David Boardman

CEO of and

[David Boardman @dbboardman]

Best part of this one-minute demo from Dustin and James: the AI assistant asks where each conversion factor came from before it sets up the yard. Describe it, confirm it, start measuring.

https://t.co/mvZBEmszlM

[Elon Musk @elonmusk]

Interesting piece from CEO of Microsoft

[David Boardman @dbboardman]

@bgurley Agreed. We must decentralize authority of statistical inference machines.

[Bill Gurley @bgurley]

This is what real software security thinking looks like. Not moral platitudes or meeting with religious leaders.

[SR Measure @SRMeasure]

Delivery verification!

[David Boardman @dbboardman]

I wrote this based on the conversations at AggNexus this month about where to get started with AI in the aggregates industry.

An AI pilot is only as good as the numbers it runs on, and in aggregates the most important number is the pile. It is the asset.

[Stockpile Reports @stockpilereport]

Everyone is being told to have an AI plan. For your stockpiles, you started years ago. Here is what is left, in order, and what your AI assistant can do in Stockpile Reports®: https://t.co/s6WvwEYxbQ

[Bill Gurley @bgurley]

This is 100% true. Verifiable fact.

Should be first question for anyone in media that interviews anyone there.

[SR Measure @SRMeasure]

The tech behind SR Measure just went further: verified Stockpile Reports® data is now callable by AI assistants like Claude & ChatGPT. Keep measuring any pile from your phone in seconds, free to try. See what's new → https://t.co/HzxEfh4Ljd #PhysicalAI

[Bill Gurley @bgurley]

This piece from @miramurati & @thinkymachines is quite consistent with the POV this past week from Alex Karp & Satya about companies controlling their own IP. Right place. Right time. Decentralized.

Apache 2.0 to boot!

[David Boardman @dbboardman]

The Stockpile Reports AI beta is officially open - https://t.co/bpTB7rSftq via @ein_news

[Aaron Levie @levie]

The battle in AI is shaping up to be a battle for context.

Everything in AI is about making sure that agents are effective as possible. That effectiveness comes down to whether the agent has the right domain expertise, access to the right context and tools to work with, and are involved in workflow in a way that users can easily interact with, review its work, and incorporate it into the rest of the process.

As a consequence, the platforms that are able to capture and leverage the best and most context within their agents —and be able to pick the right models for the task- will be the place where agents do their best work. You can just look at coding agents, legal agents, or support agents as examples of what this looks like at scale.

This is why the applied AI layer has a lot more value than just being an LLM wrapper. The ability to organize the critical knowledge for the work being done, and maintain this knowledge in a governed way where only the right people and agents have access, and the ability to improve the context for agents more and more over time, is critical.

Over time, this layer will be able to route work between a variety of models, leveraging frontier intelligence for planning and orchestration and review, and a mix of lower cost models (open or closed) for the large volume of work between these tasks.

The applied layer is also in a good position to train and develop its own models as well that are purpose built for their domains. Never good to bet against the bitter lesson, but equally taking a near frontier base model and post training it for just one type of domain work can -in many cases- lower costs or deliver better performance for certain tasks.

Finally, this applied layer is also where most of the change management of the workflow will need to occur. This is why FDEs are so important at the applied layer, because this is the point where the customer needs to have specific business problem solved by a particular vendor. Whichever companies can solve that completely in an end-to-end fashion will have the greatest moats.

As each day goes on, we’re learning more about what the likely long term market dynamics will look like in AI.

[Chamath Palihapitiya @chamath]

https://t.co/vPrPSUjTJG

[Yann LeCun @ylecun]

Exactly. I've been disseminating a similar message for years.

The concentration of power in AI and the desire for control is by far the biggest danger of AI. It could lead to a few private companies and/or countries being in control of access to information, access to knowledge, and access to the tools of economic expansion.
It's a kind of medieval obscurantism akin to the Ottoman empire banning the use of the printing press for 200 years, in part to keep control of the dogma, but also to protect the corporation of the calligraphers and scribes.

Relevant historical bits about the Internet:
1. It took a deliberate decision by Al Gore and Bill Clinton to open up access of what was then ARPAnet to commercial entities and to the public, against the desires of the entrenched telecom industry. During a public roundtable about the "information superhighway" in 1993, the CEO of AT&T told Gore and Clinton "leave it to us". Gore said no.
2. In the late 1980s, setting up an Internet presence required buying proprietary hardware with proprietary OS and software stack from Sun Microsystems, HP, IBM, or Dell. By the 2000s, all of this was wiped out by commodity hardware, Linux, Apache, and an entirely free/open software stack. This migration to open platforms was the result of market forces.

Infrastructure wants to be open.
Foundation models are becoming an infrastructure and will inevitably become commoditized.
Long term, the money is in the application layer, which is what I, Arthur Mensch, Alex Karp, and others have been saying.

[David Boardman @dbboardman]

This post will be referenced in history books. The tide is turning.

[David Sacks @DavidSacks]

Legacy Media types are calling this Alex Karp interview a “crash-out” so that’s your first clue that he is actually saying something extremely insightful. He is articulating what real “AI safety” looks like in the enterprise.

Not abstract alignment research or certification by a government-run DMV for AI. Real AI safety for businesses is the ability to control their own data, model weights, and compute — so a frontier lab can’t hoover up their proprietary knowledge and turn it into their next product.

As Karp explains, technical customers want “control over their compute, their models, their data stack, and their alpha. They want to know they own the means of production, and it’s not being transferred to someone else.”

Don’t think that can happen? Just look at Figma. According to The Information, Anthropic “blindsided” its then-business partner with the launch of Claude Design. Figma’s founder said Anthropic had not been “consistently honest” with them. Anthropic’s chief product officer had even served on Figma’s board until three days before the launch of Claude Design. Figma’s stock has fallen sharply this year while Anthropic’s valuation has surged.

This isn’t an isolated example. Anthropic has launched Claude Science, Claude Security, Claude Legal, and of course Claude Code — each expanding into categories previously served by companies building on top of their models. The pattern is consistent: watch where value is being created, then move in directly. Dominate the model layer, then use that position to capture the most lucrative verticals.

Dario has argued that open source models powerful enough to compete with Anthropic are “dangerous.” But dangerous to whom? Not to enterprises that want to retain control over their data and workflows. Dangerous to a business model that benefits from customers having few real alternatives at the model layer.

As Karp exposes, true enterprise safety isn’t trusting that a lab’s future roadmap won’t include your business. It’s retaining the ability to choose — at the model layer — who gets to see and use your alpha.

[David Boardman @dbboardman]

@SRMeasure That looks staged.

[David Boardman @dbboardman]

@SRMeasure That’s a fuzzy pile. You counting that vegetation as material?

[David Boardman @dbboardman]

Contract crushers and landscape materials companies use this for dispute resolution.

[SR Measure @SRMeasure]

Check out the SR Measure app to measure your piles! #construction #measurementtools #aggregates

[David Boardman @dbboardman]

Measuring stockpiles is fast, easy, and accurate. Anyone from the loader driver to the CFO can measure with @SRMeasure

[SR Measure @SRMeasure]

Found some great piles on the road! I love to take the time to stop and show how easy SR Measure is to use!

[David Boardman @dbboardman]

@SRMeasure is going to @conexpoconagg ! Who is going?

[SR Measure @SRMeasure]

A little over a week away, and we will be in Las Vegas for #conaggexpo looking forward to seeing you all there!!

[Patently Apple @PatentlyApple]

https://t.co/Ic4wqii34z