Product leader, UX full stack developer and entrepreneur working on a startup to modernize an age-old industry with an easy to use app and the cloud.
Washington, DC
Joined October 2009
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Will Lawrence retweeted
The CFO job has gone from accountant to banker to builder, and the whole finance function is being rebuilt around it:
1. Data stopped being the bottleneck. AI pulls and reconciles data across systems, so teams spend more time acting on numbers instead of chasing them.
2. The finance stack is becoming part of the finance team. AI-native tools stand up in days, pull their own data, and turn a monthly close into a daily one.
3. The rise of the finance engineer. Finance people proficient in Claude Code or Codex are building their own tools.
4. The finance team gets smaller and higher leverage. The old heuristic of finance at 5% of headcount is heading to 2% for the leanest teams.
5. Planning becomes continuous. Usage-based revenue (tokens, API calls) moves too fast for a monthly forecast, so planning shifts from annual budgets to a live view.
6. Controls become a critical feature of the product. Every number an agent touches keeps an audit trail back to its source, and finance still owns sign-off.
7. Finance gets a bigger mandate. Compute spend, people versus software, and build versus buy are now finance decisions.
"The CFO is becoming a builder and architect of the company's operating system... The job is still finance, but more of the job now is designing how finance gets done."
a16z's @seemaamble, @IvanoMakSF, and David Borecky on why you need a new CFO: a16z.news/p/you-need-a-new-c…
“For a lot of organizations, especially w/ no properly funded platform team, the path now points somewhere leaner with one canonical design system, machine-readable guidelines, … and only the genuinely difficult components kept as shared, audited code.”
infoq.com/articles/regenerat…
Will Lawrence retweeted
The "whiteboard defense:" I should be able to pull you aside at any moment and ask you to explain any customer-facing system you've shipped. You should be able to clearly explain how it works and defend the decisions you made. This is my benchmark for responsible AI usage.
I don't expect line-level familiarity with the code. I don't care if you remember the exact function name or implementation detail. You may not even know it. I don't care.
But if I ask "why did you do X instead of Y?", "what happens if this actor behaves maliciously?", "what data structure did you use here and why?", or "where does this fail?" you should be able to answer confidently.
For PoCs, demos, experiments, whatever: I don't care. Generate 100% of it and understand none of it. Speed over quality every time in those specific scenarios.
But if you're shipping customer-facing work, you can't be shipping things you don't understand at a high level.
Insightful article on product management and the product developer. forbes.com/sites/stephenwunk…
Will Lawrence retweeted
Strangely enough, one of founders' unique sources of strength is that they've experienced weakness. Hired CEOs tend to take the power of the companies they run for granted. But the founders remember when the company was so weak that it could only survive by delighting users.
Agree. That’s what we’re hoping for at @isheepdog.
dropping another billion dollar fintech idea:
build a neobank with industry-specific financial workflows.
there’s nothing unique about ACH, wires, stablecoins, cards, or 2% cashback anymore.
every fintech has access to roughly the same financial infrastructure.
the next era of neobanks will be won on distribution.
and one of the best distribution wedges is building software around the financial workflows of a specific industry.
one I’ve been thinking about a lot is medical offices.
for context, roughly 50% of medical practices in the US outsource their insurance billing to third-party medical billing agencies.
these companies handle everything between a patient receiving care and the practice actually getting paid:
> submitting claims
> following up with insurance companies
> fighting denials
> collecting outstanding balances
> reconciling payments
traditional medical billing companies can charge practices around 5-6% of revenue collected.
but over the last year, a new category of AI-native medical billing companies has started emerging.
instead of having hundreds of people manually calling insurance companies, submitting claims, and fighting denials, they deploy AI agents to automate large portions of the workflow.
this brings the cost down to 2-3%.
now take this one step further:
build the AI medical billing company AND the financial stack underneath it.
the practice uses your software to submit and manage claims.
insurance payments flow directly into bank accounts you provide.
your software automatically reconciles every payment against the corresponding claim.
you handle receivables, collections, cash management, cards, ACH, wires, and eventually credit.
and credit here becomes especially interesting.
because you control the revenue cycle, you have a real-time view into claims, expected receivables, payment history, denials, and cash flow.
that gives you a proprietary underwriting layer and lets you extend capital against future receivables faster and more intelligently.
now you’re not just their bank.
you’re sitting directly between the work they perform and the money they receive.
that is an insanely powerful wedge into banking.
instead of:
“switch to our bank because we have better rewards.”
the pitch becomes:
“we’ll automate your billing, help you collect more of what you’re owed, charge you less than your existing billing company, and give you a financial stack built specifically around how your practice operates.”
software becomes the distribution.
banking becomes the monetization layer.
and you can extrapolate this model across dozens of other industries.
restaurants → ai voice agent takes orders + payments, paired with banking suite
property management → rent collection + vendor payments + banking
logistics → freight invoicing + factoring + banking
stop building generic neobanks and trying to convince businesses to move their money for another 1% cashback.
own a critical financial workflow in their industry.
then build the bank around it.
banger idea.
Decent questions on priority for leaderships and entrepreneurs to ask themselves in this time and age: Replacement, Ongoing Value, and Fear. hbr.org/2026/07/3-questions-…
This is a pragmatic article that touches on the changing atmosphere of product engineering. github.blog/engineering/the-…
Will Lawrence retweeted
Something I have been thinking about: in the past, the best engineers I knew spent a lot of time automating their work in various ways. Better vim/emacs automations, writing lint rules to catch repeat code issues, building up a suite of e2e tests so they don't need to smoke test the app manually. These kinds of things were the highest leverage activities an engineer could do, because it multiplied their own output, which in turn meant they could build more things.
I think many of these automations have become even more important now. This is true for a number of reasons.
First, infra and DevX automation speeds you up. And if you are running an army of agents, each of those agents will be sped up also. More automation == more output per unit of time.
Second, moving things to code improves efficiency. Your agent could fix an issue every time it sees that issue happen, but that uses tokens and might miss cases. If Claude instead writes a lint rule, CI step, or routine, that class of issue can be fully automated forever. This is really what people are talking about when they talk about loops -- it's about automating entire types of busywork rather than solving them one off. This isn't a new idea at all. Engineers have been doing this for a long time!
Third and most importantly, automation makes it possible for others to contribute to the codebase more easily. Increasingly what I am seeing is engineers are contributing to codebases on day one because Claude can navigate the codebase for them, and that non-engineers are able to contribute to a codebase as effectively as engineers can. What gets in the way of both of these is domain knowledge that lives in peoples' heads rather than in automation -- the stuff you used to have to learn when ramping up. What has changed thanks to agents is the domain knowledge that can be encoded as infrastructure is no longer limited to what is expressible in lint rules and types and tests; it can now capture nearly all domain knowledge, encoded as code comments and skills and CLAUDE.md rules and memories. If I put up a PR for an iOS codebase I don't know and a code reviewer rejects it because it doesn't use the right framework, or if a designer builds a new feature and it gets rejected because it doesn't follow the right architectural patterns, these are failures of automation.
Every team should be writing the CLAUDE.md's, REVIEW.md's, skills, and docs that enable agents to productively work in their codebase with zero additional context from the prompter. This sounds crazy, and at the same time is a natural extension of the stuff engineers have always done: automate, and encode domain knowledge as infrastructure. As the model gets smarter and as the harness matures, this task becomes easier. In the meantime, it is on every team to look for ways to convert their domain knowledge to infra so that Claude can write code better, so that code review catches issues automatically, and so the next person working on your codebase can contribute more easily.
Will Lawrence retweeted
Just coming off of meetings with a couple dozen enterprise IT leaders discussing AI agents. Here are a few of the common themes that stand out:
* Lots of conversation that you have to solve an operating model challenge to get the full benefits of AI. Most companies have orgs that have always operated in siloes; but agents are most effectively when they are tied to a process, which often cuts across these siloes. So the big question is how do you start to deploy centrally managed agents that can work across organizational boundaries. Who manages these agents? How do they get deployed and adopted?
* Data fragmentation remains a major issue for most organizations. As long as data remains highly fragmented and not in standard formats, or data is not available to the right people and agents, enterprises are dealing with issues around being able to get answers from agents that are accurate or that conform to their business practices. This cuts across both systems with structured data (product metrics or revenue figures) and unstructured data (product roadmap or customer contracts).
* Clear sense that companies need to figure out what their core data moats are going to be in the future. If everyone has access to roughly the same superintelligence from the various models, then the context that you feed the models becomes proprietary value in the future. Capturing this data and getting it into a format that agents can use becomes very important.
* Everyone is trying to figure out the right metrics to manage to for AI adoption. General consensus that tokens are not the right metric per se, and people leaning more toward business outcomes (in an ideal world). For business outcomes (like more revenue or more shipped product), though, you have to get close to each individual workflow to figure out if it was successfully transformed with AI so it’s harder to manage top down.
* Growing view that enterprises are going to live in a multi-model world. Lots of interest (though early in actual adoption) in layers that can route workloads to different models (frontside or open weights) for cost or performance reasons. Also enterprises are trying to figure out what things do you give to the models directly vs. what do you separate as horizontal systems and context so you can swap any system in and out.
* Talent for driving AI adoption and implementation still remains a major issue and topic. Many view it as something you necessarily have to train for internally due to a shortage of talent being trained on this in the outside. As an aside, this feels like it remains a huge opportunity for those that get very good at deploying and management agents in an enterprise since most companies are looking for these skills.
* The best use-cases for AI tend to be those that fundamentally change the work being done instead of just replacing an existing process and doing it more efficiently. Companies are working through their versions of this individually because it’s different per industry, but this often remains both the most exciting and higher upside uses of AI.
Many more topics discussed recently, but overall it’s clear that there’s a ton of change going on with much more to come.
Will Lawrence retweeted
As a Director on dozens of boards, I’ve seen:
Operational excellence adds millions in profits.
My 19 traits of top 1% performing businesses 🧵…
Will Lawrence retweeted
I've heard this too many times now. "We cycled through a few different Heads of Product... they never work out."
The pattern looks like this...
1. Founders notice they aren't shipping product.
2. They think a Head of Product will fix it.
3. Head of Product comes in, and spends time/energy at the "strategy" level of framing.
4. The strategy from the Head of Product misses the mark.
5. Founders realize they probably have better ideas about strategy.
6. Founders explain what's important to the Head of Product.
7. Head of Product doesn't have the right skills to translate "this is what we want to solve" to a blueprint of what to specifically build.
In the end, it's a mix of wrong output and no output.
What went wrong here? The #1 mismatch I see is that founders don't know what to expect from the Head of Product, and vice versa.
Founders think they should delegate strategy, which is often not the case.
Heads of Product think they should contribute strategy, which is often not the case.
What's missing is this ... very often Founders have a good nose for what needs to get solved. What they need is someone who can NARROW DOWN and TRANSLATE that into a specific concept with good UX that's technically legible.
But instead what happens is both compete for the "set the strategy" role. And nobody is problems into frames into shapes to go build.
My #1 advice to founders thinking they need a head of product. Ask yourself: do you actually NOT know what you want, strategically? Are you really looking to outsource strategy?
If it's not actually about bringing in someone from outside to set the direction for your whole product ... then you need someone who is skilled ONE LEVEL DOWN in concreteness. Meaning someone who can listen to you about what matters, whiteboard the solution that pulls it all together, and work with technical people to make it happen.
There is a HUGE gap in the market today. We need product people who can work ABOVE the level of Figma-visuals and BELOW the level of "company strategy". So much work of getting to what we actually ship — the blueprint — is in-between.
And let me tell you ... if you EVER go to a product conference, prepare to be deeply misled. There are WAY TOO MANY talks about "strategy" when nobody in the room is truly responsible for strategy.
Product is ultimately about understanding the purpose and translating that into a specific design. A Head of Product who can't whiteboard a UI flow or make technical trade-offs with engineers isn't heading product. They're trying to play CEO.
Will Lawrence retweeted
Snowflake CEO Frank Slootman explains why your company priorities are wrong
“I found out early on that if you can whittle things down to just one thing, you become unstoppable. Unfortunately, people resist whittling things down to one thing because it’s really hard to decide what that one thing is.”
The former Snowflake and ServiceNow CEO continues:
“People have a very easy time telling you what their top 3-5 things are because hopefully the right things are in there somewhere… I can’t tell you how many board meetings I’ve been in where the CEO puts a PowerPoint up and it’s one bullet after another listing all of the things that are their priorities. You just know that they’re going to be a mile wide and an inch deep, swimming in glue, moving like molasses. The energy is leaving my body already just watching a long list of priorities… You’ve basically devalued what you should be doing because you’re time-sharing now with all of these other things.”
Mr. Slootman urges founders to think really hard about the one thing that matters most to your business and focusing entirely on that.
If you can’t decide, just pick one:
“I like to do things in sequence. Even if you’re not sure, do it anyways. Because in the process of doing, you’re going to find out whether you’re right, wrong, or somewhere in between, and you can adjust.”
When you prioritize just one thing, things move much faster:
“Things are going to go much quicker because have a narrower plan of attack. It’s energizing. The pace picks up.”
Source: @twistartups @Jason (Jan 2022)
Will Lawrence retweeted
We went from 0 to 2,200 paying customers in under a year by following @ycombinator's 15 rules:
1/ Do things that don't scale. Get your first 10 customers by hand.
2/ Launch now, not when it's "ready". A mediocre product in front of real users teaches you more in a week than 6 months of polishing in the dark.
3/ Charge from day one. If nobody will pay, you don't have a startup, you have a hobby.
4/ Talk to users every single day. The roadmap you need is sitting in your customers' heads, and they'll hand it to you for free
5/ Always hunt the 90/10 solution. For almost any feature there's a way to capture 90% of the value with 10% of the effort.
6/ There are only two real jobs: write code and talk to users. Everything else (conferences, press, VC coffees, corp dev calls) is fake work.
7/ You pick your customers as much as they pick you. 10 users who love you beat 1,000 who kind of like you.
8/ Growth is an output, not a strategy. Grow before product market fit and all you're buying is churn.
9/ Do less, really well. Pick one or two metrics and judge every task against them.
10/ Know if you're default alive. Paul Graham's question: on current growth and current burn, do you reach profitability before the money runs out?
11/ Don't hire until it hurts. Headcount is not progress, it's burn. Every great startup was embarrassingly small for embarrassingly long.
12/ Momentum is the only real moat in year one. Ship something every week, even something tiny.
13/ Every great startup is badly broken at some point. The game isn't avoiding fires, it's how fast you put them out. Again. And again
14/ Ignore your competitors. Startups die of suicide, not murder. In year one, the only company that can kill yours is your own
15/ Startups rarely die from running out of money. They die because the founders fall out. Brutal honesty with your cofounder is the cheapest insurance you'll ever buy
Good luck !
Will Lawrence retweeted
Most work conversations are now being recorded by default. You should probably assume that everything you say at work is getting recorded from here on out.
What’s emerging is a new category of enterprise software, organized around voice instead of text.
The system of record today is structured data: CRM entries, tickets, docs. But the highest-value context lives in conversation: the nuance on a customer call, the real argument in a product review, the offhand comment in a leadership meeting that quietly changes the roadmap.
LLMs are uniquely good at taking that unstructured voice data and making it structured, searchable, and queryable. That’s a large enterprise opportunity, and we’re still early in understanding what the software layer looks like and who owns it.
a16z GP David Haber on what AI recording means for the future of work: a16z.news/p/everything-is-re…
