Fintech AI Review #14
Fintech fall, application layers, agents, and new modes of interaction…
Do you notice the cool air, bright sun, full rush-hour trains, and the faint smell of pumpkin spice latte? It’s fall fintech conference season! In the month of September alone, I attended 5 separate events, including Finovate, the Nova Cash Flow Underwriting Summit, and the B2B Finance Expo. As we near the end of October, I and several thousand others are gearing up for Money2020, the superbowl of fintech. Unsurprisingly, the potential for artificial intelligence to make a meaningful impact in financial services is top of mind, and one can feel the conversation shifting from generalized hype to concrete applications. Among the questions I found myself asking were:
Which companies will achieve measurable value from the implementation of late-generation AI (as opposed to deterministic ML)?
Which capabilities are best delivered through a heavily productized application layer vs. interacting with a foundation model itself?
Will startups around AI today take a similar form to the SaaS companies of the last 10-15 years, or will they choose a different path?
How do individuals want to interact with AI, and what new form factors, interfaces, and communication modes will emerge?
Today’s newsletter covers several trends, stories, and technologies that address the above questions. In addition to the commentary around each specific link, a couple of other meta-thoughts come to mind.
For the past 2 years, text has been the lingua franca of AI interaction. While the ability to converse with generative technologies through natural language is alluring and familiar, I wonder if people and businesses will eventually tire of it. After all, there are plenty of statements, instructions, queries, and actions that might better be transmitted through less verbose means of communication. Is the concept of a chat prompt akin to the command line of the AI computing age? If so, what form factors will take the place of the graphical user interface in this analogy?
There’s a lot of talk in the startup investment world about the end of SaaS and the rise of “service as software”. Many observers contend that AI technologies will democratize software development, leading to more custom solutions being built within organizations rather than bought from vendors. However, it’s possible to look at this a different way. The companies that will utilize AI to build internal software are those that already have a competency in technical product development, but such organizations are in the minority. Building software is hard, but so is buying it. The number of people and organizations that could benefit from a software solution is several orders of magnitude larger than the number who could competently procure, finance, integrate, and deploy that solution. New types of workflows enabled by AI have the ability to extend automation capabilities to these previously unreachable organizations, massively growing the addressable market for productive technological innovations. The ability to innovate not only on the technology itself but on the form factor and commercial model for delivery may in fact be the best argument for the future of services powered by software.
As always, please share your thoughts, ideas, comments, and any interesting content. If you like this newsletter, please consider sharing it with your friends and colleagues. Happy reading!
-David
P.S. If you’ll be at Money2020 and would like to say hi, please drop me a line!
Recent News & Commentary
Which companies will achieve measurable value from the implementation of late-generation AI?
The AI Hype: $600B question or $4.6T+ opportunity? - Foundation Capital
In this post, Ashu Garg at Foundation capital discusses the balance between hype and opportunity in AI. He begins by noting that much of the initial unqualified enthusiasm around AI has cooled off, with frequent content from major publications and research organizations promoting the idea of a bubble destined to pop. This negative instinct displayed by many is supported by the disappointing performance of public software stocks over the past several years. This piece does a good job of faithfully representing the bear cases - and yes, some people are just lighting money on fire - but ultimately lands in a place of cautious optimism. Ashu and the Foundation team believe that AI is catalyzing a fundamental shift in the role of software. The combination of massive cost efficiencies along with the ability to fundamentally transform workflows leads him to believe that we may actually be underestimating the potential of AI.
Which capabilities are best delivered through a heavily productized application layer vs. interacting with a foundation model itself?
Even in the absence of definitive statistics, it’s probably safe to say that the majority of applicants for a mortgage or HELOC have likely complained about the process being too manual and overly time consuming. Much of this delay is due to the need for human underwriters and loan processors to manually compare information across multiple data sources. Figure, a fintech company specializing in HELOCs, has announced their deployment of an LLM-based (GPT-4 in this case) tool designed to automate such tasks, reportedly taking time and cost out of the process. The techcrunch piece also briefly mentions some of the systems Figure has put in place to run such a system, including a strict privacy agreement with OpenAI along with various model testing and evaluation frameworks. This seems to be a pretty straightforward and obvious use case for AI tools, and it will be interesting to see if Figure releases any customer-centric stats to demonstrate the impact.
Intuit Pioneers Done-for-You Future for Consumers and Businesses with Agentic AI - Intuit
Intuit is betting heavily on its ability to use generative AI and its massive repository of financial data to build agentic automation across its customers’ financial lives. This ambitious and wide-ranging press release promises upcoming capabilities, including cash flow automation for small businesses as well as an advanced, conversation-driven analytical engine. The release also references GenOS, apparently the name of their data architecture that makes such things possible, as well as other internal tools it has built to accelerate development. It’s always hard to tell from a press release exactly what will be widely available, and when, but Intuit does seem incredibly well positioned to build this version of ‘self driving money’ for its customers, and it will be interesting to watch for adoption.
Will startups around AI today take a similar form to the SaaS companies of the last 10-15 years, or will they choose a different path?
Every White-Collar Role Will Have An AI Copilot. Then An AI Agent. - a16z
It seems straightforwardly clear that intelligence is helpful in most ‘information age’ jobs. In this piece on the a16z blog, Angela Strange and James daCosta explain how every white collar job will have an AI ‘copilot’, likely followed by an ‘AI agent’. I doubt many informed people would argue with that premise, but many assume that the incumbents powering centralized systems of record (e.g. Intuit, referenced above) hold an insurmountable advantage, making it difficult for new startups to emerge. The authors here list 3 approaches that upstart competitors might use to overcome the advantages of incumbency. These include gathering data upstream of the system of record, automating workflows that happen outside that system, and unifying data across systems. Once new technologies are able to solve problems in these areas, they gain a foothold that earns them the right to expand their reach across the enterprise, even ultimately creating newer, more comprehensive AI-native systems of record. This logical framing makes a lot of sense and is already being pursued by startups in various verticals. The one thing I’d add is that it’s not a given that the application of AI copilots and agents need to map directly to the present organization of human labor. Perhaps it would be more appropriate, eventually, to say that every business problem will be addressable with a combination of AI agents and humans with copilots, but likely in workflows that don’t neatly resemble those of today.
The Agent Development Life Cycle - Sierra
Over the past couple decades, most software companies have adopted some version of what has become a fairly standard ‘software development lifecycle’ (SDLC). Of course, every technology organization does some things differently. But, there is a well-paved path, and you’ll notice a lot of things in common across the product and engineering teams of many companies. There’s value here, as teams can build upon plenty of hard-learned lessons in software building. However, AI-native applications, particularly those that make use of ‘agents’, may have different opportunities, risks, costs, and constraints than those of traditional deterministic software. In this piece, the founders of Sierra, a platform for building branded, intelligent agents for customer service, argue that the development of agentic, AI-intensive systems requires a new type of software development lifecycle. They lay out the key components of the process on which they’ve presumably converged, including Development, Testing, Release, QA, and Alignment, discussing the AI-driven differences present in each of these steps. This post is full of valuable advice and makes useful reading for practitioners in the space. That said, the computing model, cost structure, and capability set of AI technologies is so different from that of deterministic software, and so early, that we’re likely quite far from settling on the optimal lifecycle. My hope is that a new “ADLC” doesn’t become quite as standardized as things have become in the traditional software world. Perhaps AI might catalyze a new heterogeneity of approaches, leading to a creative explosion of technologies and addressible use cases.
How do individuals want to interact with AI and what new form factors, interfaces, and communication modes will emerge?
The rise of AI Agents in Financial Services - Fintech Brain Food
In this long and well-thought-out essay, Simon Taylor lays out the state of AI in financial services, including how to properly separate hype from substance, why the concept of ‘agents’ is likely to be very meaningful, and why fintech is a ripe market for the application of AI. Parts of the discourse around AI in financial services are misleading or unhelpful, he points out, including concerns around “responsible AI”, “hallucination” and the accusation that it’s just a “trick”. Even when the critics are well-meaning, these are all concerns that can be managed, and those who are working to make a real impact are building with them in mind, unlike the “aging rock stars” he aptly references. Simon is particularly bullish on the potential of agents as a winning form factor for this particular vertical. He constructs a pyramid of ascending value with agents at the top, also arguing that most practitioners should skip the “AI as copilot” phase, contending that a copilot is an awkward interface and sits outside true workflows. Finally, he recommends that people in fintech experiment aggressively with these new technologies. In Simon’s words: “Let go and dance with the AI”. Well said!
Path to high-quality LLM-based Dasher support automation - Doordash
In a complex system like Doordash, with thousands of delivery people delivering millions of orders, there is a lot that can go wrong! I was intrigued to learn that Doordash has a large ‘Dasher’ support team, which has long been a combination of automated and human interaction. However, the existing automations were highly deterministic in nature, making it impossible to solve new problems that didn’t look similar to existing ones already present in their documentation. This somewhat technical blog post outlines Doordash’s development of a RAG-based system to use generative AI technologies to automate the process of Dasher support, along with the systems put in place to ensure accuracy, timeliness, and cost. The piece includes flowcharts of the component systems, including their ‘response guardrail’ and ‘LLM judge’. The architecture is impressive, as is the thoughtfulness and detail apparently present in the system. It’s an example of the value of truly understanding a problem and iteratively building a system to solve it, in a measurable way, with previously unavailable technologies, rather than simply “tossing” the problem to a generic LLM. While this application is in a different industry, there are a lot of valuable lessons for financial services firms. The architecture of a system that provides a high quality user experience while saving time and money, grounded in factual content and with LLM and human failsafes, is something we’ll likely see across domains.
The mortgage application process in the United States tends to involve a lot of back and forth interaction with the applicant, often by phone. These phone calls involve requests for documentation, questions about terms, and coordination of the multiple steps involved in bringing a loan from application to closing. Better Mortgage has now announced its voice-based AI loan assistant, Betsy, along with some impressive demos. It’s smart of Better to begin with the ‘non-licensed’ areas of the mortgage process, where a well-calibrated AI assistant can probably outperform some meaningful percentage of human customer service agents while also staying away from interactions that require human oversight. Apparently, the voice bot is integrated into Better’s proprietary loan origination system to ensure accurate data and help the interactions fit smoothly into a customer’s workflow. I’d enjoy reading an explanation of the technical architecture, similar to the Doordash piece covered above. It will certainly be exciting to see how this works for Better and its clients, and I’m sure we’ll see a lot more moves like this one.
AI agents have brains, but where are their wallets? - Amias Gerety & Prateek Joshi
As we build more workflows around the capabilities of AI agents, the actions we ask them to take on our behalf will expand, and that will surely include making purchases or otherwise moving money. Amias Gerety at QED and Prateek Joshi at Moxie argue that the existing payments system is designed for humans and may therefore have difficulty in adapting to a world with a meaningful share of payments being made by AI agents. For example, there are plenty of risk and fraud prevention mechanisms in payment systems that aim to differentiate between humans and bots. Now these systems would have to assume the existence of good bots, somehow authenticating them and distinguishing them from malicious bots. This piece lays out several points of development around how payment systems might need to evolve to support an agent-heavy future. The question of whether existing payment systems will adapt to suit the needs of agents or whether it is possible to create new, agent-native dominant companies in payments and associated fraud prevention feels similar to the debate around humanoid vs. non-humanoid robots. Those betting on humanoid robots assert that since the built environment is centered on the human form, robots taking on that form will fit in and be more generally productive. Those on the other side contend that without the constraint of conforming to a human form, specialized robots can excel at specific use cases. In the case of payments - a massive market and potentially huge target for agent-driven transformation - there will surely be plenty of ambitious new entrants looking to make their mark.