Regular readers know that we are partial toward Technology Founder CEOs vs. hired CEOs as a general rule. Our experience is that founder CEOs are generally better than hired CEOs at anticipating customer market needs, in many cases before customers know they have a need. Founder CEOs care deeply about details of the business that a hired CEO may not give more than a casual glance. Founders in many cases work to leverage their smart senior business leaders whereas hired CEOs may feel threatened by a direct report’s potential. We could go on.
Our CEORater Technology Founder CEO Index has performed well year-to-date through December 6th enjoying a Total Unweighted Return of 14.2% and a Total Weighted Return of 17.1%. The comparable benchmarks returned 5.1% and 4.4% on an Unweighted and Weighted basis respectively. You may access the detailHERE.
Speaking of Technology Founder CEOs, we recently hosted a podcast with SS&C Technologies (ticker: SSNC) founder & CEO Bill Stone where we discussed M&A strategy, SS&C’s decentralized management approach (another core principal of ours) as well as SS&C’s “Singularity” artificial intelligence (“AI”) and machine learning (“ML”) initiative. In the case of SS&C, we believe that AI and ML will drive efficiencies across back office and middle office-related products and services – enabling customers to drive incremental throughput with less effort. Further, AI & ML has the potential to create revenue and EBITDA opportunities for front-office customers (facilitating deal sourcing as an example). You may access the episode below:
Machines don’t complain. Machines don’t get sick. Machines don’t ask for a raise. Machines don’t get tired. Machines don’t take lunch. Machines can perform complex tasks – including surgical operations and making investment decisions.
Perhaps most importantly – with the advent of machine learning – machines perpetually remain on the learning curve. The old axiom “you can’t teach an old dog new tricks” does not apply to machines.
Many tasks lend themselves well to “machine-led” environments (by “machine-led” we refer to discrete tasks and complex workflows that have or could have machine learning and/or artificial intelligence as a core underpinning). For these tasks, humans will add value by providing machines with access to data sets that machines could not acquire on their own. The saying “feed the beast” will have never been truer.
However, there are instances where machine-led processes are not optimal. For example: great works of art, music and other forms of inspired creativity and original thinking where the outcome isn’t clear.
We are inspired to create when we are inspired to create. Would a sentient machine be inspired to create the Mona Lisa? Would a sentient machine be inspired to put people on Mars as Elon Musk wishes to do? (more likely that a machine would only pursue colonization of Mars when it would be practical to pursue that outcome as a result of an event or series of events here on earth). Would a sentient machine have been motivated to create electric vehicles?
Count me an optimist in that I believe that the most strategic, creative endeavors will always benefit greatly from human participation and leadership.