Investors are analyzing how these tools might accelerate growth and value creation. “Generative AI applications enable companies to increase efficiency and enhance the experience for their customers,” said Diana Doyle, Head of Technology Equity Capital Markets in the Americas. But investors are also considering what it will cost for the creators and users of AI assistants—whether that means hiring people with AI and ML skills to facilitate LLM reinforcement learning and produce secure, accurate and authoritative results, or building or buying generative AI software.
Data Observability Needs Increase with AI
As data volume expands with the adoption of public cloud and digital transformation, companies will increasingly require software tools designed for multi-cloud environments to ingest, manage and gain valuable insights from this data. Observability, or the ability to monitor the health of applications and infrastructure in real time, is critical for companies to maintain uptime and optimal performance for their end users and consistent reliability of service. “The cost of downtime is significant,” said Melissa Knox, Global Head of Software Investment Banking. “Digital businesses can lose upwards of $5 million per hour when their applications or infrastructure are down. The focus now is on predicting what will happen and to be able to prevent outages, downtime or poor experiences—and this is achieved through AI.”
Because AI learns with data, the ability to trust the data is important. Companies want to ensure their data remains proprietary so that they can create their own AI models and improve the efficacy of their solutions to improve outcomes for their customers. They also want to prevent data loss and exfiltration and improve overall cybersecurity.
Vendors aim to prove they are the go-to platforms to address observability at scale, which is challenging because data can come from multiple clouds, regions and sources, so effective monitoring should include views into networking, storage, servers and applications. SaaS companies are racing to gain share in the $22 billion observability market and prove their leadership position to investors by adjusting their business models to become consumption based, and their go-to-market functions, enhancing their ability to sell to customers.
AI Accelerates Demand for Supercomputing
The migration of companies’ digital assets, databases and applications from internal infrastructure to the cloud has been decades in the making. But the AI revolution is accelerating the necessity of cloud services, which offer scaling, flexibility and cost savings compared to on-premise infrastructure.
The handful of blue-chip hyperscalers are the largest providers of cloud computing and storage at enterprise scale. But questions around capacity abound because generative AI requires massive amounts of data and computing power to train its models accurately: How much computing capacity is there? How quickly can it run? What is the low-latency capacity? In addition, companies are seeking cloud optimization to reduce costs by right-sizing resources spent on features and determining where to eliminate cloud resource waste.
Scaling super computers also requires chips. LLM builders, enterprises and governments are boosting semiconductor demand, and companies are seeking to improve chips to speed the movement of data in and out of memory with increased power and more efficient memory systems. In addition, electric vehicle (EV) engines and wireless network infrastructure upgrades are driving demand for chips. But customers want to reduce costs, so semiconductor companies are working to design hardware that helps companies reduce the cost of running queries and the retail prices of products such as EVs for end consumers.
One of the biggest challenges for semiconductor companies has been managing supply and demand, as supply chain issues have led to margin costs. Difficulties obtaining equipment and components, plus a surge in demand as companies and consumers prioritized semiconductor-intensive products, led to a chips shortage that became exacerbated during the COVID-19 pandemic. Investors are watching how initiatives to increase domestic production might help high-speed digital applications get the necessary supplies.