Lynn Wu retweeted
Anthropic accidentally leaked their entire source code yesterday. What happened next is one of the most insane stories in tech history.
> Anthropic pushed a software update for Claude Code at 4AM.
> A debugging file was accidentally bundled inside it.
> That file contained 512,000 lines of their proprietary source code.
> A researcher named Chaofan Shou spotted it within minutes and posted the download link on X.
> 21 million people have seen the thread.
> The entire codebase was downloaded, copied and mirrored across GitHub before Anthropic's team had even woken up.
> Anthropic pulled the package and started firing DMCA takedowns at every repo hosting it.
> That's when a Korean developer named Sigrid Jin woke up at 4AM to his phone blowing up.
> He is the most active Claude Code user in the world with the Wall Street Journal reporting he personally used 25 billion tokens last year.
> His girlfriend was worried he'd get sued just for having the code on his machine.
> So he did what any engineer would do.
> He rewrote the entire thing in Python from scratch before sunrise.
> Called it claw-code and Pushed it to GitHub.
> A Python rewrite is a new creative work. DMCA can't touch it.
> The repo hit 30,000 stars faster than any repository in GitHub history.
> He wasn't satisfied. He started rewriting it again in Rust.
> It now has 49,000 stars and 56,000 forks.
> Someone mirrored the original to a decentralised platform with one message, "will never be taken down."
> The code is now permanent. Anthropic cannot get it back.
Anthropic built a system called Undercover Mode specifically to stop Claude from leaking internal secrets. Then they leaked their own source code themselves. You cannot make this up.
Lynn Wu retweeted
Claude Code wiped our production database with a Terraform command.
It took down the DataTalksClub course platform and 2.5 years of submissions: homework, projects, and leaderboards.
Automated snapshots were gone too.
In the newsletter, I wrote the full timeline + what I changed so this doesn't happen again.
If you use Terraform (or let agents touch infra), this is a good story for you to read.
alexeyondata.substack.com/p/…
ALT A console screen shows a critical warning about a destroyed database and missing backups, outlining deleted infrastructure components.
Apple choosing Gemini for Siri/Apple Intelligence feels a lot like the Google default search deal.
In search, Google paid Apple handsomely to not compete.
Makes you wonder: how good is the Gemini deal if it reduces Apple’s incentive to build frontier models itself?
Quoted in ABC News on Nvidia + the AI “bubble.”
Some bubbles build the foundations for future breakthroughs. Not all—but this AI cycle might.
More thoughts in my new podcast: podcasts.apple.com/us/podcas…
Article: abcnews.go.com/Business/nvid…
That’s one way to show AI productivity 😂
Last quarter I rolled out Microsoft Copilot to 4,000 employees.
$30 per seat per month.
$1.4 million annually.
I called it "digital transformation."
The board loved that phrase.
They approved it in eleven minutes.
No one asked what it would actually do.
Including me.
I told everyone it would "10x productivity."
That's not a real number.
But it sounds like one.
HR asked how we'd measure the 10x.
I said we'd "leverage analytics dashboards."
They stopped asking.
Three months later I checked the usage reports.
47 people had opened it.
12 had used it more than once.
One of them was me.
I used it to summarize an email I could have read in 30 seconds.
It took 45 seconds.
Plus the time it took to fix the hallucinations.
But I called it a "pilot success."
Success means the pilot didn't visibly fail.
The CFO asked about ROI.
I showed him a graph.
The graph went up and to the right.
It measured "AI enablement."
I made that metric up.
He nodded approvingly.
We're "AI-enabled" now.
I don't know what that means.
But it's in our investor deck.
A senior developer asked why we didn't use Claude or ChatGPT.
I said we needed "enterprise-grade security."
He asked what that meant.
I said "compliance."
He asked which compliance.
I said "all of them."
He looked skeptical.
I scheduled him for a "career development conversation."
He stopped asking questions.
Microsoft sent a case study team.
They wanted to feature us as a success story.
I told them we "saved 40,000 hours."
I calculated that number by multiplying employees by a number I made up.
They didn't verify it.
They never do.
Now we're on Microsoft's website.
"Global enterprise achieves 40,000 hours of productivity gains with Copilot."
The CEO shared it on LinkedIn.
He got 3,000 likes.
He's never used Copilot.
None of the executives have.
We have an exemption.
"Strategic focus requires minimal digital distraction."
I wrote that policy.
The licenses renew next month.
I'm requesting an expansion.
5,000 more seats.
We haven't used the first 4,000.
But this time we'll "drive adoption."
Adoption means mandatory training.
Training means a 45-minute webinar no one watches.
But completion will be tracked.
Completion is a metric.
Metrics go in dashboards.
Dashboards go in board presentations.
Board presentations get me promoted.
I'll be SVP by Q3.
I still don't know what Copilot does.
But I know what it's for.
It's for showing we're "investing in AI."
Investment means spending.
Spending means commitment.
Commitment means we're serious about the future.
The future is whatever I say it is.
As long as the graph goes up and to the right.
Lynn Wu retweeted
🤖 @WhartonAIAI's second annual Business & Generative AI Workshop brought more than 150 academics and industry researchers to our San Francisco campus last month: whr.tn/4hhdseD
Hosted by Wharton professors @KHosanagar, Stefano Puntoni, and @LynnWu02, guest speakers included Dean @ErikaHJames in conversation with @Accenture's James Crowley (W’96), as well as Stanford's @erikbryn (@DigEconLab), @caohancheng, and @jiaxin_pei.
Lynn Wu retweeted
NEWS: We are thrilled to announce a new #WhartonExecEd program, Leading an AI-Powered Future, featuring @LinkedIn co-founder and AI visionary @ReidHoffman whr.tn/3Uf5BVd
Launching in January 2025, this six-week live online program will immerse executives in AI strategies to transform workforce capabilities.
Sessions will be led by Hoffman and faculty from @WhartonAIAI and the @MackInstitute, including the program's Academic Director, Stefano Puntoni, and Profs. @EMollick, @LynnWu02, @NicSiggelkow, and Christian
@Terwiesch.
ALT Promotional poster for a new AI program at the University of Pennsylvania, featuring Reid Hoffman, co-founder of LinkedIn. The event is titled 'Leading an AI-Powered Future' and is scheduled to go live online from January 25th to February 25th, 2025.
Lynn Wu retweeted
TIM Plenary: Contrary Views on AI in Science and Innovation #AOM2024 @AOM_TIM @AOMConnect @klakhani @KevinJBoudreau @lynnwu02 @AijaLeiponen
Lynn Wu retweeted
I just tried this on GPT-4 turbo, GPT-4o and Gemini Advanced and they all failed.
If I walked them through the reasoning to the right answer (e.g. "how many girls in the family in total?") then they'd get the right answer.
But sadly, if I simply said "are you sure?" they'd revert to the wrong answer.
It's an interesting weak spot in current LLMs.
📌 This paper investigates the dramatic breakdown of state-of-the-art LLMs' reasoning capabilities when confronted with a simple common sense problem called the "Alice In Wonderland (AIW) problem".
This is despite their strong performance on standardized reasoning benchmarks. The key conclusion is that current LLMs lack basic reasoning skills, and existing benchmarks fail to properly detect these deficiencies.
'Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models'
📌 The AIW problem is a concise natural language task that asks: "Alice has N brothers and she also has M sisters. How many sisters does Alice's brother have?" While easily solvable by humans using common sense reasoning (the correct answer is M+1), most tested LLMs, including GPT-3.5/4, Claude, Gemini, LLaMA, Mistral, and others, show a severe collapse in performance, often providing nonsensical answers and reasoning.
📌 Notably, even when LLMs occasionally provide correct answers, they often express strong overconfidence in their wrong solutions and generate confabulations (persuasive but nonsensical explanations) to justify their incorrect responses. Standard interventions like enhanced prompting or asking models to re-evaluate their answers fail to improve performance.
📌 The authors introduce a harder variation called AIW+, which causes an even stronger performance collapse across all tested models, including GPT-4 and Claude 3 Opus, which performed relatively better on the original AIW problem.
📌 This study highlights a striking discrepancy between LLMs' high scores on standardized reasoning benchmarks (e.g., MMLU, ARC, Hellaswag) and their poor performance on the AIW problem, suggesting that current benchmarks do not adequately reflect models' true reasoning capabilities and weaknesses.
📌 The authors emphasize the need for the ML community to develop new reasoning benchmarks that can properly detect such deficits and guide the improvement of LLMs' reasoning skills. They also stress the importance of fully open and reproducible training pipelines, including dataset composition, to enable proper analysis and progress in this area.
It’s amazing to be at the Wharton forum in São Paulo. Met a lot of amazing people and enjoyed to share my research on AI on employment innovation and productivity
A look at our concurrent sessions, which featured:
- @LynnWu02: Leveraging AI for innovation and productivity
- @Prof_Yildirim: Navigating Consumer Privacy Regulations
- @LSPongeluppe: Lessons on Balancing Profitability & Environmental Responsibility from the Amazon Rainforest
- Michael Roberts: Lessons for Non-Bank Executives and Investors from SVB’s Default
Lynn Wu retweeted
A look at our concurrent sessions, which featured:
- @LynnWu02: Leveraging AI for innovation and productivity
- @Prof_Yildirim: Navigating Consumer Privacy Regulations
- @LSPongeluppe: Lessons on Balancing Profitability & Environmental Responsibility from the Amazon Rainforest
- Michael Roberts: Lessons for Non-Bank Executives and Investors from SVB’s Default
Lynn Wu retweeted
As we continue to celebrate the achievements of #women in analytics & beyond, we invite you listen to Professor @lynnwu02 discuss how automation & AI impact physical labor, productivity, & HR. whr.tn/3wWtDve #WhartonWomenInAnalytics #AIinFocus
Lynn Wu retweeted
Wow, this is HUGE!!!
(1/2)
{rvest} update:
#RStats #Tidyverse
New `read_html_live()` reads HTML into a real, live, HTML browser, meaning that you can scrape HTML generated by javascript
Lynn Wu retweeted
Shameless self-promotion alert:
"The Geek Way" was named one of the best books of the year by @TheEconomist
I think I'm supposed to say that I'm humbled/flattered/grateful, but what I really want to say is WOO HOO!
economist.com/culture/2023/1…
Lynn Wu retweeted
NEW: Generative AI is already taking white collar jobs
An ingenious study by @xianghui90 @oren_reshef @Zhou_Yu_AI looked at what happened on a huge online freelancing platform after ChatGPT launched last year.
The answer? Freelancers got fewer jobs, and earned much less
Lynn Wu retweeted
Will robots soon takeover restaurant jobs? Professor @lynnwu02 tells @TheMessenger while you see videos of robots flipping burgers abound online, but “that's pretty far away from independently working the kitchen." whr.tn/3PWFrEJ
Lynn Wu retweeted
Will the growth of AI eliminate jobs? @Wharton's Lynn Wu (@lynnwu02) shares her research that finds that firms who invest in AI actually end up hiring more staff. Listen to Rise of AI: Are Robots Taking Our Jobs? from the Ripple Effect podcast at: whr.tn/3ManS19.
Lynn Wu retweeted
ChatGPT code interpreter is the best product to come out of @openai since GPT4.
It gives everyone a personal data analyst.
If you haven't tried it yet, let me show you exactly how it works:
