tools may also change how workers search for information, write code and complete routine office tasks. If customers need fewer separate applications, some vendors could face weaker demand.
The risks are not uniform. Providers with specialized data, deep links to customer systems or strict security controls may be harder to replace. Products serving regulated industries may also face slower change because buyers require careful testing.
Key issues for the sector include:
- Whether customers will pay extra for AI features
- How quickly AI services can generate lasting revenue
- Whether computing costs will reduce profit margins
- Which established applications face replacement risk
Investors Seek Proof From Results
AI announcements can influence market expectations, but financial results provide a firmer test. Investors will watch contract growth, customer adoption and management forecasts for evidence that demand is durable.
They will also examine spending. A company may report strong interest in AI while absorbing large development and infrastructure costs. That gap can delay profits even when product use rises.
Supporters of established software companies can point to trusted customer relationships and years of stored business data. Those assets may help vendors provide useful AI services within products that employees already use.
Skeptics see a threat from simpler applications and automated software development. They argue that AI could reduce barriers for new competitors, forcing older vendors to spend more while defending prices.
A Sector Moving From Hype to Execution
Radke’s focus on both investment and disruption shows why the software outlook cannot be reduced to a single AI trade. The same technology can expand demand for one provider while weakening another.
The next stage will depend on execution. Companies must prove that AI features solve real problems, earn revenue and maintain acceptable costs. Investors, meanwhile, will need to separate broad enthusiasm from verified business gains.
Future earnings reports should offer clearer signals about adoption, pricing and margins. Until then, the sector is likely to remain divided between optimism over new spending and concern that AI may rewrite long-standing software economics.
