As we look back at 2020, and head into 2021, I have been contemplating about summarizing some of my recent learnings and thoughts about the future!
First of all, as a Venture Capitalist, I have to recognize and admit that I have been sitting upon a very privileged spot; throughout the past year we were reminded about how resilience and flexibility are vital aspects for survival, and I am indeed very grateful to be surrounded by incredible founders and partners who have been moving above and beyond the normal, working nonstop during these unprecedented times.
Below my article contribution to Emerj Artificial Intelligence Research.
1. Off-the-Shelf or Long-Term Investment
Today’s AI technology owes its existence to public and private sector (i.e. Big Tech) initiatives that built its foundations and created its initial breakthroughs through enormous effort and investment.
Today, the real opportunity lies at the intersection between AI technology advancement and industries. It’s not about machine learning by itself – it’s about the use of these useful algorithmic approaches to business problems. It’s about: AI applied to X.
Two options are open to today’s enterprise firms:
- They can buy generic AI-enabling APIs (Google, Amazon, etc..), share their data and lose control of your margin over time.
- They can invest in their own innovation roadmap, build in-house AI/ML talent teams and work with applied AI vertical solutions. In this case, they own and control the data, their team can perform fast product iteration and customization, and they can maintain control of your margins.
Actionable Insight: Off-the-shelf solutions can serve a purpose, there’s no shame in using them for the right point solution, to explore the technology and begin gaining experience solving problems. In the long-term, however, smart companies won’t use plug-and-play APIs on their own, but as part of a rich AI ecosystem – including many in-house tools and new data infrastructure – all working to solve problems and unlock bigger capabilities.
Building an IP is important, but your differentiation lies in having unique training data. Enterprises will go from understanding to prediction to prescription to autonomous processes – and in order to unlock that full transition, in-house engineering and proper infrastructure are a must.
2. AI Talent the Real Global War
Talent is a critical asset for success in today’s extremely competitive economy. As years go by, AI is likely to automate the routine and repetitive aspects of many workflows. But the industry requires more AI/ML talent to build complex expert AI systems, software architectures, and new hardware (AI chips) today. Some of the critical aspects I notice in the industry are the following:
- Contrary to general assumptions, AI research is significantly less open. In fact, only 15% of papers are publishing their source code (source).
- Only about 53% of AI projects successfully make it from prototype to full production, according to a 2020 research by Gartner.
- AI professors are being picked up by Big Tech companies, creating a critical gap and consequential shortage of new talent entering the market from academia.
Actionable Insight: Applying AI is not about hiring PhDs, it’s about teams that can work together and get things done – mixing AI expertise and deepsubject-matter expert knowledge. Google and Facebook want to buy up teams in their acquisitions, not just individual AI PhDs, and that’s what other enterprises need to understand, too.
Big tech is generally very good at integrating acquired product teams and letting the teams keep doing their job. Enterprises need to learn how to do AI M&A. Retention of top AI talent is hard, but with M&A, acquired talent often needs to vest equity, so you have time to retain top talent – and use their experience to upgrade your team as you work together on important AI projects.
Full article at https://emerj.com/ai-future-outlook/2021-ai-trends-vc/