Specialist
VP at New York-headquartered software company
Agenda
- Update on the current state of commercial machine learning technology and products for data science use cases
- Competitive environment across market leaders, emphasising niche market challengers, plus players such as Databricks, GCP (NASDAQ: GOOGL), AWS (NASDAQ: AMZN), Microsoft Azure (NASDAQ: MSFT), DataRobot, H2O.ai and Domino Data Lab
- Industry and customer adoption dynamics
- 2023 outlook for industry innovation and trends in AI and machine learning applications
Questions
1.
Could you start by describing the current state of the market for machine learning and AI vendors? How would you categorise or segment the core products?
2.
Could you expand on how you broke down the core market segments across the key vendors in the space? Who are the main players? Some players might overlap in multiple areas, such as the hyperscalers, as well as Databricks, Snowflake, DataRobot, H2O.ai, C3.ai, Palantir and IBM. Who are the market leaders?
3.
Could you describe the coopetition between large cloud providers and third-party vendors? As product roadmaps converge, are there areas of the industry where you expect these partnerships to become less symbiotic or vice versa? Looking at the smaller vendors of point solutions that you mentioned, who might be the winners and losers? What are the winning strategies?
4.
Are there any point solutions that are least likely to be pushed out of the market by hyperscaler solutions, considering the more successful competitors and their offerings?
5.
Large language models using natural language processing models such as GPT-3 and Stable Diffusion technology have seen tremendous interest in 2022. According to a recent report, nearly all of 2022’s largest AI financings were for language start-ups such as Anthropic, Inflection AI, Cohere, Hugging Face and others. What are the current or near-term use cases for this technology? The technology has blown up and gained a lot of interest, perhaps a gimmick in some ways, but how do you expect it to grow and disrupt the market in the near term?
6.
According to a Gartner report, the market for AI software would reach USD 134bn by 2025, accelerating from 14.4% in 2021 to 31.1% in 2025. I saw another report estimating a USD 209bn TAM by 2029. What’s your TAM estimate across the industry?
7.
Which companies are positioned to deliver more verticalised solutions, considering industries that might be most ripe for disruption? How difficult is it to sell these solutions to customers with less mature data science operations?
8.
Can you explain the importance of synthetic data? A Gartner study projected that 60% of all data used in AI development will be synthetic vs real by 2024. Scale AI launched a synthetic data platform called Scale Synthetic. How will synthetic data create new opportunities across industries?
9.
How important are TCO [total cost of ownership] and ROI considerations when customers are evaluating the use of AI and machine learning vendors? What other evaluation criteria is there? How might evaluation criteria change amid a potential recession in 2023?
10.
What are the impacts of tech industry lay-offs on the machine learning industry? It seems there’s a lot of prominent data science talent floating around. Are you seeing any vendors snatching up this talent?
11.
Are vendors doing a good job at getting traction and expanding use cases for current customers? What challenges do vendors face when approaching this?
12.
You mentioned tech cost-cutting and reduction of speculative R&D spend in the industry. What are the margin expansion opportunities for the mature machine learning and data science vendors?
13.
You mentioned industry consolidation trends and areas that are ripe for M&A. Which areas of the market might start seeing M&A activity most quickly?
14.
Do you have any concluding thoughts regarding the machine learning industry?
