Please support this podcast by checking out our sponsors:
- KrispCall: Agentic Cloud Telephony - https://try.krispcall.com/tad
- Lindy is your ultimate AI assistant that proactively manages your inbox - https://try.lindy.ai/tad
- SurveyMonkey, Using AI to surface insights faster and reduce manual analysis time - https://get.surveymonkey.com/tad

Support The Automated Daily directly:
Buy me a coffee: https://buymeacoffee.com/theautomateddaily

Today's topics:

Uber hits AI budget wall - Uber’s internal adoption of coding agents surged so fast it reportedly exhausted its early-2026 AI budget, despite measurable code output gains. Keywords: Uber, AI coding tools, Claude Code, costs, R&D.

GenAI productivity paradox returns - A large NBER survey finds most executives report little to no productivity or employment impact from generative AI so far, echoing the historic “productivity paradox.” Keywords: NBER, productivity, J-curve, adoption, trust.

Atlassian trains AI on work - Atlassian plans to collect more customer metadata and some in-app content by default in cloud products to train AI features, raising governance and compliance questions. Keywords: Atlassian, Jira, Confluence, data training, opt-out.

Public backlash and uncanny AI - An essay argues rising anti-AI sentiment is partly driven by an ‘uncanny valley’ effect across text, voice, and video that feels almost human—but not quite. Keywords: public trust, uncanny valley, deepfakes, chatbots, education.

Doctorow critiques AI doomsday framing - Cory Doctorow warns that treating superintelligent-AI risk like a Pascal’s Wager can justify endless spending, while today’s real threat is corporate power and accountability erosion. Keywords: Doctorow, governance, digital public goods, regulation, power.

Open-source security reports surge - curl’s maintainer says AI-assisted tooling is driving a flood of credible vulnerability reports, shifting open-source security work toward relentless triage. Keywords: curl, vulnerabilities, AI tooling, triage, open source.

LLMs outperform compilers in microbench - Performance testing suggests LLMs can sometimes propose surprisingly fast low-level optimizations for narrow tasks, beating typical compiler output in a benchmark—though correctness risks remain. Keywords: ARM64, Apple M4, SIMD, assembly, benchmarking.

Swiss open-science foundation models - The Swiss AI Initiative opened another major call to fund open-science artifacts for foundation models and societal applications, backed by national compute and research partners. Keywords: Switzerland, open science, foundation models, GPUs, ETH/EPFL.

AI hardware boom fuels e-waste - Analysts warn AI’s fast GPU and server refresh cycles could add millions of tons of e-waste by 2030, with disposal burdens often shifting to developing countries. Keywords: e-waste, GPUs, Basel Convention, India, recycling.

-Uber Blows Through 2026 AI Budget After Surge in Anthropic Claude Code Use
-AI’s Productivity Payoff Still Elusive, Echoing the 1980s Solow Paradox
-Swiss AI Initiative Opens Third Major Funding Call for Open Foundation Model Research
-Essay Links Growing Anti-AI Sentiment to a Widening ‘Uncanny Valley’ Effect
-Doctorow Calls AI Doomerism a New Pascal’s Wager, Urges Focus on Corporate Power and Digital Public Goods
-Atlassian to Collect Jira and Confluence Data by Default to Train Rovo AI
-curl Faces AI-Driven Surge in Security Reports as Next Release Nears
-Fabraix Introduces Nyx, a Black-Box Adversarial Testing Harness for AI Agents
-Lemire Benchmarks AI-Generated ARM Assembly Beating a C++ Baseline
-AI Hardware Boom Threatens to Accelerate E-Waste Dumping in Developing Countries

Episode Transcript

Uber hits AI budget wall
We’ll start with AI inside software teams, because the gap between “AI is changing everything” and “who’s paying for this?” is getting harder to ignore.

Uber’s aggressive adoption of AI coding tools has hit a very practical constraint: cost. According to reporting from The Information, Uber’s CTO said the company has already burned through its planned AI budget early in 2026 after internal usage spiked. Engineers were encouraged to use tools like Anthropic’s Claude Code and Cursor, and usage was even tracked on internal leaderboards—great for adoption, not so great for keeping spend predictable. Uber is now rethinking how it budgets for these tools and is preparing to test OpenAI’s Codex as it broadens its options. The most interesting signal here is that the tools aren’t just experiments: Uber says roughly eleven percent of live backend code updates are now generated by AI agents, touching the kinds of systems that directly affect matching, pricing, and bug fixes. It’s a reminder that at enterprise scale, “AI productivity” can come with a very non-trivial operating bill.

GenAI productivity paradox returns
That cost story lands right next to a bigger economic question: where are the productivity gains everyone keeps promising?

A new NBER study surveying thousands of executives across several major economies found that while many companies report using AI, usage is often light—closer to an hour or two a week than an always-on copilot. And nearly nine in ten respondents said AI hasn’t measurably changed employment or productivity over the past few years. That’s striking, given how bullish AI messaging tends to be on earnings calls.

The takeaway isn’t that AI can’t help—it’s that the gains may be bottlenecked by trust, uneven rollout, and plain old workflow friction. Researchers point to a familiar pattern from earlier IT waves: early disruption, messy implementation, and then a delayed payoff once organizations redesign processes and invest in the complements—training, data practices, and incentives. If that “J-curve” is real, the current moment could be the expensive middle where tools exist, but the organizational rewiring is still catching up.

Atlassian trains AI on work
And speaking of rewiring workflows, there’s a major shift in how enterprise software vendors want to fuel their AI features.

Atlassian says it will begin collecting customer metadata—and in some cases in-app content—by default from its cloud products like Jira and Confluence to train its AI tools. The change is slated to begin in August 2026 and affects a very large customer base. Atlassian draws a line between de-identified metadata signals and the actual content people write in tickets and pages, and it says it will de-identify and aggregate what it uses.

Why this matters: it reverses the comfort many teams had that their work systems weren’t feeding a vendor’s training pipeline by default. It also introduces a governance wrinkle, because opt-out options vary by plan tier. For security and compliance teams, this turns into a familiar question: if your project tracker becomes training data, what does that mean for sensitive internal details, retention, and regulatory obligations—even when a vendor promises de-identification?

Public backlash and uncanny AI
Let’s zoom out to the public mood around AI, because another thread this week is that sentiment is hardening—and not always for strictly technical reasons.

A LocalScribe essay argues that hostility toward AI is being amplified by a kind of “uncanny valley” that’s spreading beyond robots into everyday digital experiences. The claim is that people aren’t only worried about fraud, privacy, or job displacement; they’re also reacting viscerally to near-human outputs that feel emotionally off—chatbots that sound empathic but shallow, synthetic voices that almost pass, and realistic videos that crumble under scrutiny. Whether or not uncanny-valley theory fully explains the trend, the practical consequence is clear: if people increasingly associate AI with “something pretending to be real,” trust becomes harder to earn, and adoption in sensitive areas—like education and healthcare—gets politically and socially tougher.

Doctorow critiques AI doomsday framing
That trust and governance tension shows up in a different form in an argument from Cory Doctorow.

Doctorow says fears of future superintelligent AI are sometimes treated like a new Pascal’s Wager: because catastrophe might be possible, advocates argue we must spend vast resou...