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Big Players’ Missteps Of Turning Billion Dollar Startups To Failures

In the world of technology, acquisitions are a common strategy for large companies to expand their capabilities, enter new markets, or eliminate competition. However, not all acquisitions lead to success.

“How do you make money? Spinoffs, split-ups, liquidations, mergers and acquisitions.” – Mario Gabelli, American Stock Investor and CEO of Gabelli Asset Management. 

Now is that statement all true? In the world of technology, acquisitions are a common strategy for large companies to expand their capabilities, enter new markets, or eliminate competition. However, not all acquisitions lead to success. IBM’s ambitious Watson Health initiative, boosted by the success of its Watson AI in other industries, eventually failed due to a series of critical errors. Despite IBM investing around $4 billion to expand its Watson Health portfolio and purchasing many startups, including Truven, Phytel, Explorys, and Merge, the effort failed to meet its expectations. A crucial cause was a lack of explicit problem description and domain expertise in healthcare, resulting in a wide deployment of AI with no unique use cases established. For example, Watson’s cancer diagnosis tool, which was trained on hypothetical instances from a small dataset, struggled with real-world applicability and accuracy. The initiative’s overhyping and underperformance eroded its credibility, which was worsened by integration issues across many healthcare systems. Ultimately, IBM’s financial mismanagement and failure to achieve healthcare providers’ long-term aspirations resulted in the rumoured sale of Watson Health’s assets for $1 billion in 2022. This story demonstrates the vital necessity of strategic focus, data quality, and reasonable expectations in AI-powered healthcare innovation. “Being a first mover is not necessarily an advantage,” sources said.

When large enterprises acquire AI startups, the outcomes can be particularly volatile. While some acquisitions lead to significant growth and innovation, others result in failure. When major organisations acquire AI startups, they frequently face integration issues caused by disputes between the startup’s nimble culture and the enterprise’s inflexible institutions. This cultural mismatch can inhibit innovation and slow growth. Furthermore, the loss of autonomy that comes with purchase might reduce the startup’s inventive drive and increase employee unhappiness. Misalignment of strategic goals complicates matters further, since the acquiring company’s focus on short-term advantages may differ from the startup’s long-term aims. Furthermore, overestimating synergies and possible advantages might result in unwarranted expectations and disappointment. Poor post-acquisition financial management, particularly the failure to connect ongoing R&D investment demands with the acquirer’s financial plans, can put additional strain on resources and lead to failure.

Part I: Cautionary Tales – When Big Tech Fumbled

HPE and Determined AI:

In June 2021, Hewlett Packard Enterprise (HPE) bought Determined AI, a San Francisco-based firm that specialises in machine learning (ML) software, with the goal of expanding its artificial intelligence and high-performance computing capabilities. HPE was initially hopeful about incorporating Determined AI’s open-source ML platform into its products, believing it would improve engineers’ speed-to-production for AI models and achieve faster commercial benefits for clients. However, integration proved more difficult than expected, resulting in underperformance. The combination of Determined AI’s inventive startup culture and HPE’s established corporate structure presented integration challenges, and hopes for synergies may have been overstated. Furthermore, the fast growth of the AI ecosystem faced difficulties in quickly capitalising on Determined AI’s technology.

Uber and Otto:

Uber’s January 2016 acquisition of Otto, a self-driving truck startup formed by former Google workers including Anthony Levandowski, marked a watershed moment in the company’s pursuit of autonomous vehicles. The $680 million acquisition in August 2016 spurred early optimism about using autonomous technology to revolutionise the trucking business, as seen by Otto’s successful 120-mile beer delivery demonstration later in the year. However, the venture faced considerable hurdles. Legal disagreements arose with Waymo (Google’s self-driving vehicle project) over claimed trade secret theft, culminating in a high-profile lawsuit in February 2017. Otto faced severe technological and regulatory difficulties when developing self-driving vehicles. Ultimately, Uber moved its strategic focus to self-driving vehicles, abandoning Otto’s truck project in 2018 because of the difficulty of obtaining complete autonomy and negotiating regulatory environments.

Olive AI

Olive AI, a healthcare automation company founded in 2012, experienced rapid growth and achieved high valuations, particularly during the COVID-19 pandemic. By 2021, the company had raised $902 million in funding and reached a valuation of $4 billion, with its AI solutions deployed in over 900 hospitals across 40 U.S. states. However, the company faced significant challenges, beginning with the layoff of 450 employees in July 2022 due to economic difficulties. Subsequent layoffs and the sale of business segments followed, culminating in Olive AI’s announcement of its shutdown and asset sale in October 2023. The downfall was attributed to rapid growth challenges that strained product and engineering resources, strategic missteps acknowledged by CEO Sean Lane, economic headwinds, integration issues from multiple acquisitions, and financial pressures that hindered profitability and operational sustainability. “Our fast-paced growth and lack of focus strained our product and engineering resources and prevented us from executing quickly on key initiatives. I take responsibility for this,” he wrote.

Part II: Learning from Mistakes – Recent Acquisition Successes

While the above examples highlight potential pitfalls, many tech giants have learned from past mistakes and executed more successful acquisitions in recent years, particularly in the AI space.

Microsoft and Nuance Communications

In March 2022, Microsoft completed its $19.7 billion acquisition of Nuance Communications, aiming to enhance its AI and cloud capabilities, particularly in healthcare. Nuance, a leader in conversational AI and ambient intelligence, brought products like Dragon Medical One and PowerScribe One, which are widely used for clinical speech recognition and documentation. The acquisition sought to integrate Nuance’s AI tools with Microsoft’s Azure cloud services to create more efficient healthcare solutions. Nuance’s technology, already in use by over 55% of U.S. physicians, 75% of radiologists, and in 77% of U.S. hospitals, was expected to improve clinical documentation, reduce clinician burnout, and enhance patient care. This strategic move enabled Microsoft to offer more personalized, effective, and accessible healthcare solutions, leveraging AI to transform the industry.

Apple and Xnor.ai

In January 2020, Apple acquired Xnor.ai, a startup specializing in low-power, edge-based AI tools, for approximately $200 million. The acquisition aimed to enhance Apple’s on-device AI capabilities, aligning with its focus on privacy and efficiency. Xnor.ai’s technology allows AI models to run on devices with limited computing resources, which was expected to improve the performance and privacy of AI applications on Apple devices. The integration of Xnor.ai’s technology has enhanced features like image recognition and on-device AI processing, strengthening Apple’s AI capabilities and supporting its commitment to user privacy.

Databricks and MosaicML

Databricks acquired MosaicML for around $1.3 billion in July 2023 to improve its generative AI capabilities. MosaicML, recognised for its cutting-edge large language models (LLMs), enables businesses to create, own, and secure generative AI models using their own data. The integration of MosaicML’s technology into Databricks’ Lakehouse Platform sought to provide clients with a full AI solution, with MosaicML products such as MPT-7B and MPT-30B being commonly used for generative AI applications. This purchase has boosted Databricks’ position in the AI market by offering clients with better capabilities for more effective bespoke AI model development and deployment.

Apple and DarwinAI

In March 2024, Apple purchased Canadian firm DarwinAI to strengthen its AI capabilities, notably in manufacturing and on-device AI. DarwinAI specialises on vision-based technologies for manufacturing and optimising AI systems, with an emphasis on making AI models smaller and quicker. The acquisition aims to incorporate DarwinAI’s technology into Apple products, therefore improving both manufacturing processes and on-device AI capabilities. DarwinAI’s staff joined Apple’s AI division, helping to create new AI capabilities and technologies. This integration has increased efficiency and quality control in Apple’s production processes while also facilitating the creation of unique AI capabilities for Apple’s products.

Conclusion: Bridging the Gap Between Acquisition and Success

Analysing both failures and triumphs in large-scale technology purchases reveals numerous critical aspects that might make or break an acquisition. Cultural integration is critical since maintaining the acquired company’s culture and principles, especially for startups with strong identities, facilitates a smooth transition. Balancing autonomy and integration is critical, allowing the acquired firm to function autonomously while also incorporating it into the parent company’s ecosystem. Strategic alignment is critical, ensuring that the purchase serves long-term strategic goals rather than just removing competitors or following trends. Talent retention is crucial since it ensures that key workers from the acquired firm continue to innovate and develop products. Successful acquirers must continue to invest in the acquired company’s goods and technology to ensure long-term growth. Leveraging synergies by identifying and capitalising on the acquired company’s interoperability with existing goods and services adds value. Finally, flexibility is essential, because the ability to pivot or modify strategy in reaction to market changes and new opportunities can determine an acquisition’s ultimate success.

The success or failure of large-scale AI startup acquisitions is frequently determined by strategy alignment, successful integration, and the firm’s ability to sustain its inventive culture. While some acquisitions fail owing to poor management and integration issues, others succeed by harnessing AI technology and striking a balance between autonomy and support. Understanding these aspects allows businesses to better manage the intricacies of acquisitions while also encouraging development and innovation.

Picture of Anshika Mathews
Anshika Mathews
Anshika is an Associate Research Analyst working for the AIM Leaders Council. She holds a keen interest in technology and related policy-making and its impact on society. She can be reached at anshika.mathews@aimresearch.co
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