There is a tidal wave of startup activity around Artificial Intelligence and Machine Learning (AI/ML). It is reasonable for startups and investors to be excited; AI/ML will (eventually) transform many, if not all industries. Here, we take out our crystal ball and make a few projection about what VC funded software startups will look like in 2018.
AI/ML will be a ubiquitous part of the startup tech stack
Some entrepreneurs mistakenly think AI/ML is the pixie dust that will differentiate their company from all the others that walk in the door. But this is just table stakes to get in the game and the cost to play is getting lower. In the past, when mobile, cloud, and big data technology adoption enabled disruptions across multiple industries, costs dropped because of investments made by either large companies (Amazon, Google, etc.) or venture-backed companies to develop better horizontal (industry independent) tools and services. AI/ML is following the same trajectory of adoption and cost. Costs will continue to fall, technical capability will increase, and use will expand exponentially. So, a startup isn’t special because it uses AI/ML any more than a startup is special because it uses AWS. However, talent is in high demand. Having a strong data science team may be compelling if the rest of your business plan hangs together. For this gold rush, miners (data science talent) are a valuable asset but picks and shovels (tools, engines, algorithms) will be universal and nearly free.
VC’s have been busy funding AI/ML companies. In the near future, we will just call them software companies.
Workflow and “Full Stack” solutions beat AI/ML point solutions
AI/ML solutions typically reduce costs (e.g. labor), increase productivity, or improve accuracy. A software startup won’t close enterprise sales if it increases productivity in one place but makes the overall workflow more complex. More so, if it doesn’t improve the workflow (e.g. costs, time, and productivity) in a way that the incumbent competition cannot, there isn’t enough of a differentiator since incumbents can quickly develop the same AI/ML capability. AI/ML-based startups win when they improve workflow in a way that the incumbent technology cannot. This means that AI/ML-enabled software is most powerful (in terms of differentiation) when it provides a unique, full stack solution.
It is also difficult for AI/ML startups to win on just performance when performance is hard to quantify. In the case of many AI/ML applications, it can be difficult to compare two competing systems and declare a winner across all use cases. The “winner” can change over time since any given application can improve with more data, more computational power, and improved algorithms.
Having the best performing AI/ML algorithm at any given point is not a guaranteed winner – having the best full stack solution is the best bet.
The focus is going to be on industry vertical solutions
The promise of AI/ML is to change enterprise workflow (e.g. reduce human labor) or increase efficiency of a process. The technology will have a huge impact on parts of the economy that have traditionally had higher labor content and are slower to adopt new software solutions (manufacturing, oil and gas, medicine, pharmaceutical R&D, etc.). Billions of dollars are going into developing horizontal tools, and cloud services will drive down the cost to develop and deliver vertical AI/ML-enabled software. Software companies deploying vertical solutions often have the benefit of creating a data “castle” that grows more valuable with more customers.
In the long run, there will be a few, very large winners in the somewhat saturated horizontal category but there will be many more opportunities for AI/ML-enhanced software to disrupt the status quo in industry verticals.
Large corporations are going to be fully engaged with AI/ML faster than in past technology waves
Because of the long-term promise of AI/ML, many non-software companies will invest significant resources into data science teams, consultants, and tools. It is unclear whether this corporate activity creates headwinds for startups as corporations try to implement applications on their own; we believe that the technology capability will move too quickly for most corporations to successfully build applications in house. In any case, given the impact of this technology, it is likely that we will see most corporations build AI/ML expertise to evaluate the AI/ML component of new software purchases.
The high level of corporate engagement with the new technology has the potential to accelerate adoption of new AI/ML technology.
This is the start of a new, long, and incredibly valuable transformation in software capability. There will be a huge number of opportunities for startups to benefit from this wave. Though the level of VC and startup activity in AI/ML is arguably a bit frothy at the moment, the technology is far from reaching it’s full potential and as it continues to mature the opportunity set will keep on growing.