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Key Takeaways
- IBM's open-sourcing of AI models and strategic focus on cost-effective AI solutions aim to enhance developer engagement and improve financial margins.
- Investments in hybrid cloud and continued innovation in hardware, including quantum computing, point to a growth strategy in both software and infrastructure revenue.
- Overreliance on open-source for AI and aggressive investment in AI/cloud might risk IP control, market competitiveness, and profitability amid global economic pressures.
Catalysts
About International Business Machines- Provides integrated solutions and services worldwide.
- The open-sourcing of IBM's AI models, such as the Granite family under Apache 2.0 licenses, aims to foster innovation and developer engagement, potentially leading to increased adoption and improved product offerings, positively impacting future revenue growth through expanded software and AI solution offerings.
- IBM's focus on fit-for-purpose AI models, which are significantly less costly than larger models, positions the company to offer more economically viable AI solutions to clients, potentially improving margins by reducing operational costs related to AI deployments.
- The announcement of InstructLab, a tool for rapid AI model tuning using synthetic data, can streamline the customization of AI applications for clients, enhancing IBM's competitive edge in AI services and potentially leading to higher consulting revenue.
- The strategic direction towards hybrid cloud, as demonstrated by investments in related infrastructure and collaborations (e.g., Red Hat's AI initiatives), suggests a long-term revenue growth pathway from both software and consulting services, by responding to clients’ needs for flexible, scalable technology solutions.
- IBM's continued innovation in hardware, exemplified by the IBM Z's new AI capabilities and its quantum computing advancements, can drive future growth in infrastructure revenue through differentiated, high-value offerings that address current cybersecurity, data processing, and efficiency challenges.
Assumptions
How have these above catalysts been quantified?- Analysts are assuming International Business Machines's revenue will grow by 4.2% annually over the next 3 years.
- Analysts assume that profit margins will increase from 13.5% today to 13.6% in 3 years time.
- Analysts expect earnings to reach $9.6 billion (and earnings per share of $10.22) by about August 2027, up from $8.4 billion today.
- In order for the above numbers to justify the analysts price target, the company would need to trade at a PE ratio of 23.0x on those 2027 earnings, up from 20.1x today. This future PE is lower than the current PE for the US IT industry at 28.2x.
- Analysts expect the number of shares outstanding to grow by 1.11% per year for the next 3 years.
- To value all of this in today's dollars, we will use a discount rate of 7.65%, as per the Simply Wall St company report.
Risks
What could happen that would invalidate this narrative?- The reliance on open-source models and developer contributions for AI innovations could lead to unforeseen challenges in maintaining control over intellectual property, potentially affecting future revenues and margins.
- Substantial investments in AI and hybrid cloud technologies may not yield the expected returns if customer adoption does not meet IBM's forecasts, potentially impacting net margins.
- The strategy of focusing on smaller, more cost-effective AI models over larger general-purpose ones might limit IBM's competitiveness if larger models prove more effective or desirable in the market, affecting revenue.
- IBM's AI and cloud-based strategies face stiff competition from other major players in the field. Failure to differentiate effectively could lead to loss of market share and pressure on earnings.
- Global economic factors, such as inflation and interest rates, pose a risk to discretionary spending in consulting services, which may result in lower-than-expected growth in this segment, impacting overall profitability.
Valuation
How have all the factors above been brought together to estimate a fair value?- The analysts have a consensus price target of $186.39 for International Business Machines based on their expectations of its future earnings growth, profit margins and other risk factors. However, there is a degree of disagreement amongst analysts, with the most bullish reporting a price target of $222.0, and the most bearish reporting a price target of just $130.0.
- In order for you to agree with the analyst's consensus, you'd need to believe that by 2027, revenues will be $70.5 billion, earnings will come to $9.6 billion, and it would be trading on a PE ratio of 23.0x, assuming you use a discount rate of 7.6%.
- Given the current share price of $183.31, the analyst's price target of $186.39 is 1.6% higher. The relatively low difference between the current share price and the analyst consensus price target indicates that they believe on average, the company is fairly priced.
- We always encourage you to reach your own conclusions though. So sense check these analyst numbers against your own assumptions and expectations based on your understanding of the business and what you believe is probable.
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Disclaimer
Warren A.I. is a tool utilizing a Large Language Model (LLM) that ingests data on consensus price targets, forecasted revenue and earnings figures, as well as the transcripts of earnings calls to produce qualitative analysis. The narratives produced by Warren A.I. are general in nature and are based solely on analyst data and publicly-available material published by the respective companies. These scenarios are not indicative of the company's future performance and are exploratory in nature. Simply Wall St has no position in the company(s) mentioned. The price targets and estimates used are consensus data, and do not constitute a recommendation to buy or sell any stock, and they do not take account of your objectives, or your financial situation. Note that Warren A.I.'s analysis may not factor in the latest price-sensitive company announcements or qualitative material.