In a recent interview with CNBC, IBM CEO Arvind Krishna shared insights on the company’s AI comeback, the WatsonX development studio, and the future of business. IBM, known for its AI platform Watson, is making a strategic shift to monetize its AI products for businesses. The WatsonX development studio allows companies to “train, tune, and deploy” machine learning models, and has already generated “low hundreds of millions of dollars” in bookings in the third quarter alone. Krishna believes that WatsonX could potentially reach a billion dollars in bookings per year. However, IBM faces competition from other tech giants such as Microsoft, Google, and Amazon in the enterprise AI market.
Krishna admitted that IBM was slow to monetize its AI technology and stated that the company made a mistake by going after big, monolithic answers that the world was not ready to absorb. He emphasizes the need for AI to be more agile and customizable, with the ability for users to fine-tune and modify the technology to suit their specific needs. Krishna believes that the future of AI lies in its ability to address the massive amounts of work and data backlog that exist in various industries, such as government, insurance, and customer service.
When asked about the recent executive order on AI regulation signed by President Biden, Krishna expressed support for the order, stating that having safeguards and holding companies accountable for their AI models is important. He even suggested that companies should be legally liable for the actions of their AI models, advocating for legislation that allows for lawsuits if AI models cause harm. However, he does have concerns about sharing test results with the government and the risk of confidential information being made public.
On the topic of AI regulation, Krishna believes that the focus should be on regulating use cases based on risk rather than the technology or algorithms themselves. He supports open innovation and believes that regulations should not stifle advancement or hinder the development of open-source AI. Krishna also emphasizes the importance of trust and governance in AI, stating that model developers should be held accountable for what they create, and AI systems should be regulated based on the level of risk they pose.
Speaking about IBM’s WatsonX development studio, Krishna highlighted its flexibility in deployment. Unlike some other tech companies, IBM does not constrain where companies can deploy their AI models. Whether on a public cloud, private infrastructure, or IBM’s own platform, WatsonX allows for customization and deployment according to the specific needs and preferences of each individual client.
Krishna shared that WatsonX is gaining traction across various sectors, including financial, telecom, retail, and manufacturing industries. The technology is being used for a multitude of different use cases, such as answering phone calls, training employees, streamlining organizational processes, and improving finance team efficiency. Krishna sees interest in WatsonX coming from unexpected sectors, indicating that the potential of AI extends beyond the traditionally regulated industries.
Addressing the criticism that IBM has fallen behind in the AI race, Krishna pointed out the company’s past successes, such as the development of Deep Blue, which monetized AI as early as 1996. He acknowledged that the company was slow to monetize the learnings from Watson’s victory in Jeopardy, but recognized that the approach of going after big, monolithic answers before allowing for customization and experimentation was a mistake. He emphasized that IBM has taken steps to pivot its approach and has quickly launched the WatsonX platform to allow for greater agility and customization in AI deployment.
Krishna discussed the business of generative AI, noting that large language models will unlock massive productivity in enterprises. He cited a McKinsey estimate of $4.4 trillion in annual productivity by 2030, with language models playing a pivotal role. Generative AI, which involves modifying artwork, creating images, advertisements, and music, is an important aspect, though Krishna expressed concern about copyright issues arising from this area.
In addition, Krishna mentioned IBM’s recently launched governance product, which helps businesses ensure that their AI models comply with regulations and includes “nutrition labels” for AI. He stated that IBM has worked with universities and experts in the field to develop bias and fairness monitoring metrics, and believes that government and regulatory bodies should play a role in verifying and testing large AI models due to the resource-intensive nature of the task.
Krishna expressed confidence in the future of AI, stating that AI will create more jobs than it takes away. He emphasized that productivity gains from AI create a natural economic advantage for companies, which in turn leads to more work and the need for more employees. He believes that the fear of job displacement due to AI is overhyped and that the world will see an increase in jobs as a result of AI advancements.
To conclude, Krishna sees AI as a transformative technology that has the potential to address the massive workloads and data backlog faced by industries. He believes in the importance of customization, agility, and trust in AI deployment, and supports regulations that focus on risk rather than stifling innovation. With WatsonX gaining traction across various industries, IBM aims to capitalize on the growing demand for AI solutions. Despite past criticisms, Krishna is optimistic about IBM’s AI comeback and its ability to drive productivity and innovation in the business world.
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