Shelpuk
November 6, 2024
Your competitors are keeping an eye on AI — are you? We want to make it easy for you. Each week, we select and demystify the top five AI news items for business, product, and technology leaders.
Cut through the noise! Spend just five minutes per week with The AI Compass to stay ahead and make informed decisions.
Subscribe now!
Big Tech's Earnings: AI Investments Boost Growth but Raise Concerns — Key Takeaways
RAISE
It has been a significant earnings week for Big Tech, and several major themes have emerged in the markets.
The tech giants — Microsoft, Alphabet (Google's parent company), Amazon, Meta (formerly Facebook), and Apple — reported their quarterly earnings. Overall, companies beat expectations, with many experiencing substantial growth in their cloud businesses. Heavy investment in generative AI is beginning to pay off, showing signs of improving internal efficiency and contributing to revenue growth.
Cloud Businesses Drive Growth
Microsoft, Alphabet, and Amazon saw significant increases in their cloud businesses, fueled by higher demand. Microsoft's Intelligent Cloud segment, which includes its Azure platform, saw revenue jump by 20% year over year to $24.1 billion. Azure alone grew by 34%, with 12 percentage points of that growth attributed to high demand for AI services. However, Microsoft's Chief Financial Officer, Amy Hood, noted that "demand continues to be higher than our available capacity," indicating that growth may slow slightly in the next quarter due to capacity constraints.
Similarly, Google Cloud's revenue grew by 35% year over year to $11.4 billion, driven by accelerated growth in its AI segment. Amazon Web Services (AWS), the leader in cloud market share, saw revenue increase by 19% to $27.5 billion. Amazon CEO Andy Jassy expressed optimism about AI's role in AWS, highlighting it as a "once-in-a-lifetime type of opportunity."
AI Investments Begin to Pay Off but Raise Concerns
Big Tech has invested heavily in generative AI, and these investments are starting to yield results. Google's CEO, Sundar Pichai, stated that the company's AI investments are "paying off," crediting AI as a key driver of Google's quarterly growth. More than a quarter of new code at Google is now AI-generated and then checked by engineers, boosting productivity.
At Meta, CEO Mark Zuckerberg reported that over 1 million advertisers have used the company's generative AI tools to create more than 15 million ads in the past month, leading to a 7% increase in conversions for businesses using their image generation tools.
However, the significant increase in capital expenditures for AI infrastructure and data centers is raising concerns about long-term profitability and cash flow. Microsoft's capital expenditure reached $20 billion in the recent quarter, nearly double the amount from the same period last year. Amazon expects its capital expenditures to hit $75 billion this year, with plans to spend even more in 2025, primarily for AWS.
These substantial investments may impact long-term profitability and cash flow. While investors are excited about AI's potential, they are cautious about the high costs associated with developing AI capabilities. Michael Field, an equity strategist at Morningstar, noted that "AI's potential excites investors, but they are less excited about how much money the Big Tech names are spending on developing AI."
Challenges in Developing AI Chips
As the demand for AI capabilities grows, Big Tech companies are venturing into developing their own AI chips to improve performance and reduce reliance on established chipmakers like Nvidia and AMD. Amazon's AWS is investing in its own Trainium and Inferentia AI chips. Andy Jassy stated that while Amazon maintains a deep partnership with Nvidia, customers are seeking better price performance, prompting Amazon to invest in its own chips.
Developing in-house AI chips presents challenges, including high research and development costs, technical hurdles, and potential strain on relationships with existing suppliers. Companies like Samsung have struggled in this area, ceding ground to competitors due to certification delays and technical challenges.
These developments could affect Big Tech's relationships with established chipmakers. While diversifying suppliers and building in-house capabilities can offer cost advantages and greater control, it may also lead to increased competition with former partners.
Implications for Software Development Practices
The growing reliance on AI-generated code, as seen at Google, has significant implications for software development practices and the future demand for human software engineers. While AI tools can enhance productivity by handling routine coding tasks, there is potential for a shift in the role of human engineers toward more oversight, code review, and tackling complex problems that AI cannot easily solve.
This evolution may lead to changes in hiring practices and skill requirements. Engineers may need to focus more on strategic thinking, design, and integrating AI tools into their workflows. While AI can improve efficiency, human expertise remains critical to ensure code quality and drive innovation.
Conclusion
Big Tech's earnings this week highlight a turning point where significant investments in AI and cloud services are driving growth but also raising new challenges. Balancing the pursuit of AI's potential with the financial implications of hefty investments will be crucial. Additionally, navigating the complexities of developing in-house AI chips and adapting to changes in software development practices will shape the competitive landscape in the technology industry.
Nvidia's Market Value Surges Amid AI Boom, Approaching Tech Giants
NVIDIA founder, President and CEO Jensen Huang speaks about the future of artificial intelligence and its effect on energy consumption and production at the Bipartisan Policy Center in Washington, D.C., on Sept. 27, 2024. Chip Somodevilla | Getty Images
Nvidia has experienced a significant surge in its market capitalization, recently surpassing the $1 trillion mark for the first time. This remarkable growth is driven by the soaring demand for its graphics processing units (GPUs), which are critical components in the development and deployment of AI technologies.
In 2023, Nvidia's stock has nearly tripled, reflecting investors' confidence in its pivotal role within the AI sector. The company's GPUs are essential for training advanced AI models, including those used in natural language processing and image recognition. This positions Nvidia at the forefront of the AI hardware market, capitalizing on the industry's rapid expansion.
While Nvidia's market capitalization has grown substantially, it still trails behind tech giants like Apple, whose market cap remains above $2.5 trillion. Apple's shares have also risen this year, but Nvidia's faster growth rate highlights the market's enthusiasm for AI-related opportunities.
Furthermore, Nvidia's impressive performance has sparked discussions about its potential inclusion in major stock indices. However, as of now, Nvidia has not been added to the Dow Jones Industrial Average, and Intel remains a component of the index.
The company's financial results have been strong, with revenue increasing significantly in recent quarters. Nvidia's success underscores the broader trend of AI's impact on the technology sector and the competitive landscape. Companies that supply key technologies enabling AI are experiencing heightened demand and valuation growth.
For our business, this development signals the importance of staying attuned to advancements in AI and considering how these technologies can be integrated into our strategies. The competitive landscape is shifting as AI becomes more central to technology offerings, and companies like Nvidia play a crucial role in this transformation.
Apple Collaborates with Foxconn to Build AI Servers in Taiwan Amid Capacity Constraints
Nikkei montage/Source photos by AFP/Jiji, Reuters, and AP
Apple has approached Foxconn to build AI servers in Taiwan as part of its efforts to enhance computing capabilities and gain a larger share of the generative AI market. Foxconn, already Apple's largest iPhone supplier, is also a leading manufacturer of Nvidia's AI servers.
However, Foxconn's ability to accommodate Apple's new server orders may be challenged by existing demand from Nvidia. This raises questions about how Foxconn will manage production capacity constraints between Nvidia's orders and Apple's requirements. The company may need to optimize its production processes or expand its facilities to meet the increased demand from both tech giants.
Apple's initiative to produce AI servers in Taiwan underscores its commitment to advancing in the generative AI space. While specific details about the generative AI capabilities or services Apple aims to develop have not been disclosed, this move could strengthen its competitive position in the market. Leveraging Foxconn's manufacturing expertise may expedite Apple's AI development and deployment.
This strategic push could have significant implications for the competitive landscape of the generative AI market. By investing in dedicated AI server production, Apple positions itself to better compete with other tech companies focusing on AI advancements. The partnership with Foxconn in Taiwan not only enhances Apple's technological capabilities but also signals its intention to be a major player in the evolving AI industry.
Perplexity AI Nears $500 Million Funding at $9 Billion Valuation: Rapid Growth and Unique Approach Drive Investor Interest
Dado Ruvic | Reuters
Perplexity AI, an artificial-intelligence search engine startup, is in the final stages of raising $500 million in funding at a $9 billion valuation, a source familiar with the situation told CNBC. Competing against major players like Google and OpenAI—the maker of ChatGPT—Perplexity has attracted significant investor attention amid the generative AI boom.
Key factors driving Perplexity AI's rapid increase in valuation include strong user adoption and its innovative approach to AI-powered search. The company began the year with a valuation of approximately $500 million and has raised four funding rounds so far this year. Its app has been downloaded over 2 million times, and it answers more than 230 million queries a month. U.S. queries have increased eightfold in the past year, highlighting the platform's growing popularity.
Perplexity AI differentiates itself from competitors by providing direct answers with cited sources, combining conversational AI with reliable information retrieval. Unlike traditional search engines that offer a list of links, Perplexity delivers concise answers backed by references, enhancing user trust and efficiency. This unique selling point sets it apart from competitors like Google and OpenAI's ChatGPT.
Institutional Venture Partners, a Bay Area-based firm, is leading the new funding round. Perplexity's most recent valuation was $3 billion in June, and the significant increase reflects investor confidence in its growth trajectory and market position.
The company has faced controversy with accusations of plagiarizing content from media outlets, which it has denied. In response, Perplexity launched a revenue-sharing model for publishers in July, sharing ad revenue generated from citing articles with the original publishers. Media outlets such as Fortune, Time, and The Texas Tribune are among the first to join this program. This initiative aims to address plagiarism concerns and build collaborative relationships with content creators.
The Wall Street Journal was the first to report on the new funding round.
Zombie AI: The Growing Threat of Ungoverned AI in Business and How to Address It
Alamy
Unregulated "Zombie AI" poses a serious threat to business performance, reputation, and compliance. There is an urgent need for scalable, automated AI governance to safeguard and maximize AI investments.
As businesses increasingly use AI and machine learning (ML) across various departments, a hidden risk is emerging—"Zombie AI." These are AI applications developed without proper oversight, operating without meaningful control. While they may appear as innovative successes, they carry the potential for significant performance decline and can lead to financial, reputational, and legal damage.
The Rise of Zombie AI
Zombie AI comes from a lack of governance. Projects may overlook risks, misuse sensitive data, use insecure software libraries, incur uncontrolled costs, and deploy unreliable models. Once in operation, these models often go unmonitored, affecting critical decisions in areas such as customer service, fraud detection, and supply chain management. Over time, their performance deteriorates due to the lack of documentation and reproducibility, leading to failures when they encounter vulnerabilities.
An example of Zombie AI is Zillow's home valuation models. These models failed to account for housing market risks, resulting in significant financial losses and forcing the company to shut down parts of its operations.
Other real-world examples include:
Microsoft's Tay Chatbot: In 2016, Microsoft launched Tay, an AI chatbot on Twitter intended to learn from interactions with users. However, without proper controls, Tay was quickly manipulated into posting inappropriate and offensive content, causing a public relations crisis for Microsoft.
Amazon's Recruiting Tool: Amazon developed an AI recruiting tool that was found to be biased against female candidates because it was trained on historical hiring data that favored males. The tool was eventually scrapped when the bias was discovered.
These cases highlight the risks of deploying AI without proper governance and oversight.
The Amplifying Risk
The danger from Zombie AI is amplified by the rapid growth of AI within organizations. What might start as minimal damage from a few AI models can grow as these models power critical business applications. This growth often outpaces the ability of small teams to manage them effectively.
Balancing Innovation with Oversight
Companies face the challenge of keeping up with the fast pace of AI innovation while maintaining rigorous oversight. The rise of new technologies like generative AI brings exciting opportunities but also introduces new risks related to privacy, security, cost, and ethics. At the same time, regulatory efforts around AI are increasing globally, adding legal and compliance challenges.
To balance innovation with oversight without stifling creativity and competitiveness, organizations should:
Implement Agile Governance: Adopt governance frameworks that are flexible and can adapt to new technologies and use cases. This allows for innovation while ensuring risks are managed.
Promote a Culture of Responsibility: Encourage teams to consider ethical and legal implications from the outset, integrating governance into the innovation process rather than viewing it as a barrier.
Strategies for Scalable, Automated AI Governance
To prevent the spread of Zombie AI, organizations need to implement robust, scalable AI governance throughout the AI lifecycle. This involves more than setting high-level principles; it requires detailed actions for risk management.
Specific strategies and frameworks include:
Unified Visibility: Establish systems for continuous logging, tracking, and monitoring of all AI projects and models across departments. This ensures that all AI activities are visible and can be managed centrally.
Auditability and Reproducibility: Maintain documentation and tools that allow the organization to recreate the conditions under which models were developed and deployed. This is crucial for understanding how models make decisions and for addressing issues when they arise.
Access Management: Implement strict controls over who can access data and models. This helps manage privacy, security, and legal risks by ensuring that only authorized personnel can use sensitive resources.
Policy Management and Enforcement: Automate the alignment of AI projects with company policies and regulatory frameworks. This includes using tools that enforce compliance and provide alerts when policies are not followed.
Leading AI teams in regulated sectors, such as finance and healthcare, provide a blueprint for effective governance. They focus on activities like continuous monitoring, regular risk assessments, and strict access control.
Conclusion
The solution to the Zombie AI threat involves scaling governance through automation. By reducing manual efforts and enabling human oversight and control, organizations can ensure their AI applications are safe, accurate, and compliant. Moving beyond high-level frameworks to comprehensive governance unlocks the full potential of AI investments, fostering innovation and building trust with customers and stakeholders.