The Rise of AI in Software Stocks: Challenges and Opportunities
In recent times, chatGPT has caused a surge in the beneficial impacts of artificial intelligence (AI) in software stocks. However, the non-existence of a pricing strategy for generative AI poses a challenge, with the theory being that if the new services are effective, customers would be willing to pay using any option. Palantir and C3.ai have doubled their stock prices in recent times, manifesting the hope and uncertainty inherent in their advancements in the provision of platforms that use generative AI. Competition from the likes of Microsoft and Google, and the risk of adding AI bells and whistles to existing products without assessing the real benefits of such features, remain significant threats.
Major software companies, including Adobe and ServiceNow, are quickly becoming dominant players in the industry and have recorded significant stock price increases over the year, providing a sense of optimism to the industry. However, the difficulty in demonstrating real value and distinctiveness in terms of generating AI has caused investors to remain hesitant. Implementation of billing by usage has been identified as a viable alternative to attract customers and attract revenue, although this could lead to uncertainties in profit margins. For new AI features introduced by most software companies, there is a cautious start-point, as they navigate which ones would be accepted by customers and how best to earn by them.
The Difficulty of Moving to Usage-based Models
Following an announcement of its Q1 results, GitLab’s shares increased by a third after CEO Sid Sijbrandij outlined the firm’s plan to implement generative AI into every aspect of its service. Despite the favorable response from Wall Street, there remains the question of whether customers would need it less and thus pay for fewer seats if AI made engineers more productive. The company’s chief argued that if AI were to reduce the cost of producing software, more would be created, and profits would rise and suggested that the usage-based model be incorporated to enhance revenue.
In contrast, several firms find moving to usage-based model difficult, with C3 attributing declining revenue from contract with existing customers to transitioning to usage-based pricing. As such, there is short-term uncertainty in profit margins, and although companies are eager to incorporate AI into their services, they remain undecided on which features would be most successful.
The Challenge of Determining a Pricing Strategy
Several software companies are cautious about developing a pricing strategy for AI features. Palantir’s Alex Karp admits that the company does not have one, while other industry players have varying success rates. Companies like Palantir must figure out how to get customers to pay for their generative AI solutions, and how a service that has high processing costs would be offered at a reasonable price. Customers might not be willing to pay a premium for an AI service they can get from several vendors, most especially when such services become commonplace.
On the other hand, providers using AI to reduce software cost could indirectly reduce the amount their customers pay. GitLab’s Sijbrandij suggests that the reduction in cost could lead to an increase in the number of software created since more software companies and engineers can afford to pay for them.
The Risk of Developing AI Bells and Whistles
There is the inherent risk that software companies may introduce AI bells and whistles to existing products without actually considering the real benefits of these features. Consequently, such products become less attracive to customers when several email providers or other software companies offer automated text suggestions. Other challenges could arise as software companies compete with one another in providing AI services, while the realization of real value for customers is yet to be fully assessed.
In conclusion, AI adoption in the software industry requires a careful approach, placing emphasis on determining the best service features and pricing models suitable for customers. The article highlights the inherent potentials and uncertainties of incorporating AI into software services, with the market yet to fully recognize the significance of AI and the success rates for providers.
Summary:
The proliferation of AI in software companies has generated both opportunities and challenges in the tech industry. Although Adobe and ServiceNow have recorded significant stock price increases, several uncertainties remain for public offering companies like Palantir and C3 on the imposition of a pricing strategy for AI features. Providers and customers need to recognize the best service features and pricing models suitable for them to fully appreciate the potential of AI in the software industry.
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Artificial intelligence has finally given a jolt of life to moribund software stocks. Wall Street has been searching all year for the biggest beneficiaries of the AI surge triggered by the launch of ChatGPT: now it’s the turn of a group of companies that have been silenced after the tech boom of the pandemic.
Software companies should be well positioned to both provide the tools businesses need to build generative TO THE in their business processes and to incorporate it into the applications that millions of workers use in their daily lives. But it’s still not entirely clear which one will find the best uses for the technology or how it will get customers to pay.
A spike in the shares of two companies that have struggled to grow steadily since their recent public listing highlights both hope and uncertainty. Palantir stocks and C3.ai they’ve nearly doubled since early May, as each advertised itself as a provider of the technology platforms needed to use generative AI.
However, companies like these will compete with giants like Google and Microsoft, and the impact on revenue is entirely opaque. As Palantir CEO Alex Karp told his investors last month, “We don’t have a pricing strategy” for generative AI. The theory: If the new AI services are as good as the company claims, customers will be happy to pay one way or another.
There will be a lot of competition. The plug-and-play nature of Generative AI — anyone can tap into the large language models created by companies like OpenAI — has made the technology readily available to every software company.
There is an obvious risk that vendors will rush to add AI bells and whistles to their existing products without thinking about what real benefits the technology adds. Also, if every email provider offers automated text suggestions when you write a message, the feature will quickly become commonplace, making it difficult to get customers to pay a premium.
There’s an added risk that if AI actually makes workers more productive, it could reduce the amount of software customers buy. This is the question facing companies like GitLab, which is used to build and distribute software. Like many software companies, GitLab charges based on seat or number of people using its service. If AI makes developers more productive, will customers need it less and pay for fewer seats?
GitLab CEO Sid Sijbrandij tried to put aside that concern this week, arguing that if AI reduces the cost of making software, more will be created. Wall Street liked what it heard. GitLab’s shares jumped by a third after it announced strong results and outlined its plan to implant generative AI into every aspect of its service.
The threat of user-based pricing and the potential difficulty of getting customers to pay a premium has led many software companies to explore the idea of billing as you go – the more customers use new AI features, the more they’ll have to pay . This also has the merit of tying revenue directly to using a service that has a high processing cost.
In the short term, however, this will bring about the kind of uncertainty investors typically hate. C3, for example, attributed the plunge in remaining revenue from its existing contracts — usually a leading indicator — to the fact that it’s transitioning to usage-based pricing. The decline is evident, the impact of a future increase in revenue uncertain.
The uncertainty will be compounded by a short-term decline in profit margins. Most software companies are starting out cautiously, offering new AI features for free as they work out which ones will catch on and how best to charge.
In an interview with the FT’s Cristina Criddle this week, Adobe chief Shantanu Narayen compared this to previous changes to the technology platform. He predicted an eventual shake-up of the many venture capital-backed AI companies that have sprung up that lack an obvious business model. Previous platform switches, however, have led to a long uncertainty before the winners emerge.
Investors are already betting that incumbents like Adobe will be in a strong position to ride the AI wave with its shares up 30% this year. Similarly, shares of ServiceNow, another established cloud software company that has been talking about adding AI to many of its services, are up about 40% this year. But companies like these have yet to demonstrate that they deliver real value, and not just act as resellers for the generative AI produced by companies like OpenAI.
https://www.ft.com/content/70941913-0461-4f20-8bb0-2714db7363a9
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