david ❤️ maps

Now building SF ➡️ Berlin transplant. Find me on 🦋

[david ❤️ maps @davidlovesmaps]

A better Innsbrucker Platz is possible!

[david ❤️ maps @davidlovesmaps]

A better Innsbrucker Platz is possible!

[david ❤️ maps @davidlovesmaps]

@sam_d_1995 Two can play at this game

[Governor Kathy Hochul @GovKathyHochul]

If you’re speeding over and over again, it’s not a mistake. You’re making our streets dangerous.

I’ve got a plan to crack down on super-speeders and make New York City safer.

Let’s get it done.

[david ❤️ maps @davidlovesmaps]

Congrats to the toilet on this Edinburgh to London train

[david ❤️ maps @davidlovesmaps]

@Mehrbod365 @humantransit If we want to create make-work jobs, it shouldn’t come out of the transit budget

[david ❤️ maps @davidlovesmaps]

@StephenMPinto @SukritGanesh "Austin's population density has not increased significantly"

[david ❤️ maps @davidlovesmaps]

@Kazanjy No way there are 4000 people working at BART?! That's like 667 ppl per line!

[david ❤️ maps @davidlovesmaps]

@_Nit3_ @inboundike The OP has no idea how hard it is to stop for every single red light, amirite?

[david ❤️ maps @davidlovesmaps]

Tell me you’ve never lived abroad without telling me you’ve never lived abroad

[Christopher F. Rufo ⚔️ @christopherrufo]

@shadihamid @washingtonpost Raises the question, if immigrant groups want to retain their native culture rather than assimilate, why immigrate in the first place?

[david ❤️ maps @davidlovesmaps]

@belleofballpark @yimbyism And this is why they didn’t zone for more? Because developers wouldn’t even want the freedom to go bigger?

[Andrew Kang @Rewkang]

Researchers trained a humanoid robot to play tennis using only 5 hours of motion capture data

The robot can now sustain multi-shot rallies with human players, hitting balls traveling >15 m/s with a ~90% success rate

AlphaGo for every sport is coming

[david ❤️ maps @davidlovesmaps]

The theory here is that D.C. would become the only place in the world where congestion pricing doesn’t work

[James Hohmann @jameshohmann]

Congestion pricing would cripple downtown D.C. https://t.co/KmpKeRuItv

[david ❤️ maps @davidlovesmaps]

@sam_d_1995 @GershKuntzman Coming from Berlin, the NY Subway is shockingly dirty, rat infested, and occasionally lawless. The U-Bahn can be too sometimes, but NY is at a whole over level.

[david ❤️ maps @davidlovesmaps]

@noapologies @hybrid_lfc @elonmusk @xai @barisakis I’ve got some bad news about where the founder of xAI is from

[david ❤️ maps @davidlovesmaps]

@Noahpinion This is great, but also, brace yourself for a lifetime of being on every Democratic politician in the country’s fundraising email list.

[david ❤️ maps @davidlovesmaps]

@Boenau A dynamically priced toll lane should work as long as there are no limits on the maximum toll

[david ❤️ maps @davidlovesmaps]

@pskpspspss @AJentleson When they build lots of much-needed housing?

[Brian Roemmele @BrianRoemmele]

Anthropic's Revealing Chart on AI's Impact on Jobs

Anthropic has unveiled a pivotal chart that underscores the chasm between AI's capabilities and its real-world application in the workforce.

Derived from analyzing 2 million actual conversations with Claude, this radar chart, titled "Theoretical Capability and Observed Usage by Occupational Category," paints a stark picture of untapped automation potential across various job sectors.

At its core, the chart is a spider web diagram plotting occupational categories around a circular axis, with values ranging from 0 to 1.0 representing the share of job tasks.

The expansive blue area illustrates the theoretical coverage tasks that large language models (LLMs) like Claude could perform right now based on their inherent abilities. In contrast, the much smaller red area shows observed usage, drawn from real user interactions.

The visual disparity is immediate and profound: blue spikes outward significantly in fields like computer and math (reaching about 0.75), business and finance, and office administration, while red hugs close to the center, often below 0.2 across most categories.

This gap isn't just academic; it's a "career runway," as highlighted in discussions around the chart. For programmers, 75% of tasks are theoretically automatable, yet actual usage lags far behind.

Similar vulnerabilities appear in customer service, data entry, and financial analysis, roles traditionally seen as white-collar strongholds. Meanwhile, hands-on fields like construction, agriculture, and protective services show lower theoretical exposure, with blue areas dipping to around 0.1-0.3, suggesting AI's current limitations in physical or unpredictable environments.

Broader data amplifies the chart's message. As of early 2026, 49% of U.S. jobs expose at least 25% of tasks to AI, up from 36% a year prior. Yet, mass layoffs haven't materialized; unemployment in AI-vulnerable roles remains steady.

Instead, subtler shifts are underway: a 14% drop in hiring for 22-25-year-olds in exposed positions indicates companies are prioritizing experienced workers, shortening entry-level pathways for recent graduates.

The implications are clear: while AI's red footprint grows incrementally each month, the blue expanse signals accelerating change. College-educated, higher-earning professionals, once insulated are now most at risk, flipping the script on traditional labor disruptions.

Anthropic's chart isn't a doomsday prophecy but a wake-up call, urging workers and businesses to bridge the gap through adaptation, upskilling, and ethical integration of AI tools.

Please read the 5000 Days Series at https://t.co/tcKeuiQyql for answers on how you can thrive in the Interregnum.

[david ❤️ maps @davidlovesmaps]

Enjoying a quick visit to Colorado. I didn’t know the Rocky Mountains even had oysters. 😋