Specialist
Former cloud architect at Amazon Web Services.
Agenda
- AI and machine learning operating environment
- Intersecting competition among cloud vendors such as AWS (NASDAQ: AMZN), Google Cloud Platform (NASDAQ: GOOGL), Azure (NASDAQ: MSFT) and third-party players such as Databricks, Snowflake (NYSE: SNOW), Alteryx (NYSE: AYX), Palantir (NYSE: PLTR) and C3.ai (NYSE: AI)
- Customer traction and use cases, coopetition between cloud providers and third-party vendors, forward looking revenue potential and converging product roadmaps
- Outlook for Q4 2021 and beyond – market evolution as cloud providers shift away from third-party vendors and margin expansion opportunity
Questions
1.
Could you give an overview of the AI and machine learning software category, considering AI platforms, multimodal analytics and autoML? Do you think this is the right way to portion out the market?
2.
As you mentioned, this segment has been rapidly evolving and growing. What key trends should investors pay attention to and what industry headwinds or tailwinds revolve around machine learning?
3.
How do you think about the overall market opportunity for machine learning, as well as in its data storage, data modelling and cloud sub-categories? How should we be quantifying a TAM for the machine learning industry?
4.
Considering your estimations, machine learning could be close to a USD 200bn market in five years or so. Which customer groups are driving demand and adoption in end verticals? What kind of YoY growth should we expect as we approach that potential USD 200bn figure?
5.
Could you give an overview of the machine learning offerings of cloud players such as Google, Amazon and Microsoft? What competitive dynamics are at play among them?
6.
As you mentioned, cloud providers such as Amazon, Google and Microsoft have built up substantial offerings that dip into a number of different buckets in machine learning. What is the general thought process for customers when they’re thinking about using an offering from their cloud provider vs going with third parties? So, someone on AWS evaluating SageMaker, someone on GCP [Google Cloud Platform] evaluating Google AutoML and the same for Azure ML Studio. You highlighted a few third parties such as Databricks, Dataiku, Alteryx and Snowflake.
7.
What’s your outlook for companies such DataRobot or Alteryx who play in so many different parts of the machine learning experience but are focused on helping out the citizen user and democratising machine learning by making it more accessible? How disruptive are these types of technologies and do you anticipate them becoming more prevalent?
8.
Do you anticipate these different customer groups implementing more of a best-of-breed adoption strategy, or do you think they will be standardising on one platform as the market matures? Which will provide more value to them in the long run when they’re using machine learning solutions?
9.
How front of mind is total cost of ownership when customers are evaluating use of different machine learning vendors, and will that be a large driving point for customers to go with one of the cloud providers? I know SageMaker touts it can deliver around 54% lower total cost of ownership over three years. Would you say the cost advantages are actually that material and will that move the needle for customers when evaluating?
10.
What is your outlook for C3.ai and Palantir, and the longer-term viability of these companies relative to the vendors we’ve already discussed? It seems they have the potential to disrupt parts of the industry. How might they be positioning themselves relative to the rest of the machine learning sector?
11.
One topic that’s really front of mind for me is the coopetition or frenemy dynamic between cloud providers and other third parties we’ve discussed, given they have active partnerships with all of them, as well as their own competing solutions on almost every end-product offering. How do you think about the relative revenue opportunities for AWS, GCP and Azure from actually competing with different AI and machine learning vendors vs the compute spend that’s going through them from these partnerships? How will this impact their longer-term competitive strategy?
12.
How long do you anticipate the status quo in machine learning remaining? How long until cloud providers make a more concerted effort to compete against third parties vs maintaining the friendly mix of partnering and competing?
13.
When considering cloud providers moving up the stack longer term, how much revenue could AI and machine learning account for relative to the rest of their offerings, given the margin expansion opportunity there you highlighted?
14.
Given how crowded the AI and machine learning industry is and how there’s a lot of overlap between the vendors we discussed, how do you consider the competitive convergence in this sector and how the competitive landscape might evolve as each expands its product offering?
15.
Is there anything you’d like to highlight for us in closing and around the AI and machine learning market, different vendors, how they’re positioning themselves and the existing risks and opportunities?
