Is Stripe going to eat the payments industry? Live insights from Stripe Sessions 2023

Software has long been supposed to be eating the world. Stripe was kind enough to invite me to their annual stipe ‘sessions’ event, in person for the first time since 2023. And you might of thought of Stripe as a payments company. But the reality is they really position themselves as a software-first and they definitely have a plan to eat there way into every segment of the economy if they can. Here are my (lightly editorialized) live notes of everything Stripe focused on today.

  • Payments, checkout and advanced features (including some that start to dis-intermediate card networks)
  • Stripe for building platforms and marketplaces in every vertical (go forth developers and acquire/service/support all the small businesses for us!)
  • Billing and finance automation (Stripe for bigger business and backoffice integration)
  • Bonus: How Stripe is using generative AI

Stripe, as always, is selling based on eliminating engineering implementation and management costs, solving common painpoints, auth and fraud rates. But certainly not on price. Pricing has not been mentioned. This has always been their value prop, you may pay a little more in variable but you save in fixed costs (and time to market) of attempting to roll anything as sophisticated yourself.

Increasingly stripe is aiming up-market. Investing in enterprise feature sets for big volume customers. For the little guys, it’s all about enabling the aggregators. Specifically vertical platforms that can go out and acquire/service all the SMBs by industry with highly integrated and niche-specific software stacks, w/o Stripe having to do that themselves. 

On Payments

Payments are what Stripe calls the ‘through line’ of everything they do. Stripe is touting the simplification and elimination of engineering costs of maintaining a sophisticated payments page and checkout flow. Stripe optimises complex things like adding new global payment methods, global address autocomplete and verification etc. You pay for this in variable vs fixed costs of building in house.

Now for the new news. Stripe’s ‘link’ for cross-site one click checkout to a bank account. Now, take it from someone who ran Visa’s one click program… one click great, but again would be better if this was an open standard rather than locked-in to Stripe ecosystem. How they solve cross-site cross-site 3rd party cookies, and cross-app privacy sandboxing is unclear.

‘Link’ also enables not just card, but also pay by bank. So there’s a whole end-run around card networks. And a vehicle for Stripe to lean on future RTP rails in the future potentially. Big announcement is that Uber has now adopted Stripe link.

Uber: We say paying with link to enable pay with bank accounts as something we want to use around the world. 

Other payment reveals:

Stripe is saying that companies that shift to stripe payment elements (customizable checkout page widgets) grow topline by 10% as well as cut engineering maintenance cost. Where is that 10% coming from? They don’t break it down. Hard to guess what other conveniently confounding variables might be at work there too.

Next up stripe s700. It’s a slightly-chonky white phone/pos hybrid device. Probably runs a custom android? Apparently it can do table side ordering, but they didn’t emphasize that use case. Otherwise, the device looks… fine?

Enhanced issuer Network. Here again. Stripe is going over the top of the network sharing risk scores directly with issuers, claiming 8% fraud 1-2% auth rates. This is potentially a huge trend, again potentially disintermediating the card networks. But with the same drawbacks, will it scale for issuers to manage custom/proprietary data pipes to every major card processor?

Tap to phone! Stripe is also demoing contactless-on-glass. Finally, no more dongle required if you are okay to just accept tap-to-pay. Works on an iPhone. pretty cool and long time coming to the payments world.

Stripe aggregating the aggregators- Stripe Connect / BaaS

Stripe connect is supposed to be a generalized way to embed money movement and integrated payments. Vertical software platforms are now powering almost all corners of the economy. Platforms that use connect get to market faster, make more money and improve retention. Apparently. Now, I’ve struggled with stripe connect in the past, especially dealing with exceptions, keeping track of failed billing events and state management for customers.  Announcing updates to connect to allow more customization, basically and Stripe Elements for connect?

Also new: stripe is allowing vertical platforms to also include plugins. Like a xero plugin for payments platform for contractors. 

The meta story here is that Stripe is leaning in to the verticalization of software platforms.

Then adding in additional financial services primitives like instant payouts, card issuance, treasury and lending.

On your platform, you can further enable your sellers with tap to pay on iphone and android for super small sellers. E.g. as an electritian using a hypothetical platform for contractors.

Stripe issuing has issued 100M+ cards so far with Issuing. Thats a good number. 

Stripe doesn’t want the CAC and support overhead of actually doing business with every seller our there. They’d much rather equip vertical SaaS developers to go out and distribute all this stripe stuff. Building the killer into every industry niche you can think of. Examples where studio management tools for yoga teachers, a marketplace for home contractors, a creator marketplace for 3d printable minifigs and so forth.

Lastly: Stripe Billing – Revenue and Finance Automation

Problem- a lot of stress on backoffice for global billing. As someone who’s run big SaaS businesses myself, I will freely concede how bizarrely hard it is to just reliably charge your customers every month. 

Connect they tout handles now recurring billing, 1-off invoicing, global tax, accrual accounting, payments  reconciliation.   

These are all legit pain points. I have historically used whole third party solutions like Chargebee to manage recurring billing above Stripe. Apparently managing lots of pricing plans over time (another definite painpoint in SaaS when you are constantly iterating in pricing and special relationship deals) is now more flexible w/ Stripe.

Bonus: How and where Stripe is using generative AI so far

Now they have LLM ai that auto generates SQL queries, even if you don’t know code. The LLM is trained on the SQL tables in your payments data. Scenario: CEO pings CFO hey show me biggest  customers who still have unpaid invoices from last month, no rush, just let me know in the next 15min.” The CFO asks the AI which then in the live demo, figured out the intent of the question, the right sql query, ran the query and gave an answer. So this one demo got a biggest round of applause of the keynote

Second case, Stripe is already using LLM to power developer documentation too. They’ve refined a GPT model against all their docs that can answer developer questions. Another good usecase we’ll see more companies using soon.

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Would you, should you, have you deployed GPT4 inside your business yet?

Are people using GPT4 successfully in real commercial products yet? Is OpenAI’s latest/greatest API a good hire?  A recent timely Ask HN discussion hacker news this week poses this question. Go read the whole thing, or here are my meta-take-ways for anyone trying to build with GPT4 right now.

The model can do useful work today
Some interesting feed across a mix of viable usecases: generating marketing copy and sales emails, to “Correcting or filling missing information in structured data” or Correcting or filling missing information in structured data, or data extraction like from websites or documents, or for internally searching for company information.

But… Availability and performance is a challenge
OpenAI’s APIs aren’t always reachable and response times are variable. Do work around managing for retries, or fallback to other processes if OpenAI is not available. Rate limits are a challenge too, and the process for appealing those is going to be difficult with the current level of demand.

Mix GPT4 and 3.5 versions for speed and Cost considerations
GPT4 is the most accurate but also the slowest and more expensive per call. But you can also try mix and match for usecases where 3.5 is good enough or as a first pass. Test and optimize. At MainStreet, we had good success using basic GPT3.5 to generate super-specific customer help and training material. E.g. “draft a help center article on how [general finance concept] might apply to expenses for [specific job role category] in [customers specific business vertical]. We’d generate this kind of content offline, then review it before publishing. Even with manual review, the speedup vs generating a broad set of help content from scratch was enormous.

Keeping human review in the loop or being clear/transparent with your users

Either it seems people are mostly using GPT internally, where quality of output just has to better or more scaleable than a previous process. Or folks are building apps that explicitly expose the AI to their customers, but adding value through a novel UI or domain-specific assistance with prompting. The ‘copilot’ modality when coding or creating is already a proven commercial model and incredibly popular. Will some equivalent work for banking, investing or financial management apps? It will be interesting to see how easily all of this extends into more regulated or professional/fiduciary responsibility domains. But with the right controls, transparency and model-refinement, it will get sorted out.

Key Takeaway: The big cloud vendors will probably make all this better, just be prepared to pay. The business case here is pretty clear for Azure, Google and AWS et all. Offering, enterprise grade availability, as well as data privacy for custom-trained/refined LLM models is going to be huge business. I could also see opportunities major vertical-oriented players offering something similar. Bloomberg has announced their GPT model for Finance. I’d like to see what Stripe or Visa do for models trained on payments, or models for retail banking, lending, accounting, insurance etc.

Relevant links:

Ask HN: Who has deployed commercial features using GPT4?
Open AI API docs
Microsoft Azure OpenAI services
Google Cloud AI (Note Google Bard API is still closed and invite-only)
Amazon AWS Generative AI Announcement (Announced April 13/2022)
Bloomberg GPT (Announced March 30)

image credit: Midjourney “A robot works in an office, ai, paperwork, midcentury modern”





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Latest ChatGPT Hacks for PMs

As a software product manager it’s hard to think of everything. And you know, in our heart, the true route cause of a lot of bugs is that… the requirements could have been better. This is where I’m kindof excited about he potential of using generative AI also as a co-pilot for PMs.

A couple of prompts I’ve run across that could be of value to a product team in daily work. Of course PMs using these kinds of prompts strictly as first drafts. Or better yet after writing a spec or ticket asking GPT to write a similar ticket and then compare. The tool may give additional ideas or areas/gaps of requirements that are worth adding.

Ticket Writing Prompts:

"You will act as a consultant for tech product managers. Your primary function is to generate a user story and acceptance criteria, given a high level feature description. The user story should be catered to the specific mode of user interaction (e.g. web, mobile), using best-practice UX design guidelines. If you need additional details to provide a good answer, you will continue asking for more context until you have enough to make your user story. Ready to start?"
"As a product manager, I'd like ChatGPT to create Jira tickets for me in the context of a project focused on <enter software description>. For each ticket, I will provide specific information about the bug or feature, and ChatGPT should include this context, along with any other relevant acceptance criteria. As more tickets are requested within the same chat, ChatGPT should remember the context of previous tickets to develop a stronger understanding of the platform over time. I can also provide a list of previous tickets to establish an initial knowledge base. 

First, you should ask me to provide you some examples of previous tickets so you can understand the structure of our tickets and some base knowledge about what has been built already."

Remember that anything you put into a chatgpt prompt essentially becomes public and may violate company principles for leaking proprietry information. Instead you can use the API mode, a dedicated azure instance or a 3rdparty tool based on same that will be safer. OpenAI only keeps rights to (re)use your inputs on the consumer public-facing version of ChatGPT.


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