[David’s Note]

This week, OpenAI released a seminal and deeply pragmatic report titled Industrial Policy for the Intelligence Age: Ideas to Keep People First (published April 2026). As AI systems transition rapidly from narrow tasks to “Superintelligence”—AI that outperforms the brightest humans even when assisted—we find ourselves at a historic crossroads.

What makes this report profound is its shift away from technical benchmarks toward the “deep water” of social contracts. In a future where machine capability explodes exponentially, what happens to our jobs, tax systems, and social safety nets? OpenAI presents a bold blueprint for policymakers, featuring radical proposals such as a “Public Wealth Fund,” a “four-day working week,” and “containment playbooks” for dangerous models. This is more than a tech giant’s lobbying effort; it is a survival manual for a society facing the tide of superintelligence.

Core Insights & Key Takeaways

The report asserts that the transition to superintelligence has already begun. While it promises a leap in productivity, it also presents unprecedented economic disruption and security risks. To ensure these benefits are shared and not monopolised, OpenAI proposes a new “Industrial Policy” built on two pillars:

1. Building an Open Economy with Shared Prosperity

• The “Right to AI”: Access to AI should be viewed as a fundamental utility, akin to global literacy or electricity. The report calls for free or low-cost access to foundational models to ensure workers, small businesses, and underserved communities are not excluded from the new economy.
• Public Wealth Funds & Tax Reform: As AI reshapes production, capital gains may surge while labour income declines, threatening the funding of social programmes. The report suggests rebalancing the tax base toward capital-based revenues and establishing a “Public Wealth Fund” to give every citizen a direct stake in AI-driven growth.
• The “Efficiency Dividend” & a Four-Day Week: Productivity gains should be converted into tangible benefits for workers. The report encourages piloting a 32-hour, four-day working week with no loss in pay, alongside increased employer contributions to healthcare and pensions.
• Expanding the “Care Economy”: To absorb displaced workers, the report advocates for heavy investment in human-centred sectors like childcare, elderly care, and education—fields where human connection remains irreplaceable.

2. Building a Resilient Society

• The “AI Trust Stack”: OpenAI proposes developing standards for provenance and verification to help people verify AI-generated content and actions. This creates a foundation for accountability when harm occurs.
• Containment Playbooks for Rogue Models: For scenarios where dangerous systems cannot be easily recalled—due to leaked model weights or autonomous replication—the report calls for pre-tested “containment playbooks” to limit harm and coordinate global responses.
• Global Auditing & Information Sharing: The report suggests strengthening auditing standards for the most advanced models without stifling the startup ecosystem. It also envisions a global network of AI Institutes to share safety findings and coordinate during crises.

Ethical AI Commentary

OpenAI’s Industrial Policy offers a rare, macro-level vision within the realm of AI ethics. Historically, Silicon Valley narratives have often defaulted to “technological solutionism.” This report, however, correctly identifies the structural crises of superintelligence: the risk of extreme wealth concentration, the erosion of human agency, and the shifting balance of power. The concepts of a “Public Wealth Fund” and a “Right to AI” are commendable attempts to bake distributive justice into the intelligence age.

However, from a critical ethical perspective, we must remain vigilant:

1. The Risk of Regulatory Capture: While the report claims to oppose “entrenching incumbents,” a policy framework designed by the world’s leading AI developer carries inherent conflicts of interest. Proposals to limit strict auditing to only “a small number of companies” could inadvertently create a moat that stifles open-source innovation and smaller competitors.

2. The Challenge of Representative Alignment: OpenAI calls for “democratic input” to define AI values. Yet, in a global context, how do we ensure this input is truly inclusive across different cultures and socio-economic classes? Without a binding international legal framework, “democratic alignment” risks becoming a mirror for the values of a technical elite.

3. Power Checks on Superintelligence: True ethical AI requires more than a social safety net; it requires robust checks on those who hold the “kill switch.” The transfer of AI dividends to the public cannot rely on corporate altruism or “mission-aligned” board structures alone; it must be anchored in enforceable, global law.

As the report suggests, the conversation is only just beginning. Ensuring the age of superintelligence remains “human-first” will require the world to do more than just listen—it will require us to actively contest and define the future we want to inhabit.

Original Source: Industrial Policy for the Intelligence Age: Ideas to Keep People First

[David’s Note]

For over a decade, the “smart city” has been a buzzword in urban development, yet in practice, it often merely amounts to “data visualisation”. Behind those visually impressive data dashboards, day-to-day operations still rely on inefficient human approvals, inter-departmental buck-passing, and protracted budget cycles. This week, we bring you our curated selection of McKinsey & Company’s latest heavyweight, cutting-edge report: How AI-native public infrastructure changes how cities operate.

The report hits the nail on the head: the true revolution lies not in the sheer number of added Internet of Things (IoT) devices, but in where “intelligence” is situated within the system. As urban infrastructure crosses into the “AI-native” era, the city will cease to be a sluggish bureaucracy. Instead, it will transform into a colossal, distributed computing platform, equipped with millisecond-level autonomous, closed-loop capabilities of “perception, decision, and execution”. This article will fundamentally overturn your traditional understanding of urban governance.

▍ Core Insights & Key Takeaways at a Glance

1. A Core Paradigm Shift: From “Observation and Reporting” to “Decision and Execution”

Traditional smart city technology stops at the “observation” level: systems identify problems, and humans solve them. In contrast, the “AI-native city” achieves a fundamental structural leap—within explicit policy and regulatory guardrails, the system begins to autonomously make decisions and execute them. The urban control loop shifts from monthly reviews to “millisecond-level” continuous execution. The city begins to behave much like modern software: versioned, observable, testable, and capable of autonomous operation.

2. Redefining the Five Key Characteristics of an AI-Native City

• Operating like a distributed computing system: Bidding farewell to rigid, siloed, monolithic systems. Subsystems like transport, power, and water management operate independently yet coordinate seamlessly, much like “microservices” in a cloud architecture. Localised failures (e.g., congestion on a specific road) are automatically isolated and dynamically rerouted by the system, rather than triggering city-wide paralysis.
• High-resolution sensing as the “urban nervous system”: Shifting from periodic manual inspections to high-density continuous perception via edge computing. The system can proactively identify stress signals in physical assets before a water pipe bursts or a transformer fails, transforming “reactive maintenance” into “predictive maintenance”.
• Real-time data fabric replacing static dashboards: Data is no longer a dead report for post-event analysis, but a live signal that triggers immediate action. Governance models shift from asking, “What went wrong last month?” to “What is changing right now, and how should the system automatically respond?”
• Digital twins upgraded to “real-time operational consoles”: Digital twins are no longer merely 3D visual models, but operational stress-testing tools. Before taking action in the physical world, the system can simulate responses to torrential rain or sudden crowd surges in a virtual environment, automatically executing the optimal solution via control interfaces once found.
• AI transitioning from “providing recommendations” to “direct execution”: Within predefined safety thresholds, reinforcement learning models directly take over traffic light coordination, power grid routing, or waste collection routes. Meanwhile, generative AI fully automates the approval of routine administrative permits, allowing civil servants to focus solely on handling exceptional edge cases.

3. Evolutionary Pathways and Underlying Foundations

The report forecasts that the urban transition will unfold across three phases: awareness, predictive control, and ultimately, conditional autonomy. To achieve this, cities must build entirely new foundational capabilities, including a unified API backbone, edge computing near assets, event-stream architectures, and machine learning (ML) governance mechanisms to prevent model drift and algorithmic bias.

▍ Editor’s Deep Dive: An Ethical AI Perspective

Towards the end of the report, McKinsey wisely notes: in domains with high normative, political, or ethical stakes—such as policing, welfare eligibility, and urban planning—AI must solely augment human judgement (human-in-the-loop) and never entirely replace it.

From the perspective of Ethical AI, the “software-ification” and “hyper-automation” of urban infrastructure is a double-edged sword. We must remain vigilant regarding three major potential risks:

• Algorithmic Bias and Spatial Justice: When AI assumes full control of traffic light coordination or power grid load balancing, what is the underlying “objective function” being optimised? If, in order to ease congestion on major thoroughfares, the algorithm automatically diverts traffic, noise, and exhaust fumes towards marginalised communities in lower-income areas, this fully automated “efficiency enhancement” is essentially exacerbating social inequality systematically. AI-native cities require not only an “API-first” approach but also a “Fairness by Design” ethos woven into their very architecture.

• “Black Box” Governance and Algorithmic Accountability: When urban systems make millisecond decisions to restrict water pressure, sever services, or alter public transport routes, traditional channels for citizen appeals will be rendered entirely ineffective. If an automatically executed decision leads to property damage or safety incidents for citizens, who bears the ultimate responsibility, given that the decision-making process is an algorithmic black box? We must establish highly interpretable “AI decision logs” to safeguard the public’s right to information and intervention.

• System Vulnerability and Human Resilience: Cities heavily reliant on sensor networks and cloud-edge computing will be exceptionally vulnerable to complex cyberattacks or city-wide blackouts caused by extreme natural disasters. Whilst pursuing “technological redundancy”, cities must also retain “social redundancy”—namely, physical fallback mechanisms that can seamlessly revert to human control at a moment’s notice.

Summary:

The zenith of a truly great AI-native city isn’t making citizens feel the “coolness” of technology, but rather ensuring that urban maladies like power cuts, congestion, and flooding “never happen” through imperceptible intelligence. Yet, in the pursuit of this ultimate efficiency, we must hardcode ethical guardrails into the city’s foundational infrastructure. Algorithms may lack warmth, but urban governance must resolutely uphold a human-centric baseline. A city is, first and foremost, a habitat for humanity; only secondarily is it a colossal computing platform.

Original Source: “How AI-native public infrastructure changes how cities operate” via AI-Native Cities

[David’s Note]

SaaStr founder Jason Lemkin once defined a golden ratio for software: buy 90% off the shelf, and build only the 10% that is truly unique. Yet, as we progress through 2026, the meteoric rise of “Vibe Coding”—is shifting the tectonic plates of procurement. In this edition, we explore why 80/20 is the new efficiency frontier: when AI can manifest a functional tool in minutes, paying a steep “Enterprise Tax” for generic SaaS may no longer be the most prudent move.

Core Value Logic: The Shift from 90/10 to 80/20

As AI capabilities accelerate, the “Build Boundary” of the modern enterprise is expanding:

• The Classic 90/10 Rule: Historically, building bespoke software was so prohibitively expensive that it was reserved for the 10% of “mission-critical, no-alternative” features. The rest was bought to avoid engineering agony.
• The Optimised 80/20 Strategy: In 2026, AI has lowered build-costs so significantly that firms are reclaiming an additional 10% of their stack. This second 10% consists of “fringe” tools (e.g., bespoke middleware, internal portals) where SaaS was previously too rigid or overpriced. The shift to 20% build is a strategic play for hyper-customisation and cost-efficiency.
• The Infrastructure Hard-Line: Despite the 20% build-surge, 80% should still be bought. This core 80% represents security, API resilience, and regulatory compliance (e.g. SOC2). AI is not yet capable of “vibing” its way through rigorous architectural standards and long-term stability.

Ethical AI Commentary: Decentralised Power vs. Missing Accountability

The expansion of Vibe Coding from 10% to 20% introduces new governance challenges:

• Code Sovereignty & Transparency: As the build-ratio increases, so does the volume of “black-box” code within the firm. If AI embeds latent bias within this 20%, who is held accountable for the output?

• Shadow IT & Privacy: The ease of Vibe Coding empowers employees to bypass IT procurement. This risks the creation of unvetted tools handling sensitive customer data, creating a potential “compliance vacuum.”

The Verdict: AI’s evolution allows us to move from 90/10 to 80/20 with confidence, but that relinquished 10% of SaaS budget must be reinvested into AI architectural auditing.

Original Source: “Dear SaaStr: When Should I Vibe-Code an App vs Buy One?” via SaaStr

[David’s Note] For too long, the discourse surrounding Artificial Intelligence has oscillated between technophilic utopia and Luddite dread. However, Anthropic’s recent research on Labour Market Impacts provides a sobering, empirical corrective. By shifting the unit of analysis from “jobs” to “tasks”, this report unveils a nuanced architectural shift in how value is created—and compensated—in the age of Large Language Models (LLMs). This week, we go beyond the headlines to explore the structural metamorphosis of the modern workforce.

Key Insights: Three Pillars of the New Economic Reality

The study moves beyond speculative forecasting, offering a granular look at how LLMs intersect with thousands of professional tasks.

1. The Erosion of the ‘Cognitive Moat’

In a departure from the Industrial Revolution, where mechanisation replaced physical toil, the current AI wave is “top-heavy“. The report highlights:

• High-wage, cognitive-intensive roles (such as legal counsel, financial analysis, and software engineering) exhibit the highest “task exposure.”
• Traditional professional barriers—once fortified by years of specialised education—are being bypassed by the generalised reasoning capabilities of LLMs. In short: the more a job relies on processing information, the more vulnerable it is to disruption.

2. From Occupation to Atomic Tasks

The report argues that AI does not “swallow” jobs whole; rather, it deconstructs them.

• Most professions are “bundles” of diverse tasks. While AI may master 20–50% of these, the remaining tasks—those requiring interpersonal nuance, high-stakes moral judgement, and physical dexterity—remain firmly in the human domain.
• The Strategic Shift: The future of work lies not in “doing,” but in “orchestrating” AI-generated outputs while focusing on the non-automatable fringes of complex problem-solving.

3. The Democratisation of Skill and its Discontents

One of the most profound findings is the “levelling effect.” LLMs disproportionately benefit junior or less-experienced staff, effectively narrowing the gap between novices and experts. While this boosts overall productivity, it creates a Wage Paradox: as specialised skills become commoditised through AI, the market premium for those skills may diminish, putting downward pressure on traditional middle-class salaries.

David’s Reflection: Navigating the Shift

This research confirms that the “moat” around one’s career is no longer built on knowledge retention, but on AI-synergy. The most resilient professionals will be those who view LLMs not as a replacement, but as a co-processor—delegating the mundane to the machine to focus on the uniquely human messiness of innovation and empathy.

Ethical AI Commentary: On Agency, Equity, and the Social Contract

In light of Anthropic’s findings, we must apply an Ethical AI lens to the unfolding transition:

1. Justice in the Transitional Period If high-value tasks are automated at scale, the resulting efficiency gains must not be hoarded solely by capital owners. A “Human-Centric” transition demands that organisations take active responsibility for upskilling and reskilling programmes. We must ensure that the “productivity dividend” is shared fairly, preventing a further hollowed-out middle class.

2. The Preservation of Human Agency As we outsource cognitive tasks to models, we risk “skill atrophy.” From an ethical standpoint, we must define protected domains—specifically in law, healthcare, and public policy—where “Human-in-the-Loop” is not just a preference, but a moral and safety requirement to ensure accountability.

3. Algorithmic Bias and Opportunity If AI redefines the “ideal” way to perform a task, we must be vigilant that these models do not codify historical biases. We must ensure that “efficiency” does not become a proxy for “homogeneity,” stifling the cognitive diversity that is essential for true human progress.

Closing Thought: The disruption of the labour market is no longer a distant “if,” but an unfolding “how.” As AI masters the logic of our work, our task is to reclaim the soul of it.

For those wishing to scrutinise the empirical data, the full report is available via Anthropic’s research portal: Research: Labour Market Impacts

1. Reflection AI: The Race to Unlock Superintelligence

Reflection AI founders Misha Laskin and Ioannis Antonoglou are working to scale reinforcement learning to create an autonomous coding agent. Their journey began with groundbreaking AI advancements like AlphaGo, which demonstrated superhuman creativity in game-playing. Now, at their startup Reflection, they aim to combine the generality of language models with the capabilities of reinforcement learning to solve complex tasks, starting with autonomous coding, which they believe is key to achieving superintelligence.

Read more: https://www.sequoiacap.com/article/reflection-ai-spotlight/

2. Batteries Included, Opinions Required: The Specialization of App Gen Platforms

The article discusses the evolving landscape of AI app generation platforms, emphasizing that these platforms are not in direct competition but are instead carving out unique niches and coexisting, similar to what has been observed with foundation models. It highlights the segmentation of the market into different categories such as prototyping, personal software, and production apps, each catering to various user needs and technical expertise levels. The piece suggests that the future of app generation will resemble the current foundation model market, with multiple specialized and complementary platforms thriving in their respective areas.

Read more: https://a16z.com/specialized-app-gen-platforms/

3. 25 proven tactics to accelerate AI adoption at your company

Peter Yang interviewed leaders from six AI-forward companies to identify 25 effective strategies for boosting AI adoption within organizations. The episode offers practical advice, internal playbooks, and strategies to help teams derive value from AI tools. Key insights include steps for driving AI adoption, tracking and rewarding usage, and transforming AI power users into internal educators.

Read more: https://www.lennysnewsletter.com/p/25-proven-tactics-to-accelerate-ai-909

4. AI Will Determine The Future Of Software And Cloud Spending

Artificial intelligence (AI) is revolutionizing software development by reducing costs and enhancing productivity, with developers increasingly using AI-based solutions throughout the software development life cycle. Recent advancements in AI, such as improved reasoning and extended context length, have significantly boosted AI’s capabilities, transforming AI systems from simple chatbots to sophisticated AI agents. As AI capabilities grow and costs decrease, the software market is expected to expand rapidly, with AI-native companies leading the way in creating innovative solutions and potentially reshaping the landscape of software and cloud services.

Read more: https://www.ark-invest.com/articles/analyst-research/ai-will-determine-the-future-of-software-and-cloud-spending

That’s all for today’s Curated AI-Native Blogs and Podcasts. Join us at AI Native Foundation Membership Dashboard for the latest insights on AI Native, or follow our linkedin account at AI Native Foundation and our twitter account at AINativeF.

1. Tools vs Truth

As AI advances, the key question is how our aggregated data, which serves as our personal and organizational memory, is owned, controlled, and protected. Successful platforms like Salesforce and Github have become indispensable by evolving into systems of record, where knowledge is stored and decisions are documented. The future of AI tools lies not just in efficiency but in creating shared memory systems that transform data into enduring sources of truth and collective knowledge.

Read more: https://www.usv.com/writing/2025/07/tools-vs-truth/

2. How this PM streamlines 60k-page FDA submissions and saves millions with Claude, Streamlit, and clever AI workflows | Prerna Kaul

Prerna Kaul has developed an AI system using Claude and Streamlit to automate the creation of 60,000-page FDA submissions, reducing the process from months to minutes and saving millions of dollars. This innovation not only cuts costs but also accelerates the delivery of life-saving vaccines and treatments to those in need. Additionally, the system includes features like detecting and redacting protected health information and creating user-friendly interfaces for non-technical users.

Read more: https://www.lennysnewsletter.com/p/how-this-pm-streamlines-fda-submissions-prerna-kaul

That’s all for today’s Curated AI-Native Blogs and Podcasts. Join us at AI Native Foundation Membership Dashboard for the latest insights on AI Native, or follow our linkedin account at AI Native Foundation and our twitter account at AINativeF.

1. Introduction to AI Networking Model

The “Introduction to AI Networking Model” article on SemiAnalysis discusses a new AI Networking Model introduced by Daniel Nishball and Patrick Zhou. The article is part of a broader series of industry models and research offered by SemiAnalysis, which includes insights into the accelerator industry, AI cloud TCO, and datacenter industry. The platform also provides various tools and resources for professionals in the AI and semiconductor industries, such as a die yield calculator and GPU rental pricing information.

Read more: https://semianalysis.com/2025/07/11/introduction-to-ai-networking-model/

2. #474 – DHH: Future of Programming, AI, Ruby on Rails, Productivity & Parenting

In this episode of the Lex Fridman Podcast, Lex interviews David Heinemeier Hansson (DHH), the creator of Ruby on Rails, discussing his journey in programming, the design philosophies of Ruby and Rails, and his views on the tech industry. DHH shares his early programming challenges, his appreciation for Ruby’s aesthetics and productivity, and the significance of programmer happiness, while also addressing the complexities of modern web development and the role of AI. The conversation touches on the value of small teams, critiques the monopoly of app stores, and emphasizes balancing work with personal life, drawing from DHH’s experiences in software and race car driving.

Read more: https://podwise.ai/dashboard/episodes/4669507

That’s all for today’s Curated AI-Native Blogs and Podcasts. Join us at AI Native Foundation Membership Dashboard for the latest insights on AI Native, or follow our linkedin account at AI Native Foundation and our twitter account at AINativeF.

1. Translink AI Perspectives

Kelvin Mu traveled extensively to meet with over 30 companies, discussing the current AI landscape and sharing insights through a presentation. Key takeaways include the robust growth of AI infrastructure, the fragmentation of the model layer with new players entering the market, and the expansion of enterprise budgets for AI applications. Additionally, AI companies are receiving significant venture funding, with China emerging as a strong competitor in the AI space.

Read more: https://kelvinmu.substack.com/p/translink-ai-perspectives

2. Ben Horowitz: What Founders Must Know About AI and Crypto

In this podcast episode, Ben Horowitz, co-founder of Andreessen Horowitz, discusses the impact of AI on the future of work, emphasizing that AI should be seen as a powerful tool rather than a life form. He addresses concerns about AI causing human obsolescence, suggesting that it will create new job opportunities, particularly in manufacturing and data labeling. The conversation also highlights the importance of blockchain technology for maintaining trust and security in an AI-driven world and the need for regulatory frameworks for crypto and stablecoins.

Read more: https://podwise.ai/dashboard/episodes/4662775

3. Inside AI Policy with FAI’s Chief Economist Sam Hammond

In this episode of Generative Now, Michael Mignano interviews Sam Hammond, Chief Economist at the Foundation for American Innovation, about the intersection of AI, policy, and governance. They discuss important topics such as energy and chip production, government roles in AI development, and potential risks like copyright issues and labor disruptions, with Hammond advocating for rapid innovation and international cooperation on AI safety. Hammond also emphasizes the need for regulatory reform to keep up with technological advancements and advises AI startups to focus on creating civic tech solutions to address social challenges.

Read more: https://podwise.ai/dashboard/episodes/4662653

That’s all for today’s Curated AI-Native Blogs and Podcasts. Join us at AI Native Foundation Membership Dashboard for the latest insights on AI Native, or follow our linkedin account at AI Native Foundation and our twitter account at AINativeF.

 Background
Wayfair, a Boston-based ecommerce giant, specializes in a wide range of furniture and home decor. The company has seen a significant increase in AI applications across various departments, reflecting its strategy-driven approach to innovation.

 Challenge
The primary challenge for Wayfair was to integrate AI in ways that significantly enhance efficiency and disrupt traditional ecommerce processes, especially during a period of rapid technological advancement.

 Solution
Wayfair implemented advanced AI applications, particularly in marketing, supply chain optimization, and customer experience design, focusing on predictive modeling and cutting-edge technologies.

 Benefit
85% faster creation of new APIs, accelerating project delivery.
Drastic reduction in review time for customer feedback while improving accuracy, particularly for legal assessments.
Richer product data leading to better customer interactions and personalized experiences.

 Evaluation
Ethical AI: (7/10) Wayfair demonstrates AI use with a focus on productivity but needs stronger ethical guidelines.
AI Native: (9/10) The company excels in integrating AI across all departments, enhancing efficiency and personalization.
Application Modernization: (8/10) Generative AI is used effectively to modernize legacy systems, improving overall performance.

Statement:
1) This case is sourced from OpenAI’s official website, linked to https://openai.com/index/wayfair-fiona-tan/.
2) Evaluation results are generated by AI, lack of data support, reference learning only.

That’s all for today’s Global AI Native Industry Insights. Join us at AI Native Foundation Membership Dashboard for the latest insights on AI Native, or follow our linkedin account at AI Native Foundation and our twitter account at AINativeF.

1. CFO roundtable: AI growth, pricing, and forecasting (June 2025 Fintech Newsletter)

The June 2025 Fintech newsletter discusses how CFOs are adapting to AI’s impact on finance, focusing on new pricing models, revenue recognition, and cost management. It highlights the shift from subscription to outcome-based pricing, the need for hybrid metrics to capture true customer consumption, and the challenges of managing variable costs associated with AI models. Additionally, the newsletter emphasizes the importance of investing in AI for long-term growth and the complexities of financial forecasting in a rapidly evolving AI landscape.

Read more: https://a16z.com/newsletter/june-2025-fintech-newsletter/

2. Why I don’t think AGI is right around the corner

In a podcast episode, the speaker discusses their views on the timeline for achieving Artificial General Intelligence (AGI), expressing skepticism about its imminence due to current limitations in AI models, particularly in continuous learning. They predict that solving these limitations will lead to significant breakthroughs but expect a flawed version of continual learning to emerge first. Despite doubts about near-term advancements, the speaker acknowledges rapid AI progress and suggests tentative timelines for AI achieving human-level competence in tasks like tax preparation by 2028 and seamless learning by 2032.

Read more: https://www.dwarkesh.com/p/why-i-dont-think-agi-is-right-around

3. DeepSeek Debrief: >128 Days Later 

DeepSeek R1, a Chinese AI model, initially gained significant market share due to its low pricing, but its popularity has waned as other AI models have improved and offered better user experiences. Despite DeepSeek’s competitive pricing, its high latency and limited context window have driven users to alternative providers that offer faster response times and larger context capabilities. The focus on tokenomics, including latency, throughput, and context window, highlights the trade-offs DeepSeek makes to keep costs low, impacting its market position and user satisfaction.

Read more: https://semianalysis.com/2025/07/03/deepseek-debrief-128-days-later/

4. Julie Bornstein: Building the Future of Fashion with AI

In this episode of Generative Now, Michael Mignano interviews Julie Bornstein about her new AI-powered fashion search engine, Daydream, which aims to enhance fashion discovery through natural language queries. Julie shares insights from her experiences at Nordstrom, Sephora, Stitch Fix, and The Yes, highlighting how these roles influenced Daydream’s development and its focus on personalized shopping experiences. The discussion also covers the impact of the pandemic on The Yes, its integration into Pinterest, and the challenges and opportunities presented by large language models and trends like TikTok shop.

Read more: https://podcasters.spotify.com/pod/show/generativenow/episodes/Julie-Bornstein-Building-the-Future-of-Fashion-with-AI-e35011g

5. I’ve run 75+ businesses. Here’s why you’re probably chasing the wrong idea. | Andrew Wilkinson (co‑founder of Tiny)

Andrew Wilkinson, co-founder of Tiny, has built a portfolio of over three dozen profitable brands without venture capital, focusing on high-margin niches and leveraging AI for productivity. He emphasizes the importance of finding opportunities in overlooked industries, understanding the impact of ADHD on entrepreneurship, and the potential of AI to automate knowledge work. Despite achieving significant financial success, Wilkinson highlights that true clarity and reduced anxiety came through medication rather than wealth, and he advises hiring based on existing skills rather than potential.

Read more: https://www.lennysnewsletter.com/p/ive-run-75-businesses-andrew-wilkinson

That’s all for today’s Curated AI-Native Blogs and Podcasts. Join us at AI Native Foundation Membership Dashboard for the latest insights on AI Native, or follow our linkedin account at AI Native Foundation and our twitter account at AINativeF.