How does a company as massive as Mastercard decide where to deploy AI? Instead of centralizing every AI initiative, Mastercard operates under a “hub and spoke” model — balancing centralized AI leadership with decentralized innovation across business units.

In this episode, a16z Partner Marc Andrusko chats with Mastercard’s Chief AI and Data Officer Greg Ulrich about Mastercard’s long history of using AI, the opportunities (and potential risks) associated with integrating generative AI into fraud detection, determining what tech to employ based on use cases, and the best advice he’s ever gotten.

“In the Vault” is a podcast series by the a16z Fintech team, where we sit down with the most influential figures in financial services to explore key trends impacting the industry and the innovations that will shape our future.

Resources:

Find "In The Vault" on a16z.com: https://a16z.com/podcasts/in-the-vault/

Find more about a16z's vision for the future of fintech: https://a16z.com/banking-on-ai/

Find Marc Andrusko on X: https://x.com/mandrusko1

Find Greg Ulrich on LinkedIn: https://www.linkedin.com/in/gregu/

Find a16z on X: https://x.com/a16z

Find a16z on LinkedIn: https://www.linkedin.com/company/a16z

Subscribe to the a16z Fintech newsletter: https://info.a16z.com/newsletters-fintech.html

Most financial institutions still bank on software built before the internet, but Jack Henry's CTO, Ben Metz, is leading a quiet revolution to change that. Speaking with a16z General Partner Angela Strange, Metz shares how banks’ historical tech infrastructure and regulatory obstacles have stymied innovation—and how Jack Henry is helping them overcome these challenges. Metz outlines actionable ways startup founders can help smaller community banks leverage artificial intelligence and cloud computing, reshaping the industry at large.

Chapter Breakdown:

0:47 Backstory
2:15 Community banks and historical infrastructure
4:30 Pre-internet banking & software realities
6:45 Challenges of scaling historical banking tech
8:30 Building software & COBOL
12:15 Dissecting core banking systems
15:40 How regulatory hurdles impact innovation
18:30 Mobile banking and market shiftsT
22:45 Integrating AI into banking's foundation
25:30 Digital service core to banking
28:15 Where AI will be integrated first
31:20 Advice for fintech founders
34:45 Building a data platform to enable innovation
38:20 Working together: banking & tech startups

a16z Partner Seema Amble talks with MongoDB and Viam co-founder Eliot Horowitz about building sustainable open source software tools. From challenging Oracle to building the de facto cloud-based database management tool, Horowitz outlines MongoDB’s early go-to-market strategies, approach to community building, and enterprise sales motion. This episode provides detailed tips on creating solutions for the developer community, building trust amongst enterprises, and successfully scaling a business supported by the open source community.

01:18-03:42 Why open source is a strategic business decision

3:42:6:03 Product development strategy + making developers’ lives easier

6:03-08:24 MongoDB strategy of building community through meetups

08:24-10:49 The five-second rule for developer products

10:49-13:09 How MongoDB used meetups to fuel growth

13:09-15:35 The importance of getting people excited about your product

15:35-19:29 MongoDB’s enterprise sales strategy

19:28-20:26 MongoDB’s recruiting strategy

20:26-25:12 Open source customers vs. users, adoption patterns

25:12-27:35 The evolution of enterprise deals and building trust through demos

27:35-32:23 Product communication strategies and tactics

32:23-39:37 Managing customers and building long-term relationships

39:37-40:16 Outro

a16z Partner Seema Amble connects with Zapier co-founder Wade Foster about the company’s founding and GTM success utilizing a product-led growth (PLG) strategy. Chock-full of actionable insights, this episode explores Zapier’s product strategy, customer experience, and how the team built a sustainable growth engine that enabled them to reach profitability within three years. Foster also provides valuable guidance on brand strategy and how creating an unconventional “weird” brand helped Zapier differentiate from competitors.

1:15-2:05 Zapier's origin story
2:05-3:13 Initial product idea
3:13-5:06 Finding product-market fit and YC
5:06-7:43 How Zapier found their early customers
7:43-10:15 Strategic approach to identifying services to integrate
10:15-13:28 Identifying customer needs and optimizing onboarding
13:28-16:10 Developing a scalable customer acquisition strategy
16:10-19:07 Guidance on building a successful self-serve product
19:07:23:52 Early pricing experiments and brand evolution
23:53-27:04 Early hiring strategy
27:04-29:59 Reflecting on Zapier’s brand identity and product strategy
29:59-31:19 Advice for founders regarding customer acquisition and GTM
31:19-31:59 Outro

Please note that the content here is for informational purposes only; should not be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see

a16z.com/disclosures

.

In this episode, a16z Partner Seema Amble chats with Tomer London, co-founder of Gusto, about establishing product-market fit and product discovery. London shares how Gusto, now valued at ~$10 billion with 300,000+ customers, identified early customers who could provide valuable feedback that helped the team optimize the self-service product experience. The conversation explores Gusto's approach to product development, as well as managing and incorporating customer feedback while maintaining a clear product vision.

1:18-3:14 Gusto’s founding story
3:14- 4:56 Solidifying a GTM sales strategy
4:56-10:03 Identifying early customers and perfecting the sales pitch
10:03-12:45 Working with customers to optimize the product UX
12:45-14:31 Engendering trust and finding success with accountants
14:31-16:41 The role accountants planned in Gusto’s success
16:41-19:34 Creating a feedback funnel and balancing product vision
19:34-22:38 Self-service strategy and measuring success
22:38- 23:45 Refining the UX at scale
23:45-25:45 Identifying real-world use cases to inform product strategy
25:45-29:52 GTM learnings and advice
29:52-30:05 Outro

Please note that the content here is for informational purposes only; should not be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

High-performance compute is the bedrock of generative AI, and if there’s anyone who knows about high-performance chips, it’s AMD CEO Lisa Su. In this wide-ranging conversation with a16z Operating Partner Bob Swan—himself formerly CEO of Intel—Lisa lays out her vision for the evolution of compute within the AI ecosystem, touching not only on raw power and the continuation of Moore’s Law, but also how AMD will support “the right compute for each form factor” for a wider ranges of real-world gen AI use cases. Lisa also shares her perspective on the state of chip manufacturing, how AMD matches their R&D cycles to fast-moving industries, and how partnerships build strong ecosystems.

Read more, including a full transcript, here: https://a16z.com/how-to-build-ai-ecos...

Time stamps:

[00:01:37] Lisa's career in compute
[00:04:48] Compute in the genAI era
[00:09:39] High performance or multimodal?
[00:10:47] Making the genAI ecosystem open
[00:14:16] The chip supply chain
[00:17:53] Resiliency and the CHIPS Act
[00:20:54] How AMD balances long development cycles with short term innovation
[00:24:48] Learnings from the hyperscaler market
[00:26:54] What being fabless means for AMD
[00:31:24] Lisa's advice for startup founders

Find more content from our AI Revolution series on www.a16z.com/AIRevolution.

Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

Finding and backing companies that can reach escape velocity is the name of the game over here at a16z Growth—but picking those winners is far from easy. David George, head of the a16z Growth fund, sat down with the I/O podcast to discuss his mental models for growth-stage investing, what it really takes to go public, where AI is today and where it’s headed, and more.

[00:03:19] What makes Andreessen Horowitz different

[00:08:29] David's mental model for investing

[00:18:23] Focusing on inputs, not outputs

[00:26:52] What constitutes a growth company?

[00:29:14] David's three investing frameworks

[00:36:45] How to measure the ROI of R&D

[00:42:15] What it takes to go public today

[00:46:43] AI: market structure and infrastructure vs. application layers

To read a transcript of this conversation, click here.

In this conversation with a16z general partner David George, Scale AI founder and CEO Alexandr Wang discusses the three pillars of AI—models, compute, and data—and how creating abundant data is core to the evolution of gen AI. With Scale’s work across enterprise, automotive, and the public sector, Alex is also building the critical infrastructure that will allow any organization to use their proprietary data to build bespoke gen AI applications. In addition to talking about frontier data, Alex also shares his learnings from the growth of Scale, his approach to leadership, and what he thinks growth-stage founder/CEOs tend to get wrong about hiring.

Read more, including a full transcript, here: https://a16z.com/frontier-data-foundries-alex-wang-scale-ai/

Timestamps:

[00:00:58] How frontier data will change gen AI

[00:08:47] Are big tech companies over-investing in AI?

[00:14:39] Where the best AI businesses will thrive

[00:17:05] How enterprise businesses are approaching AI adoption

[00:19:50] What does the next phase of gen AI products look like?

[00:23:23] Alex's approach to scaling Scale

[00:25:36] The founder fallacy

[00:30:12] MEI and how Alex views talent acquisition