YouTube Digest

English

Based on "Robert Herjavec: The hidden reason smart people stop growing | Big Think+" from Big Think Watch the original video

Beyond the Guru: Robert Herjavec's Unconventional Truths About Mentorship and Lifelong Growth

For anyone aspiring to climb the career ladder or launch a successful venture, the quest for a mentor often feels like a non-negotiable step. We envision a sage figure, perhaps a titan of industry or a celebrity entrepreneur, who will take us under their wing and guide us to greatness. But according to Robert Herjavec, the renowned investor from Shark Tank, this common perception of mentorship is not only flawed but might be precisely why many smart people unknowingly halt their own growth.

Herjavec, frequently bombarded with requests like, "Robert, can you be my mentor?", offers a resounding "No." Not out of malice, but out of a deep understanding that the traditional view of mentorship is riddled with myths. His insights challenge us to rethink how we seek guidance and, more importantly, how we cultivate a mindset of continuous learning.

Myth #1: You Need a Celebrity Mentor

The biggest misconception, Herjavec argues, is the belief that a mentor must be a household name or a "guy from Shark Tank." This star-struck approach misses the fundamental purpose of mentorship. "The entire role of a mentor is to teach you something that helps you at this point in your career," he explains. You don't need a universally recognized leader; you need someone who possesses the specific knowledge or experience relevant to your current challenges. Learning, Herjavec emphasizes, can come from anyone, anywhere.

Myth #2: Mentorship is a Lifelong, Constant Relationship

Life is dynamic, and so too should be our sources of wisdom. Herjavec debunks the idea that mentorship is a static, enduring relationship. The advice a 23-year-old starting their career needs is vastly different from what a 45-year-old senior vice president requires. "Your mentors, like your life, will change over time," he states. This fluidity means we should embrace different mentors for different phases and needs, rather than searching for a single, all-encompassing guru.

Myth #3: Mentorship Requires a Formal Contract

Forget invisible contracts or formal agreements. Some of Herjavec's most profound mentors had no idea they were guiding him. He cites Warren Avis, the founder of Avis Rent A Car, as one such figure. "If he was still alive today and you asked him, 'Were you a mentor to Robert?' He would say, 'I have no idea what you're talking about.'"

Mentorship, Herjavec reveals, is often a product of observation, learning through "osmosis," and simply taking it all in. This informal approach is more accessible than ever before. "You can just learn, read, engage in many, many different ways, especially today with YouTube," he notes, pointing out the vast trove of knowledge available, including his own thousands of hours of video content.

The Hidden Growth Killer: Ego

The true "hidden reason smart people stop growing," according to Herjavec, isn't a lack of opportunity or talent, but something far more insidious: ego. "Ego has killed more careers than anything else in the world," he asserts. Regardless of how brilliant your ideas or how impressive your achievements, a dose of humility is essential.

"The first key to it is being open to the idea that you may not have all the answers," Herjavec advises. Confidence is a virtue, but arrogance is a barrier. If you believe the world has nothing left to teach you, you effectively shut yourself off from learning. Mentorship, at its core, is about being a constant learner. Herjavec illustrates this with a surprising anecdote: "One of the greatest lessons I ever learned was from a janitor, who had an incredible work discipline and a really fanatical drive for perfection and constant improvement." Wisdom, he reminds us, is truly all around us.

The "Red Car Theory" of Opportunity

To truly embrace learning and find mentors, one must first be open to seeing them. Herjavec shares a powerful analogy, the "red car theory," taught to him by one of his own mentors. If asked how many red cars you saw on the way to work yesterday, you likely wouldn't know. But if instructed to count them tomorrow, you would.

"Opportunity is like that, and mentorship is like that," Herjavec explains. When you are consciously open to it, actively looking for it, opportunities for learning and guidance become visible everywhere. The world doesn't change, but your perception does. So, be open, look for it, and then, crucially, "never be afraid to ask." Herjavec's general experience is that "98 percent of the people I've encountered are super nice. And if you ask for help, most of the time, people will help you."

Mastering the Mentor-Mentee Dynamic

While informal learning is vital, some direct engagement is inevitable. For those seeking to maximize a mentor-mentee relationship, Herjavec offers practical, often surprising, advice:

  1. Respect Their Time: This is paramount. Herjavec, a "very, very busy guy," makes it clear: if he has free time, he wants to spend it with his kids, "probably don't want to hang out with you." Mentors are often successful precisely because they manage their time meticulously. Understand this and make every interaction count.
  2. Know How to Communicate: Different people prefer different communication channels. Herjavec uses Mark Cuban as an example: "Mark Cuban doesn't talk on the phone ever. He hates talking on the phone. But if I emailed him at 10:30 tonight, he'd get back to me within three minutes." Herjavec himself dislikes phone calls because they disrupt his day, preferring email for its asynchronous nature. It's your responsibility to learn your mentor's preferred method.
  3. Understand Boundaries: The idea that "we're one big family" at work is often a myth. "My company was not my family. It was a business. My family was my family," Herjavec states. He advises understanding where a mentor draws the line between professional and personal life. Some are highly social; others, like Herjavec, prefer not to be called on weekends. However, he's an early riser, often up at 5 AM, and suggests that reaching out between 5 and 9 AM might be a "great time for me." The onus is on the mentee to understand the mentor's rhythm and preferences to make the most effective connection.

In essence, Herjavec's philosophy on mentorship isn't about finding a singular, famous individual to light your path. It's about cultivating an open, humble, and strategic mindset. It's about recognizing that every interaction, every observation, and every conscious effort to learn can contribute to your growth. By shedding the myths and embracing these unconventional truths, we can unlock our full potential and ensure that our learning journey never truly stops.

Based on "How Linear Turned AI Agents Into First-class Users" from Every Watch the original video

The Quiet Revolution: How Linear Turned AI Agents into First-Class Citizens

In a world clamoring for instant AI integration, where every company scrambled to affix a chatbot to its product, Linear, the esteemed project management tool, took a different path. While the market declared "SaaS is dead" and frantically chased token counts, Linear's co-founder and CEO, Ki, cultivated a patient, thoughtful approach that has now positioned his company as a quiet leader in the age of AI agents.

Linear, a name synonymous with taste, craft, and an almost whispered reverence among its early adopters, began its journey pre-AI. Long before large language models became household names, Linear earned a reputation for quality and a deliberate pace of development. The company intentionally kept its doors somewhat closed, avoiding the hyper-growth pressure to instead build something enduring. This foundational philosophy, as Ki explains, would become the very bedrock of their successful transition into the AI era.

"We just want Linear to be the best product in this category and like helping companies move work forward," Ki states, emphasizing that the core mission hasn't changed, only been amplified by AI. "Our goal was always that Linear can take more of the burden of running product teams... and let the product teams and individuals actually build the things. And now, it's also like they build it with the AI, or the AI builds it."

Understanding Before Acting: Navigating the AI Wave

When the first wave of AI hype hit, many companies rushed to integrate chatbots, often without a clear use case. Linear, with its design-first philosophy, resisted this urge. "I come from a design background," Ki explains. "A lot of times my way of approaching things is first I'm trying to understand them... what happens in the tech world a lot of times is people don't try to understand things. They often like jump into the 'oh I can do this so I'll do it now' but like did you think should you do it or does it actually help you?"

This critical self-reflection led Linear to experiment internally with chatbots, only to find them "not really that useful." The crucial question became: "What is the workflow where you would actually need this or use this?"

Instead of chasing trends, Linear spent years understanding potential AI workflows. This led to a prescient insight: "I don't think there's going to be one agent but everyone will have many agents, and companies will build their own agents." Recognizing this multi-agent future, Linear released an open agent platform with robust documentation, allowing external coding agents – including OpenAI's Codex and homegrown solutions from customers like Coinbase – to integrate seamlessly. Linear positioned itself not as the sole agent, but as "a system for guiding the agents and building this context."

Beyond Vanity Metrics: The True Measure of AI Success

The host noted a common market sentiment: "The stock market thinks that SaaS is dead." This narrative often pushes companies to demonstrate AI integration at any cost, leading to what Ki calls "vanity metrics."

"The biggest like vanity metric is like how much of your code is agent written or how many PRs are you merging," Ki observes. "I think like that's like not the right metric. It's measures output, but what does that output do? Does it actually generate value? Is it improving the product?"

Linear's approach is far more nuanced. While they encourage their team – now about 120 people, half on product – to use AI tools, they emphasize classic metrics: profit, revenue, and user love. Crucially, they focus on quality. Ki challenges the industry: "Why do you even have bugs in your product? Like you should be like there's no excuse for it anymore." Linear internally enforces a "zero bugs" policy, with a one-week SLA for fixes. Here, AI agents play a vital role, performing the first pass on bug fixes, then alerting engineers for review and finalization. It’s a testament to how AI can elevate quality, not just speed.

The "Slow to Decide, Fast to Execute" Philosophy

Linear's adoption of AI tools internally reflects a core philosophy: "I don't want the problem finding to be fast. Like you should take the time to find the right problem and like the right approach for the problem and then once you decide that then you can go faster on it."

This means AI is not used to rush the initial conceptualization, but to accelerate execution once a clear direction is set.

This strategic application of AI ensures that speed serves thoughtful decision-making, rather than replacing it.

From Hub to Core: The Strategic Evolution of Linear Agent

Initially, Linear thrived as the central hub for external AI agents. The host even mused, "This is the perfect business for this era because it's still SaaS... you're the one who has the sort of sticky interface... but you don't have to pay for any of the actual tokens."

However, Linear recognized a limitation: when they weren't in control of the agents, they couldn't fully optimize the end-to-end workflow or implement their own innovative ideas. This led to a significant strategic shift: the development of Linear Agent.

"One of the reasons we are doing this coding agent is like we actually think we see this like a lot more smoother end-to-end workflow," Ki explains. The Linear Agent, with its inherent context of an organization's work, can understand and act more naturally than external agents that require explicit prompting. It can turn a task into an issue, start writing code, show diffs, and facilitate reviews, all within Linear's environment. This aims to create a "product context or the product memory platform" rather than a generic agent platform.

This move does introduce a new business model consideration: token costs. While basic Q&A functionality will be included, coding agents will likely operate on a usage-based billing model. It's a calculated risk, prioritizing deeper integration and a superior user experience over maintaining a purely token-cost-free model.

A Glimpse into the Future

Ki offered a preview of Linear Agent in action:

These examples highlight how Linear Agent leverages the platform's inherent context, allowing for more natural, powerful interactions that streamline complex product development.

The Enduring Value of Human Judgment

Linear's journey into AI is a masterclass in thoughtful adaptation. It demonstrates that in an era of rapid technological change, core values like patience, quality, and a deep understanding of user needs remain paramount. By moving beyond superficial integrations and focusing on how AI can genuinely enhance human creativity and efficiency, Linear is not just surviving the "AI revolution" but actively shaping its most productive future.

As Ki concludes, the goal isn't just more output, but meaningful output. "We shouldn't be tied into the past experience, like the past product we have, but like see like what the future product should be." In a world where AI can build anything, Linear is ensuring that we first build the right things, with clarity, context, and unwavering commitment to quality.

Based on "23 AI Trends keeping me up at night" from Greg Isenberg Watch the original video

The AI Awakening: 23 Trends Keeping Greg Isenberg Up at Night

The world of artificial intelligence is moving at a dizzying pace, transforming industries and redefining what's possible. For entrepreneur and investor Greg Isenberg, this isn't just a technological shift; it's a profound re-architecture of business, creativity, and daily life – a phenomenon so potent it keeps him awake, churning with both excitement and a healthy dose of apprehension.

In a recent deep dive, Isenberg outlined 23 AI trends that dominate his thoughts, presenting a compelling vision of the opportunities, the looming challenges, and the radical shifts awaiting those ready to build in this new era. It’s a call to action, a warning, and a roadmap for navigating the most asymmetric building window of our time.

The Dawn of Hyper-Speed Entrepreneurship: Your Company, Today.

Forget the months-long process of traditional startup incubation. Isenberg envisions a world where the "one-hour company stack" is not just a dream, but a reality. "You grab an idea, you vibe code something, you build a landing page, you add a Stripe, and you can get first customers," he exclaims, highlighting the mind-blowing speed at which ideas can now materialize into revenue-generating entities.

The traditional timeline of building a company – months for development, a year for first revenue – is obsolete. In 2026, Isenberg predicts, you could have an idea by 9 AM, a product built by 9:45 AM, first customer by 10 AM, and be iterating by lunch. This isn't "vibe coded slop," he asserts, but the result of powerful agent engineering platforms like Claude Code, Google AI Studio, and Code Interpreter, which enable comprehensive, rapid development. The caveat? You still need an audience or distribution, a challenge AI is also poised to help solve.

This acceleration leads to the rise of "ambient businesses" – autonomous entities that run with zero or very low daily human input. Imagine agents monitoring markets, identifying opportunities, executing trades, and handling customer service, allowing founders to check in only every few days. Isenberg believes these autonomous businesses will soon generate seven or eight figures, driven by "the arrow of progress" pushing us towards self-steering, agent-driven enterprises.

The Agent Economy: A New Digital Frontier

The evolution of the digital landscape is clear: from the "App Store Era" (2009-2015) where humans operated apps, to the "API Economy" (2015-2024) where developers wired systems together. Now, Isenberg declares, we are entering the "Agent Economy" (2025-2030). This is a world where AI agents discover and hire other agents on the fly, dissolving fixed tasks and creating entirely new marketplaces.

This vision sparks a fascinating startup idea: "The Glassdoor of AI agents." Just as platforms like Glassdoor rate human employees and companies, a similar system will be needed to evaluate the reputation and efficacy of AI agents. Isenberg points to a Gartner stat predicting 20% of commerce by 2030 will be agent-to-agent, machine-to-machine. With 31,000 agent skills already on marketplaces (albeit many of them "garbage"), the opportunity to build high-quality skills and agents, from CEO agents to dev agents to sales agents, is massive. He cites open-source technologies like Paperclip, which allow agents to spin up subtasks and shut them down when complete, as a glimpse into this serverless, agent-orchestrated future.

The Vertical AI Gold Rush: Bigger Than SaaS

YC (Y Combinator) predicts over 300 unicorns in vertical AI this decade, a testament to the immense opportunity in specialized niches. Isenberg draws a sharp contrast between "Vertical SaaS" and "Vertical AI." While Vertical SaaS captures a fraction of IT spend, selling licenses for tools humans operate, Vertical AI taps directly into a company's labor P&L.

"You're building basically an agent as a software because you're doing that thing that companies are hiring human beings to do," Isenberg explains. This means the total addressable market for Vertical AI is an order of magnitude larger than Vertical SaaS, as it replaces headcount rather than just augmenting it. Outcomes are sold, agents do the work, and the potential for billion-dollar businesses is significantly higher.

Where are these "boring goldmine verticals"? Think industries still bogged down by phone calls, faxes, and outdated processes: insurance (with its 30-year actuary tables), legal, logistics, elder care, government, accounting, and construction. The key, Isenberg advises, is to pick a sub-niche within these boring categories, ideally one with less red tape and competition.

The Shift to Outcome-Based Pricing: Paying for Results

This agent-driven paradigm also heralds a fundamental shift in pricing models. The old "per-seat" licensing model, prevalent in SaaS, is dying. Isenberg attributes the recent stock market corrections for SaaS companies partly to this, as investors fear both reduced seat counts and the ease of "vibe coding" solutions.

The evolution moved from per-seat to usage-based, and now, rapidly towards "outcome-based" or "pay-per-result" pricing. Gartner predicts 40% of enterprise SaaS will shift to outcome-based pricing by 2030. Companies like Zendesk are already embracing this, and 83% of AI-native SaaS have made the switch.

Isenberg believes someone will build a billion-dollar business simply by converting legacy SaaS companies to outcome pricing. But even more compelling, he argues, is the opportunity to build outcome-based startups from scratch. "Why even help them when you could be incubating yourself?" he challenges, emphasizing the first-mover advantage and inherent appeal of paying only for delivered results.

The SaaS Graveyard and the Scarcity Flip

Not all software will survive this AI revolution. Isenberg foresees a "SaaS graveyard" for generic tools:

What will survive? Vertical workflow tools that pivot to agent companies, infrastructure providers, and data moats.

This leads to a profound "scarcity flip." AI commoditizes generic content, basic design, data entry, and routine analysis. What becomes scarce and premium?

Isenberg even envisions luxury brands adopting "No AI Involved" certification labels, akin to "organic" for food, to signify premium, human-made quality. The "premium stack" will range from "human-made" (most premium) to "AI-assisted but human-led" (human taste with AI speed) to "fully AI service" (commodity, racing to zero pricing). This explains his interest in incubating "IRL stuff" – karaoke bars, escape rooms, immersive theater – where human presence and experience are paramount.

Redefining the Founder and Team: The Director and the Ghost Crew

The role of the founder is also evolving. Gone is the sole focus on "founder-market fit." Isenberg introduces "founder-agent fit": "Can you orchestrate a fleet of agents towards your goal?" He compares the modern founder to a film director – not holding the camera, acting, or writing the score, but getting the best performances out of their "actors," who are now machines. The ability to manage and optimize agents for a particular niche becomes an unfair advantage.

This leads to the concept of the "ghost team org chart." In the future, a company's "About Us" page might feature a couple of human founders alongside a roster of AI agents: sales agents, content agents, customer support agents. These agents might even have names, personalities, images, and the ability to video chat or send voice notes, blurring the lines between human and machine colleagues. This vision excites Isenberg, suggesting a future with more holding companies owning multiple AI-native agent businesses, run by these ghost teams.

The classic "1000 true fans" concept also gets an AI-era update: it's now "100 true fans." With agents drastically cutting costs, 100 customers paying even $50 a month (for a total of $60,000 profit for a single person running with agents) can constitute a real business. This "micro-monopoly math" allows for the incubation of multiple profitable ventures, especially for those adept at building media and distribution.

The Shadows of Progress: Agent Attack Surface

Amidst the opportunities, Isenberg doesn't shy away from the darker side. The "agent attack surface" is a significant concern. Prompt injections, poison context windows, malicious service providers, agent-to-agent manipulation, and compromised training data pose serious cybersecurity risks. "I would be lying to you if I said this didn't freak me out, that bad things are going to happen," he admits, noting that Palo Alto Networks has already documented real-world agent injection attacks.

He contrasts agent injection with traditional phishing. While phishing tricked humans into clicking bad links, targeting email inboxes and relying on human judgment as defense, agent injection tricks AI agents via hidden instructions, targeting context windows and web content. The agent's autonomy becomes the vulnerability, and the potential for damage, especially when agents have system access and make autonomous decisions, is "far bigger than phishing."

This necessitates a new form of "digital hygiene": the "agent permission stack." Just as we review app access, we'll need to regularly cleanse and review what our agents can access (files, emails, bank accounts), remember (conversations, personal data), do (send emails, make purchases, modify code), and share (with other agents or third parties). This is a nascent but critical area for new cybersecurity startups.

Seizing the Asymmetric Opportunity: Build Now

Isenberg stresses that we are in an "asymmetric window" for builders. The build cost is effectively zero, niches are wide open, and audiences are underpriced. He warns that this won't last forever: "I think that there's 12 months where competition starts catching up. Some of the best niches get claimed. Some of the tools get crowded. I think there's probably 24 months where the window narrows."

His advice is clear: "Things are not settling down. This is the new normal." The opportunity is asymmetric because with just an API key, some prompts, a tweet, and a small niche audience (100-5000), one can create a 24/7 business with 60-95% margins, compounding distribution, and zero or few employees. "This is the most asymmetric time to be building a startup," he asserts.

Finally, Isenberg advocates for "building in public," despite warnings about inviting competition. He believes the benefits outweigh the cons, especially if your audience is also your customer. Sharing what you're building, allowing the community to vote on features, and shipping updates rapidly (in days, not months) fosters trust and compounds distribution. This "flywheel" creates a strong moat, particularly in a world where "forking a business" (copying others quickly) will become common.

In an era defined by rapid change and unprecedented potential, Greg Isenberg's insights serve as both a thrilling invitation and a sober reminder. The future of AI is not just coming; it's already here, keeping some awake with its boundless possibilities and pressing challenges. The question is, what will you build with it?

Based on ""We're Not Writing Code by Hand Anymore. That's Over." | Owen Jennings & David Haber - The a16z Show" from a16z Watch the original video

The Code Whisperers: How Block's Radical AI Overhaul Declared Manual Programming 'Over' and Reshaped its Workforce

In the fast-evolving landscape of technology, seismic shifts often happen quietly, then erupt into public view. For Block (formerly Square), the parent company behind Square, Cash App, and Afterpay, that eruption came in the form of a dramatic 40% reduction in force. But this wasn't just another layoff in a challenging economic climate; it was a strategic, forward-looking decision, explicitly driven by the profound and immediate impact of artificial intelligence.

Owen Jennings, Business Lead at Block, who previously helmed Cash App during its critical scaling period, shared a candid and revealing account of this transformation on the a16z Show. His message was unequivocal: the era of writing code by hand is over, and the very structure of tech companies is being fundamentally rewritten by AI.

The Binary Shift: When Everything Changed

For decades, there was a clear, almost sacred correlation between the number of people at a company and its output. More engineers, designers, and product managers typically meant more features, more products, and more growth. According to Jennings, that correlation "basically broke" in the first week of December.

"What we were seeing," Jennings explained, "is that one or two engineers, or a designer and an engineer who was on the tools, is able to be 10, 20, 100x more productive."

This wasn't a gradual evolution; it was a "binary change." Block had been investing in "agentic development" – systems where AI agents assist or even autonomously perform development tasks – for two to three years, launching their first agent harness, "Goose," in early 2024. They saw "meaningful progress" throughout 2024 and 2025 (in the transcript, this appears to be a misstatement, likely referring to an earlier period given the late November/early December "binary change" context).

However, late November and the first week of December brought a sudden, qualitative leap. The emergence of foundational models like Opus 46 and Codex 53 fundamentally shifted the capabilities of these AI tools. Initially, these models were "pretty good at writing code especially for new ventures and kind of like green space." But almost overnight, they became "incredibly capable working with existing complex code bases."

This meant that AI could now not only generate new code but also effectively navigate and contribute to the dense, intricate web of legacy systems that characterize large, established software products. "We're not writing code by hand anymore. That's over. That's done," Jennings declared, underscoring the finality of this paradigm shift within Block.

Block's Bold Restructuring: A Response to AI, Not Overhiring

The decision to reduce Block's workforce by over 40% was not a mere response to the "ZIRP (Zero Interest Rate Policy) hangover" or overhiring from the 2021 boom, as some might assume. Jennings was adamant: "If you thought it was like croft and bloat... then like this riff would have accrued to the operational teams... Those were really, really meaningful cuts on the development side."

The cuts were disproportionately larger on the development side – engineering, product, and design – precisely because these were the areas where AI tools had delivered the most dramatic productivity gains. Sales and account management teams, for instance, saw "fairly de minimis" reductions.

The executive team, led by CEO Jack Dorsey, spent the first quarter of the year grappling with the implications of this new reality. "What does this mean fundamentally? What does this mean in terms of how we're going to build products, how we're going to build software for customers, and then also how we're going to run a company?" These were the questions that ultimately led to the substantial restructuring.

Executing with Principle and Empathy

Such a drastic change, even if strategically motivated, carries significant human impact. Block approached the reduction with core principles:

  1. Reliability (P00): Ensuring no outages or service disruptions.
  2. Customer Trust & Compliance: Navigating complex regulatory environments without compromise (e.g., compliance teams were largely untouched).
  3. Durable Growth: Continuing to build on the roadmap and make long-term bets, albeit with smaller teams.

From an execution standpoint, Block aimed for transparency and generosity. They offered substantial severance packages, didn't instantly cut technology access, and held an all-hands meeting where Jack Dorsey and the executive team explained the decision directly to the entire company.

Culturally, the impact was profound. While the initial days brought "shock" and "ambiguity," the company quickly adapted. A key outcome was a massive reduction in meetings, "probably like 70 or 80%," freeing up time for "building and work." Weekly all-hands with Jack Dorsey became a new norm, fostering direct communication and a sense of shared purpose. The new Block is "smaller, leaner, [with] fewer layers, larger spans," and a renewed focus on building.

Inside Block's AI-Powered Engine: Goose, G2, and Builderbot

Block's internal AI infrastructure is the engine driving this transformation. At its core is Goose, an "agent harness" that is model-agnostic. This means Block can swap in different foundational models (Anthropic, OpenAI, open-source models – they use over 120 internally) depending on the specific task. Goose provides the framework for AI agents to operate.

Building on Goose, Block developed G2, an internal "agentic operating system." G2 allows anyone within the company to automate "any deterministic workflow," empowering employees across various functions.

Perhaps most illustrative of the shift in software development is Builderbot. This tool "autonomously merging PRs and actually like building features to 100%." While it often completes 85-90% of a complex feature, with a human providing the final 10% context and polish, its impact is undeniable. The ability to go from "idea to like this is in the hands of 100,000 or a million customers has been compressed massively since December."

A New Way of Working

The organizational structure has been completely reshaped. Gone are the classic hierarchical, functional teams of eight server engineers, four client engineers, a PM, and a designer working linearly. Instead, Block now operates with "small squads" of one to six people, offering "way more flexibility and fluidity." These squads can fluidly move between products, iterating rapidly.

Layers have been dramatically reduced, by "50 or 60% on the development side," meaning information flows more freely. The workflow itself has become non-linear: instead of sequentially submitting a Pull Request (PR), getting a review, and making changes, engineers now manage "14 agents who are building PRs on my behalf right now." The human role shifts to "context switching between all of those," checking on work, nudging it, and committing it.

This agent-driven workflow extends beyond engineers to product managers and growth marketers, who now manage "countless agents running right now." The role of the human is increasingly to oversee, guide, and refine the output of these intelligent systems.

Beyond core development, AI is automating "anytime there's a deterministic workflow." This includes customer support, where chatbots and AI phone support handle the majority of inquiries. In product operations, risk operations, and compliance operations, AI models are increasingly making decisions. While a "human in the loop" is currently critical, Jennings predicts that "over time it's like pretty obvious that these systems are just going to be so much better than like having a thousand humans who are doing that work."

Generative UI: Reshaping Products and User Experience

Block's AI transformation isn't just internal; it's profoundly changing the products customers interact with. The company, which has functionalized its structure to build a unified financial platform across Square, Cash App, and Afterpay, is leveraging its agentic infrastructure to create dynamic, personalized user experiences.

"For the past 10 or 15 years, everyone's used to a static UI, a rigid UI," Jennings noted. "That's going to fundamentally change in the next like six months. Generative UI is here."

This means your Cash App, for example, will look "really different" from someone else's, not just through basic personalization but through on-the-fly generation. Moneybot, Block's "CFO in your pocket" within Cash App, can generate charts and visualizations dynamically based on a user's query ("How have I been spending my money?"). These visualizations are not hardcoded; they are generated in real-time by the AI.

On the Square side, ManagerBot allows a multi-location restaurant owner to "build me an app where I can manage scheduling for these two locations and like automatically fire off texts... to my employees." The app itself, and its specific look and feel, are generated on demand and are not part of the application's source code.

This generative UI offers "way more control" and promises "higher engagement" and "better product." However, it presents new challenges, particularly in quality assurance (QA) for "non-deterministic outputs." Block is also heavily investing in "proactive intelligence," understanding that customers may not know the right prompts. The AI needs to anticipate needs and offer relevant insights and actions.

The New Moat: Understanding the Unseen

In a world where code can be generated by AI, traditional moats like network effects, regulatory licenses, and even hardware (which is harder to "vibe code") remain important in the near term. But in the long term, Jennings argues, defensibility will hinge on something far more abstract: "the extent to which the company understands something that is pretty hard for other companies to understand."

Block is building towards becoming an "intelligent system itself," sitting on a "rich data and deep insight" about how sellers and buyers participate in the economy. The key question for any company will be: "how quickly can you iterate to improve that understanding over time?"

This involves building "world models" internally and externally – understanding customers, but also understanding how Block itself operates. Companies will need a "markdown file" of who they are, their values, metrics, and priorities, combined with a feedback loop from their unique "signal" (that deep understanding) and powerful AI tools like Builderbot or Clawed Code.

This loop – "This is what I'm seeing. This is what's happening. Great. This is our markdown file for Block... and then you have a gentic system, so you can just build stuff" – will iterate at unprecedented speeds. What once took months to build a feature with humans, now takes a week or two. In the future, this loop could run "hundreds, thousands of times a day," with humans acting more as editors or nudgers.

The ultimate warning is stark: "If your answer to that [what you deeply understand] is 'I don't know,' then you maybe could get vibe coded away."

A Glimpse into the Future

Block's radical transformation offers a compelling glimpse into the future of the tech industry. While the immediate implication might be fewer engineers, designers, and PMs for a given product or roadmap, Jennings suggests a "Jevons paradox" effect. The increased efficiency might lead to a "superset of things that can be built," potentially resulting in more tech companies or the expansion of development into entirely new sectors.

The message is clear: AI is not merely a tool for optimization; it's a force for fundamental restructuring. Companies that embrace this shift, understand its implications, and build their operations and products around intelligent systems will lead the next era of innovation. For Block, the future of programming is not about writing code by hand; it's about orchestrating intelligence.

Based on "Today’s Mission to the Moon" from New York Times Podcasts Watch the original video

Our Next Giant Leap: Why Humanity is Returning to the Moon

Nearly six decades after the United States made history by landing men on the moon, a new era of lunar exploration is dawning. This time, the mission isn't just about planting a flag; it's about building a future, a permanent human presence beyond Earth. Today marks a pivotal moment in this ambitious endeavor, as four astronauts prepare to embark on a journey that will swing them around the moon and back, a critical test for humanity's sustained return to our celestial neighbor.

The mission, known as Artemis 2, is the latest step in NASA's grand plan to not only put humans back on the moon but to establish a long-term base there. As Ken Chang, a science reporter for The New York Times, explains, the Artemis program is designed in carefully orchestrated phases, acknowledging the immense challenges of lunar exploration.

"The goal this time in this program, which is called Artemis, is to get people back to the moon, but also stay there this time," Chang notes. "And so this program is broken up into pieces because it's really hard to land on the moon. You don't want to do all the hard things all at once."

The journey began with Artemis 1 in 2022, an uncrewed flight that sent the Orion spacecraft around the moon for several weeks, successfully proving the basic machinery. Now, Artemis 2 is taking the next monumental leap: sending a crew of four astronauts to test the critical life support systems. Their primary objective, as Chang starkly puts it, is "to not die." This mission will ensure that the spacecraft can sustain human life for an extended period, producing carbon dioxide, water, and other waste that must be managed. If successful, Artemis 2 will pave the way for Artemis 3, which aims to land astronauts on the lunar surface within a few years.

Beyond the Footprint: A Vision for a Permanent Lunar Outpost

But why return to the moon now, after so many decades? The rationale extends far beyond simply revisiting a past achievement. NASA's vision for the Artemis program is to establish a sustainable presence, a permanent lunar base that will serve multiple purposes.

Initially, this base would function as a scientific research station, akin to the outposts currently operating in Antarctica. Scientists could study the moon's unique environment, its geological history, and its potential resources in unprecedented detail.

Beyond pure science, the moon base holds significant commercial and economic potential. There's growing interest in harnessing the moon's resources. One of the most intriguing prospects is the mining of Helium-3, a light isotope of helium that is exceptionally rare on Earth but more prevalent on the moon's surface. Helium-3 is considered a potential fuel for future fusion reactors, offering a clean and abundant energy source. It's also believed to be useful for advanced quantum computers, which could revolutionize fields like artificial intelligence. With Helium-3 currently valued at roughly $3 million a pound on Earth, even a small quantity from the moon could yield substantial profits, attracting private companies already planning such ventures.

The moon also presents unique opportunities for groundbreaking scientific endeavors. Imagine a giant radio telescope built on the far side of the moon. This shielded location, protected by the moon's entire mass from Earth's constant radio noise – generated by TV, cell phones, and podcasts – would offer an unparalleled vantage point to "listen to the universe." Such a telescope could detect faint signals from just after the Big Bang billions of years ago, offering humanity a chance to "hear the ancient sounds of really the dawn of time itself," the echoes of creation itself.

Furthermore, the moon is envisioned as a testing ground for future missions to Mars. Many of the challenges of living and working on the moon – developing nuclear power plants, constructing habitats, and refining life support systems – are directly applicable to a Mars mission. The moon's gravity and environmental conditions provide a crucial proving ground before humanity attempts the even more ambitious journey to the Red Planet.

Finally, there's the romantic notion of humanity spreading out into the solar system, transcending our confinement to a single planet. A moon colony represents the first tangible step towards becoming a multi-planet species. Underlying all these aspirations is a compelling geopolitical competition, particularly with China. Being the first to establish a significant presence on the moon means setting the rules for space commerce, securing prime locations, and controlling potential resources. "Being first means being the one who's in charge," Chang emphasizes.

The Crew: Four Pioneers on a 10-Day Odyssey

At the heart of this monumental mission are four individuals, carrying the weight of these dreams on their shoulders. Their 10-day journey is a meticulously planned sequence of events, designed to push the boundaries of human spaceflight.

The commander of Artemis 2 is Reid Wiseman, a former fighter pilot in the U.S. Navy who served two deployments in the Middle East. Before his assignment to this historic mission, he led NASA's astronaut office. Wiseman reflects on the profound nature of their journey: "I was outside last night and I was looking up at the moon thinking the next time I see this sight there's a good chance that we will have been around the far side and back." A poignant detail about Wiseman is that his wife passed away a few years ago, meaning he will be leaving his daughters behind for this critical 10-day period.

Joining him is Victor Glover, also a former naval aviator. Glover made history as the first Black man to serve an extended stay on the International Space Station, and with Artemis 2, he will become the first Black man to travel to the moon. He speaks to the collaborative spirit of the mission: "10 years from now when the next challenging thing happens, maybe we can look back on this and go, 'Hey guys, remember we did that. We had our own wing shot, remember?' And it was global. It was international. And we did it together."

Christina Koch, an electrical engineer, holds the record for the longest single space flight by a woman, at 328 days. She worked on NASA missions on the ground before being selected as an astronaut. Koch emphasizes the collective nature of their achievements: "All these things that we talk about first are really not about any one individual's accomplishment, but more about celebrating where we are at."

Rounding out the crew is Jeremy Hansen, a Canadian astronaut, making him the first non-American to venture into deep space. Hansen possesses a dry sense of humor, quipping that "If something goes wrong on this mission, then NASA can blame Canada." More seriously, he notes, "We don't know the ripple effects of what we're about to do, but we can do it well to the best of our ability and have joy while we're doing it. And hopefully that will make a small contribution."

Their journey begins with a long day even before launch, waking eight hours prior. After donning their space suits, they'll be driven to the launchpad, ascend a massive tower, and enter the Orion spacecraft. They'll spend four hours inside before the countdown reaches zero, and just eight minutes later, they'll be in space.

The first two days in orbit will be spent meticulously checking every system on Orion through two looping orbits around Earth. Only once everything is confirmed to be working perfectly will they fire the engines to propel them towards the moon, a quarter-million-mile journey that will take approximately four days. Life inside the Orion capsule, which offers about as much space as two minivans, will be a unique experience, made more comfortable by the ability to float and utilize all available volume.

Day six marks the mission's climax: the lunar flyby. As they approach the moon, it will appear roughly the size of a basketball held at arm's length. The moon's gravity will then gracefully pull them around its far side, an event that will cause a temporary loss of radio communication with Earth for about 40 minutes. During this silent period, the astronauts will make critical observations, seeing parts of the far side of the moon that no human eyes have ever witnessed in actual daylight. Previous missions passed over these areas in darkness, leaving them unseen by human eyes in their full illumination. The trajectory is so precise that the moon's gravity will effectively "sling them around and throw them right back toward Earth without them doing much of anything," a testament to the power of celestial mechanics.

After the lunar flyby, the astronauts will endure another three days of travel back to Earth – what might be the "boring part of the trip," as Chang describes it. On the final day, Earth's gravity will guide them into the atmosphere for a splashdown in the Pacific Ocean off San Diego. They'll be recovered by ship, flown to shore for medical checks, and finally return to Houston, bringing Artemis 2 to a triumphant close.

A Shifting Landscape: NASA and the New Space Age

Artemis 2 represents a significant moment for NASA, a "triumph for them in that the old ways of doing things succeeded," as Chang notes. This mission is largely an "old school NASA" production, with the agency designing and operating the entire spacecraft. However, it also marks the end of an era.

For the subsequent mission, Artemis 3, the landscape of space exploration will dramatically shift. Private companies like SpaceX, founded by Elon Musk, and Blue Origin, started by Jeff Bezos, will become integral partners. These "new space" companies are developing the lunar landers that will eventually transport astronauts to the moon's surface. Artemis 2, in this sense, is the last major mission where NASA operates independently from these burgeoning private space giants, paving the way for a collaborative future where government agencies and commercial enterprises work hand-in-hand to explore the cosmos.

A Message of Hope in Turbulent Times

The launch of Artemis 2 comes at a time of global turbulence, reminiscent of the era when humanity first ventured to the moon. In 1968, a year marked by assassinations, social unrest, and war, the Apollo 8 mission provided a profound moment of unity and hope. That Christmas Eve, as the three Apollo 8 astronauts orbited the moon, they took turns reading from the Book of Genesis, sharing a message of creation and peace with a world in turmoil.

"In the beginning, God created the heaven and the earth," they broadcast. "And God said, 'Let there be light.' And there was light." This simple act resonated deeply, with some even suggesting that Apollo 8 "saved 1968."

While we don't know what message the Artemis 2 crew might share upon their return, the imagery alone – of Earth seen from a quarter-million miles away – has the potential to offer a similar moment of calm and perspective. It's a reminder that despite our differences, we are all inhabitants of the same fragile planet, a "good earth" suspended in the vastness of space.

Artemis 2 is more than just a mission; it's a testament to human ingenuity, resilience, and our innate drive to explore. It's a blend of scientific ambition, economic opportunity, geopolitical strategy, and the enduring romantic quest to reach for the stars. As the four astronauts prepare for their journey, they carry with them not just the hopes of a nation, but the aspirations of humanity for a future among the stars.