At the NORTEC 2020 trade fair for Manufacturers I was invited to a round table discussion about the introduction of AI in the manufacturing industry. Large companies, universities and local business leaders explored how to use AI to drive innovation and create a business plan for it. I was asked to present the role of virtualization for AI/ML projects, and a data scientist was interested (and surprised) by the performance benefits of virtualization as described in the VROOM blog article How Does Project Pacific Deliver 8% Better Performance Than Bare Metal?
Several representatives and local executives from private and family businesses discussed their business. Small and private companies are driving the economy in Northern Germany, where there is not a single DAX company, but many small and medium-sized companies. I was surprised to learn that these smaller companies increase their turnover much faster and more strongly than the large public companies. The consensus was that long-term investments exceed short-term investments. Public companies must take shareholder value into account and provide quarterly figures. Many decisions are made to increase short-term revenues. Smaller companies have a time horizon of 10 to 20 years for their investments, resulting in a more stable and reliable business. They work over many generations.
This has an interesting influence on their AI strategy. These companies cannot afford large investments, so we have discussed joint projects with students from local universities. These entrepreneurs cannot risk investing large sums of money because they have to control the risks. But they are very interested in AI and there are first companies that are starting to get value out of AI. But they are only at the beginning. Another stumbling block is the concerns about using the public cloud for AI projects, especially in terms of compliance and intellectual property protection. As a result, they will want to run AI/ML software in their local data centers or locations. The amount of investment is often only around €10,000 to €15,000 for hardware, so at first I thought this was too uninteresting for cloud infrastructure providers like VMware, who tend to support larger projects. But I was asked about the virtualization of AI/ML workloads because almost everyone has had good experiences with VMware vSphere (or VMware Workstation). In addition, universities and research institutions like DESY have to cover completely different dimensions, which can make infrastructure projects with virtualization interesting.