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Specialist
Former Director at Scale AI
Agenda
- Scale AI’s overall operating environment and market positioning across competing vendors and large cloud vendors such as Snorkel AI
- Value proposition, revenue drivers and demand outlook amid macroeconomic headwinds
- Scale AI’s positioning to benefit from increasing enterprise AI adoption and generative AI proliferation
- Implications of Scale AI’s 2023 20% workforce reduction
- Industry trends, product roadmap and consolidation outlook
Questions
1.
Could you begin by describing Scale AI and where its products fit within the overall value chain for AI solutions, and the company’s value proposition?
2.
Could you elaborate on Scale’s current products, such as its core product, Scale Data Engine, including Scale Rapid and Scale Studio, and any other important products, whether it be Nucleus, Synthetic, Validate or anything else?
3.
Are Rapid and Studio the core data labelling offerings, and the other products such as Nucleus, Synthetic and Validate are outside of that?
4.
What is your growth outlook for immature products, such as Synthetic or Validate? You mentioned building a synthetic data platform is a difficult undertaking. What is your outlook for the company’s ability to develop those products?
5.
How do you view the revenue mix shifting over time, given these different products in the space?
6.
How are market trends towards AI, specifically generative AI market adoption, benefiting Scale AI? How is Scale AI well-positioned given this current market trend?
7.
What are your expectations for Scale’s product roadmap, and anything that Scale needs to develop to stay ahead?
8.
One recent TAM estimate for the global AI market was at USD 150bn in 2023 and growing at 36.8% CAGR to USD 1.345tn by 2030. One report on the data labelling and annotation market estimated TAM to be USD 0.8bn in 2022 and growing to USD 3.6bn by 2027 at 32.2% CAGR. Competitors such as Snorkel AI, who also specialise in data labelling, estimated a much larger TAM in 2019 of USD 37.9bn. How should one size TAM for Scale AI, given its products offerings and market position?
9.
Back in 2019, Snorkel’s TAM estimation was USD 37.9bn. What would the current estimation be?
10.
What is driving growth today? Are there any opportunities for Scale to drive revenue expansion, whether it be through additional products or expanding within a current customer for data labelling use cases? What would that expansion motion look like at Scale?
11.
What does an expansion motion look like? When Scale is able to increase revenue with a customer, what exactly is the customer doing? Is it expanding the amount of data labelling needs the company has?
12.
What kind of revenue multiples can Scale expect from expanding revenue with an existing customer? Is it 1.5x or 3x? How large is that opportunity?
13.
How does revenue growth break down between net new logo and expansion revenue?
14.
Do you have any comments regarding the 2022 DoD [Department of Defense] Joint Artificial Intelligence Center award to Scale AI of USD 249m in a blank purchase agreement and Scale’s overall opportunity in the government market?
15.
What are your rough estimates for revenue mix across core verticals you mentioned earlier – automotive, big tech, manufacturing and defence?
16.
Are there any additional opportunities to pursue new logo growth in any new end markets?
17.
Is the vast majority of Scale’s customer base enterprise-sized or is it more of a mix?
18.
How is Rapid’s PLG [product-led growth] strategy playing out? Is it difficult?
19.
A few enterprise AI vendors are facing significant challenges during the sales process. Given the complexity of AI solutions and the large size of these contracts, showing ROI can be difficult. How difficult is the sales motion for Scale AI? Are there any specific challenges there?
20.
Demonstrating use cases with tangible ROI can be a key difference maker to sell enterprise AI solutions. How significant is this in Scale’s sales process? Is Scale doing this effectively?
21.
Are indirect sales or partnerships a significant aspect of the go-to-market?
22.
When will Scale come up, in a bake-off scenario, head-to-head with Snorkel? When will the company win or lose vs Snorkel? What primary factors will drive that outcome?
23.
Is winning on quality due to something about the technology or is it more caused by the scale of Scale’s solution?
24.
You mentioned Scale could be more expensive and wouldn’t drop its price. What is your rough estimate of how much more Scale costs vs other competitors?
25.
What is Scale’s overall competitiveness vs other solutions? Is the company somewhat competitive or clearly winning a lot more than the others?
26.
How sticky are Scale’s products? What are the most common reasons cited by customers, related to churn?
27.
When a customer churns from Scale AI, is there any common reasoning?
28.
Snorkel AI has a more programmatic approach to data labelling, which employs less labour. Can this approach potentially be a threat to Scale over time, given Snorkel’s ability to bring prices down and have a higher margin product with that programmatic approach?
29.
What is the significance of Scale AI’s generative AI partners? It lists a number from OpenAI to Cohere to Stability to Adept. Are there any of these that come to mind as being important to Scale’s business?
30.
Despite a USD 7.3bn valuation in 2021, given the current market valuations, what’s your view on Scale, as an acquisition target vs a future IPO? What is your opinion on what a future IPO might look like, if you believe that’s still more likely?
31.
Are there any milestones that Scale AI requires to achieve, prior to an IPO, or is this just a question of timing?
32.
If Scale were to be acquired, what kind of company profile might represent a potential acquirer?
33.
How are Scale’s margins trending? What is the core thing to be considered while improving margins?
34.
There might be a little disorganisation at an AI start-up riding a boom. Can you provide any colour on what exactly was causing that disorganisation or what it looked like?
35.
What are your thoughts on Scale AI’s current management team?
36.
You alluded to driving efficiency on gross margins around data labelling. How much more headroom, do you believe, is there for increasing gross margins? Is there a lot more improvement to be made or is it a bit more incremental?
37.
What are Scale’s acquisition opportunities? Is there any kind of company profile you view as a potential acquisition target for it?
38.
How has Scale fared at past integrations of acquisitions? Have these integrations been fairly smooth or have there been any challenges?
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