I empower people. I build startups. I empower people to build startups & I build startups to empower people. Founder @symphco
Cebu City, Philippines
Joined July 2007
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I watched the @Tesla Roadster demo and grinned like a kid, and I showed my wife the flips, to which she asked, "But what happens to my coffee?” Great point, and I love my coffee, so I made a concept film for AETHER, a tumbler meant to keep your coffee in the cup through a flip and in zero-g. There's no prototype and nobody has tested the seal, but I'd rather keep dreaming than drop the idea because the spill is so easy to picture.
Dave Overton retweeted
After Typhoon Odette, @KaMLhuillier set out to stay open no matter what.
Two-year cloud roadmap, done in 8 months on @googlecloud. 1.6M+ Gemini interactions in 60 days. 25M Filipinos served.
Full story (Symph as co-build partner): cloud.google.com/customers/m…
#finance #ml #google
I took my kids to a library for the first time the other day.
AI can summarize every book on the shelf. But access to knowledge is not the same as understanding it.
Reading a full book still builds patience, judgment, and curiosity. Those who read books will have superpowers.
We think about things like this and how we want to shape the future with AI at @symphco.
daveoverton.com/2026/07/10/l…
A dad just walked into Starbucks, pushing a stroller.
The child is lying down in the basket under the chair, pretending to fly. #ParentingWin
Interesting timing. The cost of tokens is real, and it is something that over the last 30 days we had to aggressively manage.
🦔Microsoft canceled its internal Claude Code licenses this week after token-based billing made the cost untenable, even for a company with effectively infinite cloud resources. Uber's CTO sent an internal memo warning the company burned through its entire 2026 AI budget in just four months. American AI software prices have jumped 20% to 37%, and GitHub (owned by Microsoft) is dropping flat-rate plans for usage-based billing across its products.
My Take
The AI subsidy era is ending in real time. The same company that put $13 billion into OpenAI and built the Azure infrastructure powering most of Anthropic's compute just looked at the bill from a competitor's coding tool and decided it was not worth paying. That is not a productivity failure on Anthropic's end. Token-based pricing is forcing every enterprise customer to confront the actual cost of running these models at scale, and the number turns out to be far higher than the flat-rate experiments suggested.
This ties directly to my Gemini Flash post yesterday. Anthropic, OpenAI, and Google all raised effective prices in the last six months. Enterprises that built workflows assuming AI costs would keep falling are now watching annual budgets evaporate in months. Two outcomes look likely from here. Either enterprises scale back AI usage to fit budgets, which slows the revenue ramp the labs need to justify their valuations ahead of IPOs, or the labs cut prices and absorb the losses, which makes the unit economics worse at exactly the wrong moment. Both paths land in the same place, the numbers stop working, and somebody has to take the writedown.
Hedgie🤗
Man, the buzz from @aiDotEngineer #Singapore is really strong!
Any favorite takeaways or talks?
Dave Overton retweeted
Stellar talk by @JoshuaNewton who shared in @aiDotEngineer about how Design is the Difference and how everyone is an Artist now.
Dave Overton retweeted
"If a PM has forgotten how to do the work, no AI tool is favorable"
- Seneca, probably
Dave Overton retweeted
People at major AI labs (using internal models) 3-4 months ahead of startup silicon valley engineers
SV founders/eng 3-6 months ahead of NY
NY founders/eng 6-12 months ahead of rest of world
Most people have no idea how fast AI shifting as 1-2 years behind SOTA
"The future is here, just not equally distributed" - Robert Heinlein
Show receipts.
One underrated product feature in AI software is the receipt.
When an agent drafts a reply, updates a record, creates a task, or recommends a decision, the user should be able to see the source context, the action taken, the confidence level, and the next step without digging through logs like an engineer on call.
Receipts turn AI from a mysterious assistant into inspectable software. They also make reviews faster because the human is judging a trace, not reconstructing a crime scene from chat history.
If the system cannot explain what it did, the user will either distrust it or overtrust it. Both are expensive.
AI makes building cheaper, which exposes the quality of a team’s taste much faster.
When a team can generate screens, flows, copy, code, and test scaffolds quickly, the hard questions move upstream. Which workflow is worth automating? Which source context belongs on screen? Which action needs approval? Which shortcut will create support debt later?
Speed without taste creates a lot of plausible software. Taste decides where the review surface sits, what evidence the user sees, which permissions stay locked, and what recovery path exists when the agent gets a customer workflow wrong.
This is a new problem, and to be honest, we don't have it perfectly figured out, yet. I am curious - what has worked for you?
Dave Overton retweeted
As agents become the biggest users of software, then all software has to be available in a headless fashion. Agents won’t be using your UI, they’ll be talking to your APIs.
So the question becomes what is the business model of software and this headless approach in the future?
Here are a few thoughts on how everything plays out based on what we’re seeing and doing at Box, but also conversation with other platforms.
1) Seats don’t go away for *people*. Seats are still a convenient and efficient way to have a customer use technology predictably for a set of users within a baseline set of usage. The key, though, is that when the customer pays for a seat, it has to come with a set of usage of APIs on behalf of that user that the agent can use on their behalf.
The user will need to be able to interact with their data and the underlying tool via any agent they work with, and an embedded amount of usage will come with the seat. I would imagine most software -Box included- will enable seats to work with their data at a relatively high volume via systems like ChatGPT, Codex, Claude, Gemini, Cursor, Copilot, Perplexity, Factory, Cogniton, et al. quite seamlessly. If you don’t do this, you’re DOA.
2) Agents may have “seats” if they are doing stateful work in the system, but they will be priced very differently than people. Seats (or the equivalent) can make sense when you have an agent that has its own workspace, stores its own data, needs a different set of permissions compared to the user, and so on.
If a company wants this agent to be around for long period of time, that may very well look like another “user” in the system. Openclaw-style agents highlight what this future could look like.
The only issue on pricing here is that one customer could decide to do all their work in 1 agent, and another might split it into 1,000 agents. So pricing like a human seat is nearly impossible and impractical; each company will have a different approach for this as it gets tricky perfectly trying to capture all the value within an agent seat.
3) The dominant pricing for headless use that goes above the seat allotment, or when an agent is firmly acting on their own, will be a consumption model. Many enterprises software platforms have previously operated like this with PaaS options, and agents will look like another machine user of their system.
In some cases the APIs might get priced just as they did previously, but in other cases there may need to be new types of APIs that represent the work an agent would do in one go -more akin to an outcome- instead of a series of API calls. This is especially germane when the headless software also has an agentic use-case embedded within in, such as orchestrating the process within their own system via AI.
Overall the growth of this usage pattern is effectively unbounded as the use-cases for agents operating on data in these systems will dramatically exceed what people do with their data and tools today. Every platform that goes headless (which will be anyone that wants to take advantage of agents) will need to adopt a model like this. Some may fight it initially but it’s an inevitably as there will always be more and more agents outside your platform than people.
Overall, there’s a lot of really interesting changes left to come in software due to headless use of these systems. Early days.
Most AI devs can show you something impressive in an hour or less.
That is not the hard part. The hard part is what happens after the demo, when the system meets real users, messy data, changing requirements, third-party APIs, support tickets, and the occasional 2 AM incident.
If those questions have not come up, you may not be buying a product. You may be buying a demo with a backlog attached.
That is why the cheapest AI dev often becomes the most expensive choice.