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  • ✨96. 👨🏻‍💻Your employer is (probably) unprepared for AI | 🌤️ climate tech SPACs | ⚡️Tesla & Samsung's 4-nm chip for self-driving 5.0

✨96. 👨🏻‍💻Your employer is (probably) unprepared for AI | 🌤️ climate tech SPACs | ⚡️Tesla & Samsung's 4-nm chip for self-driving 5.0

PLUS: humanistic view of AI✨Maryland's AI hiring rules✨RMI's electric sector transformation✨Isometric raises $25M for carbon removal✨EV sales in Germany exceeded 50K in June✨US EV sales accelerating


  • Advancing technology without a greater purpose creates chaos and moves us backward.

  • Concerns about AI stem from the fear that it is driven by ruthless forces with little regard for ethics and humanitarian responsibilities.

  • Human traits like sympathy, empathy, and humor cannot be replicated by AI.

  • AI operates based on data and predictions, lacking true intelligence.

  • AI can be useful as an assistant for tasks that remove human drudgery and enhance efficiency.

  • We must use AI judiciously, considering the consequences of our decisions to make better choices.

  • Maryland, Illinois, and New York City are among the few places that have regulations requiring employers to ask for consent before using artificial intelligence (AI) in certain parts of the hiring process.

  • State laws regarding AI use in hiring have not kept up with the increasing adoption of AI by businesses.

  • Policymakers need a better understanding of AI's civil rights implications, as companies often self-regulate and there is a lack of governance around AI.

  • Some self-regulatory practices have raised concerns, such as biased AI recruiting tools used by organizations like Amazon.

  • Maintaining human involvement in the hiring process, from product design to regular monitoring of automated decisions, can help avoid bias and errors caused by AI.

  • Legislators are calling for more transparency and disclosure from companies regarding their use of AI in hiring processes.

  • The impact of artificial intelligence (AI) on the economy depends on its widespread adoption by firms beyond Silicon Valley.

  • Diffusion of technology across businesses is as critical as innovation for long-term economic growth.

  • Countries like Japan, despite being innovative, struggle with spreading new technology throughout their economies.

  • France excels at spreading knowledge and new tech across its economy, leading to a smaller productivity gap between top and middling firms.

  • Diffusion of technology played a crucial role in productivity growth during the 20th century but has slowed down in recent years.

  • Lower diffusion can be attributed to incremental technological progress, sluggish competition, and growing regulation.

  • Business reorganization around AI and the integration of AI models into in-house data will take time, money, and a competitive drive.

  • Costs associated with AI adoption are currently high, and concerns about privacy and security hinder data sharing and modification of models.

  • Mass deployment of AI may require several years for technology to become more affordable and for businesses to be ready for a revolution.


Imagine you're a data scientist working for an e-commerce company. The company wants to improve its product recommendation system to increase customer engagement and sales. They have a vast amount of customer data, including browsing history, purchase history, customer demographics, and product reviews.

How would you approach building an effective product recommendation system using this data?

What machine learning algorithms or techniques would you consider for this task?

How would you handle challenges like data sparsity, cold start problem, or scalability?

What evaluation metrics would you use to assess the performance of the recommendation system?

How might you continuously monitor and update the recommendation system as new data becomes available?
Please outline your data science approach to enhancing the company's product recommendation system, including preprocessing steps, algorithm selection, and strategies for ongoing optimization.

Want to submit your own? Tweet me @MarcHoag!



  • Major companies including Akamai Technologies, General Motors, Meta, Prologis, Salesforce, and Walmart have joined forces with RMI (Rocky Mountain Institute) to form the Zero Emissions, Reliability Optimized Grid Initiative (ZEROgrid).

  • ZEROgrid aims to accelerate the transition to a reliable and affordable zero-emissions electric grid by enabling corporate action in clean energy procurement, policy, investment, R&D, and operations.

  • The initiative seeks to incentivize broader corporate engagement in grid decarbonization activities and develop a holistic framework with targeted metrics and engagement with grid operators.

  • Corporate renewable purchases have played a significant role in increasing clean energy capacity, and ZEROgrid aims to increase the impact and engagement of corporations beyond procurement alone.

  • Founding members emphasize the importance of decarbonization, grid reliability, and affordability in transforming the electric grid and supporting a clean energy future.

  • RMI is an independent nonprofit focused on transforming global energy systems through market-driven solutions aligned with a 1.5°C future.

  • Climate tech SPACs (Special Purpose Acquisition Companies) are generally trading below their initial price, indicating a lack of investor confidence.

  • Sam Altman's AltC Acquisition Corp., which recently announced a merger with nuclear fission company Oklo, is unlikely to buck the trend.

  • Hardware companies, particularly those with significant capital requirements and a long path to commercial viability, face challenges in the SPAC process.

  • The nuclear industry, known for lengthy design and construction timelines and budget overruns, amplifies these challenges.

  • Despite nuclear fission accounting for a significant portion of the US electricity mix, the industry has seen limited growth in recent years.

  • Isometric, a startup focused on carbon removal, has raised $25 million in seed funding to develop a carbon removal registry and a science platform.

  • The carbon removal registry aims to issue "high-quality, long-duration" carbon removal credits, addressing a gap in the market for transparent and reliable carbon offsetting.

  • Isometric's science platform allows carbon removal companies to publish and share data, while also enabling Isometric's team to vet the companies and their claims.

  • Initial carbon removal startups on the platform include Charm Industrial, Eion, Planetary, and Brilliant Planet.

  • Isometric's founder and CEO, Eamon Jubbawy, aims to bring structure and order to the carbon removal space, learning from the pitfalls of traditional carbon offsetting.

  • While there are still uncertainties and challenges in carbon removal technology, Isometric's efforts to provide transparency and metrics could help distinguish effective solutions in the field.


  • Germany saw over 50,000 all-electric car sales in June 2023, with BEV market share reaching almost 19%, the highest level this year.

  • Total plug-in electric car registrations in June amounted to 68,918, an 18% increase YoY, with all-electric car registrations capturing 18.9% of the market.

  • Year-to-date, there have been over 299,000 passenger plug-in electric car registrations in Germany, a 2% decrease from the previous year.

  • Volkswagen, Tesla, and Mercedes-Benz were the top brands for plug-in electric car registrations in June, with Volkswagen leading in BEVs, Tesla in total plug-ins, and Mercedes-Benz in PHEVs.

  • The best-selling all-electric car model in June and year-to-date was the Tesla Model Y, followed by the Volkswagen ID.4/ID.5 and the Volkswagen ID.3.

  • Tesla is rumored to be partnering with Samsung to develop a self-driving chip based on a 4-nanometer node for Tesla's Hardware 5.0 (HW 5.0).

  • Tesla has been building its own chip architecture team since 2016, led by chip designer Jim Keller, to create powerful and efficient chips for self-driving capabilities in consumer vehicles.

  • In 2019, Tesla unveiled its self-driving chip, Hardware 3.0 (HW 3.0), boasting significant processing improvements and lower power consumption compared to the previous generation.

  • Tesla has already started deploying Hardware 4.0 (HW 4.0) in its vehicles but is now working on the next-generation hardware, potentially incorporating Samsung's 4-nanometer node technology.

  • While Tesla has collaborated with Samsung in the past, it has also reportedly secured a large order of its self-driving chip from Taiwan's TSMC. It remains unclear if Tesla will work with both Samsung and TSMC for its upcoming chip or solely with Samsung.

  • Electric vehicle (EV) sales in the US have reached 4 million, driven by price cuts at Tesla and Ford, tax credits up to $7,500 for consumers, and increased manufacturing capacity.

  • Tesla, General Motors (GM), and Rivian reported strong EV sales in the second quarter, with Tesla holding a 61% market share and GM at just over 4%.

  • While EVs still represent less than 10% of new vehicle sales, rising sales are making them more mainstream, especially in regions like California.

  • The easing of the supply chain crisis has improved the overall auto industry, including EV production, with new players like Rivian increasing output.

  • Discounts, tax credits, and expanded charging network options have contributed to the rising demand for EVs in the US market.


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-Marc 👋

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