Here’s the thing: your mood isn’t something that just happens to you. It’s something you can influence, if you’re willing to pay attention.

The first step is awareness, not control

Most people assume bad moods are caused by outside events. The difficult client, the failed campaign, the deal that fell through. And sure, those things trigger it. But staying in a bad mood? That’s on you.

The Stoics had a version of this. You can’t control what happens. You can only control your response to it. That’s easy to say and genuinely hard to do, especially when you’re running a business and the stakes feel real every single day.

But here’s a practical place to start: for the next two weeks, log your mood like you’d log your energy levels or your workouts. Not to judge yourself, just to notice. What triggers it? Time of day? Certain people? Certain types of tasks? You might be surprised how predictable your bad moods actually are once you start tracking them.

Research from the American Psychological Association consistently shows that emotional awareness, simply naming and recognising an emotional state, is one of the most effective ways to reduce its intensity. You don’t need to fix the mood immediately. You just need to see it clearly.

The “do nothing” move that actually works

When something happens that puts you in a bad mood, your first instinct is almost always the wrong one.

Someone sends you a passive-aggressive message. A team member drops the ball publicly. A competitor takes a dig at you online. Your gut says: respond now.

Don’t.

Take ten seconds. Look at what’s actually at stake. If your integrity or an important relationship is on the line, yes, act. But if it’s noise dressed up as urgency? Let it pass. The response you write in a bad mood is almost never the one you’d be proud of an hour later.

This isn’t about being passive. It’s about being deliberate. The operators who manage their mood well aren’t the ones who feel less. They’re the ones who pause long enough to choose their response.

You can generate a good mood, not just wait for one

This is the part most people miss. Managing your mood isn’t only about avoiding the bad stuff. It’s about actively creating the conditions for a better state.

A few things that actually work: physical movement (even a 10-minute walk shifts things meaningfully), deliberate context switching, and what some psychologists call “broaden-and-build,” stepping back to see the bigger picture when you’re locked in a spiral.

The goal isn’t to never feel bad. Bad moods carry real information. They tell you something is off, something needs attention, a boundary has been crossed. The goal is to not stay there longer than necessary.

Set a rough internal rule for yourself: 10 minutes of sitting with it is fair. After that, move. Not by suppressing it, but by redirecting your attention to something that matters.

A study published in Cognition and Emotion found that people who use reappraisal strategies, actively reframing a situation rather than just venting or suppressing, report higher wellbeing and better decision-making over time. As an operator, your decision-making is your product. Your mood is directly upstream of it.

🚀 YC Startup Spotlight

Focus Buddy (YC W25)

What it does: An AI productivity coach that stays on a call with you while you work, helping you stay focused, recover from distractions, and avoid burnout. It detects when you’re drifting and intervenes in real time through voice.

Use it when: You’re working through a high-stakes focused block and need something that holds you accountable without the friction of another app to manage.

What triggers your bad mood most often at work? Hit reply, I’m genuinely curious.

Until next time, take care of yourself.

Connect with me on LinkedIn

Rayn

I know it sounds almost too simple. But spend time with an 80-year-old who’s still building things, still learning, still picking up the phone, and you’ll feel the difference immediately. There’s an aliveness to them that has nothing to do with their energy levels and everything to do with their orientation toward the world.

Curiosity isn’t soft. It’s a strategy.

There’s a tendency to treat curiosity as a personality trait, something you either have or you don’t. But that’s not how it works.

Curiosity is a practice, a daily habit. And like any practice, it atrophies when you don’t use it.

This is what tends to kill curiosity: busyness. When you’re fully maxed out (back-to-back meetings, a full inbox, a to-do list that breeds overnight) there’s no space left to wonder about anything. You’re in execution mode constantly. And execution mode, by design, only looks at what’s directly ahead.

The problem isn’t that busy people are incurious. The problem is that busyness creates an illusion of forward motion while actually shrinking your world. You’re moving fast inside a smaller and smaller circle.

The operators and entrepreneurs who tend to thrive over decades, not just for a year or a funding cycle, are almost always the ones who protect some space for curiosity. They read things outside their category. They ask questions they don’t need the answers to. They follow threads that go nowhere obvious.

What “staying curious” actually looks like in practice

It doesn’t mean reading 50 books a year or signing up for online courses you never finish.

It looks more like this: the 82-year-old entrepreneur who flew to Amsterdam by himself, still working on new tech, still genuinely excited about what’s next. The investor in his seventies who has never wanted to retire, not because he needs the money, but because stepping back feels like giving up the thing he loves most.

What these people share isn’t a schedule hack. It’s a refusal to close off. They treat every new conversation, every new problem, every new industry development as something worth understanding, not just something to react to.

For operators, this matters more than it might seem. The market shifts. Tools change. What worked last year stops working. The ones who adapt fastest aren’t always the most experienced. They’re often the most curious. They didn’t stop asking “why does this work?” when they found something that worked.

A World Economic Forum report on future skills consistently puts curiosity and continuous learning near the top of the traits employers and leaders will need through 2030 and beyond. This isn’t a soft skill anymore. It’s becoming a core competitive advantage.

The relationship between curiosity and longevity

There’s actual science behind this, not just anecdote.

Research on cognitive health suggests that people who remain mentally engaged, pursuing new knowledge, new skills, new social connections, show slower cognitive decline as they age. The brain, much like a muscle, responds to use. Curiosity, in this sense, is a form of maintenance.

But more immediately practical for founders and operators: curiosity is what keeps you from becoming the person who’s always fighting yesterday’s battle. Every industry eventually produces people who “know how things work” and then, at some point, can’t figure out why things stopped working. That’s usually not a market problem. It’s a curiosity problem.

“Everything is noise except for doing the things that you’re curious about.”

That line stuck with me. Because it reframes curiosity not as a nice-to-have, but as a filter. Not everything deserves your attention. But the things you’re genuinely curious about? Those deserve it completely.

🚀 YC Startup Spotlight

Read Bean (YC W24)

What it does: Helps intermediate and advanced language learners improve reading skills using real-world content (articles, news, native texts) adapted to your level. It measures where you are, transforms content into bite-sized lessons, and reinforces vocabulary over time.

Use it when: You’re trying to learn a language as an adult and the usual apps feel too juvenile. Real content, adapted to you. That’s the curiosity-friendly way to learn.

What’s one thing you’ve been genuinely curious about lately that has nothing to do with your immediate work? I’d love to hear it. Hit reply.

Until next time, take care of yourself.

Connect with me on LinkedIn

Rayn

That’s not me being optimistic, I run a software company too. That’s Mike Cannon-Brooks — co-founder of Atlassian, a company doing north of $5 billion in cloud revenue and accelerating responding to the loudest narrative in tech right now. And honestly, he’s right. Software isn’t going anywhere, well I hope so. But a lot of software companies are about to have a very rough few years.

This is what is actually happening, and what operators need to understand about which side of the line they’re on.

The “Death of SaaS” panic is a Category Error

Every few weeks, someone on X declares that SaaS is dead, AI ate it, and we should all pivot to agent wrappers. Then the same week, a company like Harvey raises $200 million at an $11 billion valuation. Harvey is… software. Growing 300% year-over-year.

So which is it?

On a recent episode of 20VC, Cannon-Brooks broke it down simply: will every SaaS company survive the next five to ten years? Absolutely not. Will a lot of them continue to grow and prosper? Absolutely. Is that any different from the last decade? No.

He pulled up Atlassian’s old competitor lists from 2005, 2010, 2015. Most of those companies don’t exist anymore — merged, acquired, or gone. And Atlassian has a completely new set of competitors today. That’s just how the technology industry works.

The difference now is the rate of change. In a normal cycle, maybe 5-10% of companies fail each year. During an architectural shift — cloud, mobile, and now AI — that death rate compresses into a two-year window where it could hit 50%. Not every company dies, but the ones that aren’t rebuilding fast enough absolutely will.

Software customers are going to buy more in the future. The question is whether they’ll buy it from you.

The input-output framework that changes everything

Here’s the most useful mental model from the conversation, and it comes directly from how Cannon-Brooks thinks about AI’s impact across different business functions.

Some domains are input-constrained. Legal, customer support, HR — there’s a fixed number of problems coming in. Your lawyers can’t create more legal problems just because they got faster. Your customers ask 100 questions a day, and if you have twice as many customers, that becomes 200. The input is based on some external ratio.

Other domains are output-constrained. Engineering, product development, creative work — the roadmap is never finished. You can always build more. The constraint is how much you can produce, not how much demand exists.

Here’s why this matters for operators:

In input-constrained domains, AI mostly cuts costs. You answer the same questions with fewer people. That’s a real, measurable ROI — easy to sell, easy to prove. But in year two and three, those savings are baked in. You need to keep delivering new value or you’re just a line item that shrinks.

In output-constrained domains, AI expands what’s possible. Engineering teams aren’t getting smaller — they’re shipping more. According to Cannon-Brooks, Atlassian’s 10,000 R&D people are building things faster and better than ever. Some of their AI features now run at a thousandth of the cost compared to when they launched. The features still work. The margin just went up.

If you’re building a company, ask yourself: am I selling into an input-constrained or output-constrained domain? The answer changes your entire strategy.

The Danger: Being third in a subsegment

The customer support space tells this story perfectly. Sierra just crossed $150 million in ARR. Fourteen new companies in the space have raised over $100 million in the last two years. And they’re competing against ServiceNow, Salesforce, Atlassian, Zendesk, and Intercom.

The market is real. The money is flowing. But as the 20VC crew pointed out, the worst position to be in is third place in a subsegment. You’re too small to dominate, too big to pivot, and the market leader can decide to annex your space anytime they want.

This is happening everywhere. VCs are piling into consensus winners because it’s the venture capital equivalent of “you can’t get fired for buying IBM.” Of newly minted unicorns in Q1 of last year, 40% already had one or more up rounds by Q4. The money says: once you’re winning, we’ll double and triple down.

For operators, the lesson is uncomfortable but clear. If you’re not the obvious leader in your space, you need a different playbook. Adjacent territory. A wedge nobody else owns. Or the honest conversation about whether you’re building something defensible at all.

🚀 Tools of the week

• 🚀 Cloudera: Your weekly playbook for navigating agent chaos and turning AI disruption into a scalable advantage. Listen to The AI Forecast podcast.

• ✂️ EZTrimmer: Trim, split, and export videos in seconds with AI.

• ✅ Notion AI: Use AI directly inside Notion to boost your productivity.

• 📽️ Kubrix: Create cinema-quality stunning AI-generated videos in seconds.

• 🤖 Lorka: Chat with the top AI models in one place.

🧪 YC Startup Spotlight

Abundant (YC W25)
• Provides on-demand human operators who step in when AI agents fail in production, ensuring 100% reliability while generating training data for improvement.

• Use when: You’re deploying AI agents in production and need a safety net for edge cases, especially in regulated industries like healthcare, legal, or finance. abundant.ai

Until next time, keep building, keep questioning, and don’t let the noise drown out what actually matters.

- Rayn

Bill Gross, the founder of Idealab, studied hundreds of startups and found that timing accounted for 42% of the difference between success and failure. The idea — the thing most founders obsess over — came in third.

Sam Altman puts it differently: wait to have a good idea before you start a startup. If you start without one and just cast around, you’ll be under pressure to make something up. And made-up ideas don’t survive contact with reality.

So how do you find the right idea at the right time? Here’s what the data and the best founders actually tell us.

Presented by Scriptbee

If you’re building a startup in 2026, here’s a question worth asking: when someone asks ChatGPT or Perplexity for a solution in your category, does your brand show up?

Most founders have no idea. Scriptbee gives you the answer. Track your brand’s visibility across AI search engines, see which queries trigger mentions of your product, and optimise your content to win more AI-generated recommendations. One B2B SaaS company grew their AI visibility by 40% in 60 days.

WHAT MATTERS TODAY

1. The clone trap kills more startups than bad execution

Here’s the pattern. Something works — a new app, a business model, a category — and thousands of people rush to copy it. It happened with social networks after Facebook. It happened with photo-sharing apps after Instagram. And it’s happening right now with AI wrappers.

Altman shares a revealing stat from when he joined the board of Helion, a nuclear fusion company: that same year, roughly 10,000 photo-related startups launched alongside it. One nuclear fusion company. Ten thousand photo apps.

The odds were obvious, but nobody was paying attention. Meanwhile, 42% of startups fail because they build products nobody actually wants — the single biggest killer. Not bad code. Not weak marketing. Building something the market never asked for.

The takeaway for operators: if you can name ten companies doing the same thing you’re doing, that’s not validation — that’s a warning sign.

2. The best ideas come from personal pain, not market research decks

Strip away the mythology and most transformational companies started with someone solving their own problem. Altman is emphatic about this — a very high percentage of the best startups trace back to founders scratching their own itch.

Airbnb’s founders needed to pay rent. They noticed hotels in San Francisco were sold out during a design conference, so they put air mattresses in their living room. That wasn’t a market analysis. That was survival. Stripe’s founders were building websites and kept running into the same nightmare: payments were absurdly hard to integrate. They didn’t conduct a survey. They felt the pain every single day.

This matters because personal experience gives you an unfair advantage. You understand the nuance. You know what “good enough” actually looks like to the end user. You don’t need a focus group to tell you the problem is real — you’ve lived it.

The data backs this up. First-time founders have an 18% success rate. Those who’ve previously failed do slightly better at 20%. But founders with a successful exit? They hit 30%. Why? Because they’ve developed the pattern recognition to spot real problems versus imagined ones.

3. Timing is the invisible advantage nobody talks about enough

Here’s where Altman’s thinking and Gross’s data converge beautifully. Both argue that great companies cluster around major technological waves — not because founders coordinate, but because the wave creates new problems that need solving.

The internet wave in the mid-to-late 90s produced Amazon, Google, and Yahoo. The mobile wave after the iPhone in 2007-2008 produced Uber, Airbnb, Instagram, and WhatsApp. Each cluster appeared in a tight window, right after a major platform shift.

Altman’s insight is that you don’t need to create the wave — that takes too many resources for a startup. What you can do is notice it early, while others still think it’s a toy, and build on top of it.

The current wave? AI. But here’s the nuance that separates winners from the wreckage: almost $200 billion was poured into AI startups in 2025 alone, yet 95% of generative AI pilot projects in enterprises failed to deliver measurable ROI. The money is flowing, but the real problems — the ones where AI genuinely changes the outcome — are still being discovered.

The areas still underbuilt right now? Product discovery (figuring out what to build, not just coding it). AI agent reliability in production environments. AI-native companies that don’t just sell tools to incumbents, but replace the incumbents entirely — what YC calls “full-stack AI.”

The best founders don’t create waves. They notice them while everyone else still thinks they’re a toy.

QUICK WINS

🎯 Test your idea with one question: “Is this my problem?” If you haven’t personally felt the pain, you’re guessing. Guessing gets expensive fast.

🔧 Use the “10,000 photo apps” filter: Before building, search Product Hunt, YC’s directory, and Crunchbase for competitors. If you find dozens doing the same thing, either find a genuinely different angle or move on.

💡 Time your wave, not your launch date: 10% of startups fail specifically because of mistiming — launching too early or too late. The question isn’t “is this a good idea?” It’s “is the market ready for this right now?”

🏄 Look where the money’s going, then go adjacent: The crowded plays in AI are chatbots and copilots. The underbuilt opportunities are in infrastructure, reliability, and vertical AI for industries that haven’t been touched yet — construction, logistics, legal, agriculture.

📊 Talk to 20 potential users before writing a single line of code: Startups with mentors are 3x more likely to succeed. But even before mentors, the cheapest insurance is simply asking people whether they’d pay for what you’re building.

Don’t start a startup without a good idea. Start with the problem — the idea will follow.

Looking to make sharper decisions faster?

If today’s piece hit a nerve; if you’re wrestling with which idea to pursue, or whether the timing is right. I work 1:1 with founders and operators to stress-test ideas and build systems that stick. Book a session: https://topmate.io/chalkmeout

🚀 YC Startup Spotlight

Conveo (YC S24)

An AI-powered qualitative research platform that conducts and analyses voice and video interviews at scale — delivering customer insights in hours, not weeks. Trusted by Unilever, Google, and Sanofi, Conveo raised $5.3M in seed funding to help teams understand what customers actually want before building.

Use when: You’re validating a product idea or testing positioning and need deep customer insights without the 6-week agency timeline. conveo.ai

That’s all for today. If something here resonated, forward this to a founder friend who’s still searching for “the idea.” Sometimes the nudge matters more than the advice.

Connect with me on LinkedIn

Until next time,
Rayn

OpenAI started putting ads inside ChatGPT. A Harvard study confirmed that AI is making us work more, not less. And Amazon quietly started building a marketplace where publishers can sell their content directly to AI companies.

If you zoom out, the pattern is obvious: AI is no longer a tool you choose to use. It’s becoming the environment you operate in. The ads you see. The pace you work at. The way your content gets monetised. All of it is shifting — fast.

Here’s what actually matters for operators this week.

PRESENTED BY Scriptbee

If ChatGPT is now running ads inside conversations, the question for your brand isn’t just “are we advertising there?” — it’s “are we even showing up when someone asks AI about our category?”

Scriptbee tracks your brand’s visibility across ChatGPT, Perplexity, Claude, and Gemini. See which queries trigger your citations, where competitors are outranking you, and what to fix. One B2B SaaS company grew AI visibility by 40% in 60 days.

→ See where your brand stands in AI search

WHAT MATTERS TODAY

1. ChatGPT now has ads — and it changes the game for AI search

OpenAI officially flipped the switch on Monday. Ads are now live for Free and Go tier users in the US — appearing at the bottom of ChatGPT responses, clearly labelled and visually separated from the AI’s answers.

The timing is worth noting. This launched literally one day after Anthropic ran Super Bowl ads mocking the entire concept of AI chatbot advertising. OpenAI’s response? Press ahead anyway.

Here’s what operators should pay attention to: ads are matched based on your conversation topic, past chats, and previous ad interactions. If someone asks ChatGPT for project management tools and your competitor has bought that placement — they’re showing up, not you.

The operator takeaway: ChatGPT isn’t just a productivity tool anymore. It’s becoming an advertising platform. And if your brand isn’t visible in AI conversations organically or through paid placement, you’re ceding territory to competitors who are.

2. AI doesn’t save you time — it makes you do more

Researchers from UC Berkeley spent eight months embedded inside a 200-person tech company, studying how AI tools changed daily work. Their findings, published in Harvard Business Review, are a reality check for every founder selling “10x productivity” narratives.

What they found: employees didn’t use AI to finish early. They used it to take on more. Product managers started coding. Researchers took on engineering tasks. People fed work to AI during meetings, breaks, and lunch. Nobody asked them to — they just did.

The researchers identified a dangerous pattern they call “workload creep.” Burnout was reported by 62% of associates and entry-level workers, compared to just 38% of C-suite leaders. As one engineer put it: “You don’t work less. You just work the same amount or even more.”

The operator takeaway: if you’re rolling out AI tools across your team, don’t just measure output. Measure intensity. Build in what the researchers call “intentional pauses” — structured breaks, sequenced workflows, and protected time for human collaboration. The companies that win with AI won’t be the ones that adopt fastest. They’ll be the ones that adopt most thoughtfully.

3. Amazon and Microsoft are building AI content marketplaces — and it matters for every creator

According to The Information and TechCrunch, Amazon is in talks with publishers about launching a marketplace where content owners can license their material directly to AI companies. AWS has already circulated slides grouping the marketplace alongside its core AI tools like Bedrock.

This follows Microsoft’s Publisher Content Marketplace launch last week — a licensing hub that lets publishers set usage terms for AI companies building with their content.

Why this matters for operators: the AI industry has spent the last two years scraping content and negotiating one-off deals with major publishers. Now, we’re moving toward structured marketplaces with usage-based pricing. If you’re creating content — whether that’s a newsletter, a blog, or a knowledge base — your material is becoming a tradeable asset in the AI supply chain.

The operator takeaway: start thinking about your content as a product, not just a marketing channel. The companies and creators who have well-structured, high-quality content libraries will have leverage as these marketplaces scale. This is the beginning of a content licensing economy that could reshape how publishers monetise in the AI era.

QUICK WINS

🎯 ChatGPT ad controls exist: If you’re a Free or Go user, you can dismiss ads, turn off personalisation, and delete your ad data with one tap in Settings. Worth doing now before the defaults settle in.

🔧 NotebookLM now connects to Gemini: You can link your NotebookLM notebooks directly to Gemini’s chat box. Click the (+) icon, select your notebook, and get contextualised answers from your own curated sources. Handy for operators sitting on research libraries.

💡 The “AI practice” framework: The Harvard researchers recommend three things for teams using AI — intentional pauses (scheduled breaks), sequencing (coordinated workflow phases), and human grounding (protected time for dialogue). Simple, but most companies aren’t doing any of them.

🚀 Content licensing is the next SEO: Just as brands once optimised for Google, smart operators will soon optimise for AI training marketplaces. If you’re producing original content at scale, keep your archives clean, well-structured, and rights-clear.

📊 The burnout gap is real: 62% of entry-level workers reported AI-related burnout vs. 38% of executives. If you’re a founder or manager, this gap should worry you — the people doing the heaviest AI-augmented work are the most at risk.

FAQ

Will ChatGPT ads affect the quality of responses? OpenAI says no — ads are displayed separately at the bottom of responses and don’t influence the AI’s answers. Paid tiers (Plus, Pro, Business, Enterprise) won’t see ads at all. But the precedent is set, and sceptics are watching closely.

Does the Harvard study mean AI tools aren’t worth using? Not at all. The research shows AI does increase output. The problem is that without intentional boundaries, workers absorb the gains as extra workload rather than reclaiming time. The fix isn’t less AI — it’s better AI adoption practices.

Should I care about AI content marketplaces if I’m not a publisher? Yes. If you create any content — blog posts, newsletters, documentation, courses — you’re a publisher. These marketplaces could eventually let you license your content to AI companies. Getting your content house in order now is a smart long-term move.

“You don’t work less. You just work the same amount or even more.” — Engineer quoted in the Harvard Business Review study on AI and work intensification

The companies that win with AI won’t be the ones that adopt fastest. They’ll be the ones that adopt most thoughtfully.

Feeling the AI intensity yourself?

You’re not imagining it. The pace is real, and most operators are figuring this out alone. I work 1:1 with founders and operators to build sustainable systems — for AI adoption, for team management, for cutting through the noise.

Book a session: https://topmate.io/chalkmeout

🚀 YC Startup Spotlight

Basepilot (YC W24) • Builds AI coworkers that automate repetitive browser-based tasks — data entry, form filling, lead sourcing — by learning from simple demonstrations. No code required, and teams report saving around 30% of their time weekly on mundane back-office work.

• Use when: Your team is drowning in repetitive workflows across web apps and you want to free up capacity for higher-value work — without hiring or building custom automation.

basepilot.com

What’s one thing you’ve changed about how your team uses AI after noticing the workload creep? Hit reply — I’d genuinely love to hear what’s working (and what’s not).

That’s it for this week’s deep dive.

The AI landscape is moving fast, but that doesn’t mean you have to sprint every second. Sometimes the smartest operator move is to slow down just enough to see where the ground is shifting.

If something here landed for you, forward this to one person who needs to read it. And if you haven’t already, connect with me on LinkedIn — that’s where I share the stuff that doesn’t always make it into the newsletter.

Until next time,

Rayn

They’re not bad at building them. They’re just... slow.

You spend three hours updating your runway forecast. Another two debugging a circular reference. Half a day rebuilding last quarter’s actuals because you changed how you track CAC.

And by the time you’re done, the numbers you wanted to test are already outdated.

I just tried Claude in Excel (Anthropic’s new add-in), and honestly? It solves this exact problem. You chat with it like you’d chat with a CFO who actually gets spreadsheets.

Ask it to explain a formula. Update assumptions without breaking dependencies. Build a three-statement model from scratch. All without leaving Excel.

This isn’t about replacing your finance knowledge. It’s about getting back the 10 hours a week you spend wrestling with formulas instead of analysing scenarios.

This issue is brought to you by Scroll

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Thousands of companies rely on Scroll to automate knowledge workflows across documentation, RFPs, and agency work. One founder cut their team’s search time by 60%. Check out Scroll.ai.

What is Claude in Excel?

It’s an add-in that puts Anthropic’s Claude Opus 4.5 model directly in your Excel sidebar. You chat with it like you’d chat with a CFO who actually understands spreadsheets.

Ask questions about specific cells. Update assumptions while preserving formulas. Debug errors. Build new models from templates. All without leaving Excel.

According to recent analysis, automation and AI-powered templates have reduced the average time to construct comprehensive financial models by 40% over the last five years. Claude in Excel accelerates this even further by turning natural language into executable spreadsheet actions.

Why this matters for startup founders

Most financial modeling tools fall into two camps: either they’re too basic (glorified calculators) or they require an accounting degree to operate.

Claude in Excel sits in the middle. You can ask “What assumptions drive the revenue forecast in Q3?” and get an answer with citations to the exact cells. Or say “Increase growth rate by 2% and show the impact on terminal value” and watch it update everything correctly.

Research shows that 61% of venture capital firms now explicitly require startups to include financial models within their pitch materials. But here’s the catch: investors want dynamic models with scenario toggles and KPI dashboards, not static spreadsheets.

Claude in Excel lets you build these faster. Much faster.

What it actually does well

Reads and explains complex models: Ask about specific formulas or entire sections. Claude navigates multiple tabs and provides answers with direct citations to cells.

Updates assumptions safely: Change values while maintaining all formula dependencies. Every change gets highlighted with explanations.

Builds and fills templates: Create three-statement models from scratch or populate existing templates with data from uploaded documents (like 10-Ks).

Debugs errors: Identifies sources of #REF!, #VALUE!, or circular references and suggests fixes that maintain spreadsheet integrity.

The catch? It’s currently in beta and only available to Pro, Max, Team, and Enterprise users. Chat history doesn’t save between sessions. And it doesn’t handle advanced features like macros, VBA, or conditional formatting.

What founders are actually doing with it

Based on the documentation and early use cases, founders are using Claude in Excel for three main workflows:

Scenario analysis: “Add a downside case assuming revenue drops 15%” or “Create base, bull, and bear scenarios with different growth assumptions.”

Investor prep: “Build a 3-statement financial model for a SaaS company” or “Fill this DCF template with data from the uploaded 10-K.”

Error hunting: “Find all circular references in this workbook” or “Why is this NPV calculation returning #VALUE?”

One operator shared that they used Claude to build a full financial model for a Series A pitch in under four hours – something that would normally take two days with a CFO consultant.

The bigger shift happening here

This isn’t just about Excel getting smarter. It’s about the entire financial modeling workflow changing.

Startups invest in finance early to make better strategic decisions as they scale and to raise capital on stronger terms. But most founders waste hours each week just maintaining their models instead of using them to make decisions.

CB Insights recently reported that fintech startups are increasingly deploying AI agents to handle complex, repetitive financial tasks. Claude in Excel is part of this shift: moving from spreadsheets as databases to spreadsheets as interfaces.

You don’t need to know every formula. You need to know what questions to ask.

Does Claude understand financial modeling conventions?
Yes, Claude is trained to recognise common financial modeling patterns and industry-standard calculations. However, always verify outputs match your specific methodologies.

Can I use this with sensitive data?
Claude works within your existing security framework. For highly sensitive or regulated data, follow your organisation’s data handling policies.

What if Claude makes a mistake?
Claude highlights all changes to your workbook. Review carefully before saving. You can always undo using Excel’s standard undo function.

Will chat history be saved?
Not currently. Each session starts fresh. However, Anthropic is working to support this in future versions.

“A financial model is the numerical expression of your startup’s goals. The best models aren’t ‘right’ – but the differences between projections and actual results drive insight into your company’s potential.”

Building financial models is one thing. Using them to make better decisions is another.

If you’re struggling with investor-ready projections, scenario planning, or simply making sense of your unit economics, let’s talk. I work with founders on everything from first-time fundraising decks to Series A financial models that actually close rounds.

Book a session at topmate.io/chalkmeout and we’ll build a financial model that tells your story.

YC Startup Spotlight

Crunched (YC F25) – The Excel AI analyst for power users in consulting, investment banking, and private equity. It error-checks workbooks end-to-end, builds models from scratch, and handles grunt work like extracting and linking data from information memorandums to template models. Use it when you need to scale financial modeling operations without hiring junior analysts.

What’s the most time-consuming part of your financial modeling workflow right now?

I’m fascinated by how AI is changing the operator toolkit. Not in a “this will replace everyone” way, but in a “this frees you up to think instead of execute” way.

Claude in Excel is exactly that. It’s not going to build your strategy. But it might save you 10 hours a week so you can actually think about the strategy.

And honestly? That’s the unlock.

Let me know if you try it. I’m curious what workflows you build.

Until next,
Rayn

Connect with me on LinkedIn if you want to chat about financial modeling, fundraising, or just startup operations in general.

They set goals. They feel inspired. And by February, it’s over.

Not because they’re lazy. Not because they lack discipline. But because they’re building on sand instead of structure.

Research from the European Journal of Personality confirms what you’ve probably felt: setting attainable goals is more closely linked to well-being than simply aiming for lofty or vague aspirations BetterUp. The problem isn’t your ambition, it’s your architecture.

This week, I’m sharing 20 evidence-backed techniques to make 2026 different. Not through willpower. Through structure.

The Four Acts of Designing Your Best Year

Most people treat goal-setting like writing a shopping list. They scribble down wishes, tape them to the fridge, and hope motivation shows up.

But motivation is unreliable. Structure is what lasts.

Here’s how to build a year that actually works.

Act One: Get Clear on What Actually Matters

Before you set a single goal, run a regret review. Not to punish yourself but to learn.

Pick your biggest regret from 2025. Just one. The thing that still bugs you when you’re lying awake at 2am. Maybe you didn’t protect your creative time. Maybe you drifted from people who mattered. Maybe you spent 300 hours scrolling instead of building.

Write it down. Then write the lesson and the plan to avoid repeating it in January.

Research on regret shows the worst response is ignoring it. The second worst is wallowing. The best? Using it as instruction.

Next, jump forward to December 31st, 2026. Close your eyes. The thing you cared about most didn’t happen. What went wrong?

This is called a pre-mortem, imagining failure before it happens so you can prevent it. If future-you failed because you never scheduled family time, block 10 sacred family days right now. If you failed because nobody held you accountable, find that person this week.

Finally, choose a theme word for 2026. Not a sentence. One word that captures who you’re becoming.

Simplify. Connect. Build. Ship.

Psychologists call this a self-cue; a simple trigger that snaps your attention back to what matters when you drift.

Act Two: Build Structure That Sets You Free

Studies show that writing down specific goals increases the likelihood of achieving them by 42% compared to simply thinking about them Bristolcreativeindustries. But writing goals isn’t enough. You need structure.

Start by protecting your first hour. Research from the University of London found that multitasking early in the day drops your cognitive performance by the equivalent of 10 IQ points. That’s the difference between great work and forgettable work.

Use that first hour for deep work. Writing. Exercise. Reading something that expands your thinking before the world shrinks it. No email. No phone. No reacting to other people’s priorities.

Then adopt the 2-minute rule: if a task takes 2 minutes or less, do it immediately. Small tasks create invisible cognitive clutter. The 2-minute rule is a fog remover.

Every Friday, run a shutdown ritual. Spend 5 minutes reviewing your week, setting your top three priorities for Monday, and blocking 60-90 minutes for something that matters. Then say out loud: “Shutdown complete.”

Sounds corny. Works brilliantly. Your brain gets cognitive closure, which means it can actually rest.

Finally, every Sunday, run a 15-minute reset. Look at what’s coming. What’s essential? What can wait? What needs breathing room?

People who wrote goals clearly and tracked progress were 33% more likely to achieve substantial outcomes Bristolcreativeindustries. The review is as important as the writing.

Act Three: Build Motivation That Lasts

Here’s where most people stumble. They rely on inspiration. But inspiration is fleeting.

Start with the 85% rule. Research from UC San Diego found that systems learn best when they’re right about 85% of the time. Not 100%, not 50% but 85%. That’s the sweet spot where you’re stretched but not snapped.

Pick one goal. Deliberately dial the difficulty so you’re succeeding 8 or 9 times out of 10. If it’s too easy, increase the challenge. If you’re failing constantly, dial back.

That uncomfortable feeling? That’s not failure. That’s the emotional signature of learning.

Next, track small wins daily. Harvard research shows people feel and perform best on days when they experience progress and even tiny progress. At the end of each day, write down three ways you moved forward. Sent that email. Read three pages. Chose the walk over the scroll.

The act itself matters more than the list. Once you see daily wins, momentum becomes hard to stop.

Act Four: Connect and Renew

Nobody sustains excellence alone.

Build a challenge network with a small group who care enough to tell you uncomfortable truths. Don’t just ask “what do you think?” Ask “what’s one thing I can do better?”

Then curate your circle. You need three people: a challenger who won’t let you coast, a cheerleader who believes in you on bad days, and a coach who’s already played the game you’re trying to play.

Sociologist Nicholas Christakis found that emotions and behaviors spread through social networks up to three degrees. Happiness spreads. So does discipline. And their opposites.

Choose carefully.

Finally, create a to-don’t list. Every quarter, ask: what’s not worth my time? A meeting that adds nothing. A committee you’ve outgrown. A project you’re keeping alive out of habit.

Subtraction frees up time and attention in ways addition never can.

“A year is a long time. Treat it as four shorter chapters each with its own focus. Four 90-day pushes will beat one vague 12-month intention every single time.”

The One Practice That Changes Everything

Here’s the final move: send 26 thank-you notes in 2026. One every two weeks.

Research on gratitude shows that written letters produce a lasting boost in happiness, reduce stress, and deepen connection. In one study, gratitude writers reported 25% more life satisfaction and even exercised an extra 90 minutes per week.

Handwritten is best. Short is fine. Simple, clear, sincere.

You’ll brighten someone’s day, strengthen a relationship, and lift your own mood every time.

Want help making this your best year yet?

These aren’t just nice ideas they’re systems. And systems beat motivation every time. If you’re serious about building structure that lasts, I work with professionals and founders who want to operate better, decide faster, and build sustainable growth.

Book a session at topmate.io/chalkmeout and let’s design your year together.

What’s one small structure you’ll build in January to make 2026 different?

Reply and let me know. I read every response. Connect with me on LinkedIn

You already have everything you need to succeed. You just need better systems to use it.

Don’t wait for motivation. Don’t rely on willpower. Build structure to protect your mornings, track your wins, choose your circle, and subtract what doesn’t serve you. The year you want is waiting on the other side of these decisions. Make them now. Make them count. Make 2026 the year everything changed.

The smartest founders right now? They’re not chasing what’s hot on Twitter. They’re finding the problems that make other founders uncomfortable. The ones that feel a bit... dangerous.

Why did Door Dash win when 12 other food delivery startups died?

You know what killed most food delivery startups in 2014? They all went “full stack” - cooking food, running kitchens, managing logistics. DoorDash said nah, we’ll just deliver. Everyone called them unambitious.

Today they’re worth $65 billion.

Here’s what’s happening in AI right now: Everyone’s hiring forward deployed engineers. Everyone’s chasing the same enterprise verticals. Everyone’s building on the same “proven” playbooks.

How do I know if my idea is contrarian or just bad? Contrarian ideas have desperate early customers. Bad ideas have interested observers. If someone’s willing to use your broken MVP today, you’re onto something.

The opportunity? Do the opposite of what everyone else is doing.

“If you only want to work on things that are hot, you’re going to find yourself working on derivative ideas with 100 competitors.”

How did a “terrible” idea become worth $7.5 billion?

Hardware startup? Strike one. Selling to local governments? Strike two. Based in Atlanta? Strike three.

Garrett Langley built Flock Safety anyway. VCs calculated the total addressable market at $60 million tops. Too small, they said. Too hard, they said.

Today Flock Safety solves 10% of all reported crime in America. Valuation: $7.5 billion.

The lesson isn’t “ignore VCs” - it’s that spreadsheet logic misses human desperation. When kids are getting kidnapped and cars are getting stolen, nobody cares about your TAM calculations. They care about catching criminals.

What if VCs won’t fund my weird idea? Perfect. Less competition. Flock Safety almost didn’t get funded. Bootstrap to early revenue, then watch VCs suddenly discover your “category.”

Which rules should you actually break?

Forget the framework everyone’s using. Here’s what actually predicts startup success:

Would you pay for this if it was illegal? Not suggesting you break laws. But Uber was technically illegal. So was Airbnb in most cities. When humans want something badly enough to risk fines, you’ve found real demand.

Are the current laws just... dumb? Taxi medallions made sense before smartphones. Securities laws make less sense for blockchain. Find regulations written for a world that no longer exists.

Does explaining your idea make you slightly uncomfortable? If everyone immediately gets it and loves it, you’re already too late. The best ideas sound dangerous at first. OpenAI was mocked by AI researchers for years. SpaceX was Elon’s “vanity project.”

Should I really ignore successful playbooks? Don’t ignore them. Invert them. If everyone’s going vertical, go horizontal. If everyone’s B2B, consider B2C. The playbook becomes the problem.

What does this mean for your AI startup?

The two-year AI gold rush window is closing. The obvious ideas are taken. But here’s what’s opening up:

Codegen replacing humans: Not augmenting. Replacing. One YC company built an AI forward deployed engineer that does installations in minutes, not weeks.

Industries everyone thinks are “too hard”: Government contracts. Healthcare compliance. Financial regulations. The moats that kept big companies safe are now attack vectors.

Business models that shouldn’t work: Compound startups are supposedly impossible. Campfire is killing NetSuite anyway by building everything at once with AI.

How do I validate fast without building everything? Sell first, build second. Get 3 customers to commit before writing code. Their desperation level tells you everything.

What’s the one assumption everyone’s making that’s wrong?

Here’s what I want you to think about: Look at your competition. What are they all doing the same? That’s your opportunity.

The next unicorn is hiding in the idea that makes everyone else nervous. Not just intellectually uncertain - actually uncomfortable. The kind where your friends at parties say “that’s a tarpit idea” or “that’ll never scale.”

What if I fail because I zigged while everyone zagged? You’ll fail faster and cheaper than the 97 companies fighting over scraps in the “obvious” market. And you’ll learn something nobody else knows.

Remember: Nine out of ten people might tell you you’re crazy. But that one person who gets it? They might be your co-founder, your first customer, or your first investor.

Ready to level up your contrarian thinking?

Building something everyone thinks is impossible? Let’s talk strategy. I work with founders navigating the space between “that’ll never work” and “why didn’t I think of that.” Book a session: https://topmate.io/chalkmeout

YC Startup Spotlight

Giga ML

AI forward deployed engineer that automates enterprise integrations. Use when you need to onboard enterprise customers in hours, not months.

-

Reply and tell me: What’s the one thing everyone in your industry believes that might be completely wrong?

Until next time, keep building what others won’t.

Narayanan

They’ve been promising to transform how we work for months now. But the reality? Most teams spent weeks wrangling fragmented tools, debugging mysterious failures, and watching their agents crash in production.

That changed this week.

OpenAI just dropped AgentKit — and honestly, it’s the first time building production-ready AI agents feels… approachable. Companies like Ramp went from blank canvas to working buyer agent in hours instead of months, slashing iteration cycles by 70%. LY Corporation built a complete multi-agent workflow in under two hours.

If you’ve been waiting for the right moment to build AI agents into your business… this might be it.

Today’s Partner: Scriptbee.ai

Scriptbee.ai helps enterprises and startups become visible in AI Search. Think of it as SEO, but for the new world where people ask AI instead of Google.

The analytics show you exactly which queries trigger your brand, where you rank, and how to grow your share of AI-generated recommendations. No black box. Just clear visibility into the fastest-growing discovery channel in tech.

What AgentKit Actually Does (And Why It Matters)

Let’s cut through the hype.

AgentKit is a complete toolkit for building, deploying, and optimizing AI agents. But the real story isn’t the tools themselves — it’s what they replace.

Before AgentKit, building an agent meant:

• Cobbling together orchestration frameworks

• Writing custom connectors for your data

• Manually reviewing thousands of trace logs to find failures

• Weeks of frontend work before you could even test with users

• Crossing your fingers every time you pushed to production

After AgentKit? Most of that goes away.

Agent Builder: Your Visual Canvas for Complex Workflows

Agent Builder provides a drag-and-drop interface for composing multi-agent workflows with version control, inline evaluation, and preview runs.

What this means for operators: You can now map out your agent’s logic visually. No more black boxes. Your product team, legal team, and engineers can all look at the same canvas and understand exactly what the agent will do.

Ramp’s team reports slashing iteration cycles by 70%, getting an agent live in two sprints rather than two quarters. That’s the difference between testing an idea this month versus next year.

ChatKit: Deploy Conversational Interfaces in Minutes

Here’s where it gets practical.

ChatKit lets you embed customisable chat-based agent experiences directly into your product, handling streaming responses, thread management, and thinking displays.

One operator at Canva put it perfectly: “We saved over two weeks building a support agent with ChatKit and integrated it in less than an hour”.

Two weeks → one hour. That math works.

Evals Platform: Finally Know If Your Agent Actually Works

This is the unglamorous part everyone avoids… until their agent hallucinates in front of a customer.

The new Evals capabilities include datasets for rapid test building, trace grading for end-to-end workflow assessments, automated prompt optimization, and support for third-party models.

Carlyle’s team reports cutting development time by over 50% and increasing agent accuracy by 30% using the evaluation platform.

Think about that: 30% more accurate agents, in half the time. That’s not incremental — that’s a different category of reliability.

The Guardrails You Didn’t Know You Needed

Guardrails provide an open-source, modular safety layer that helps protect agents against unintended or malicious behaviour, including PII masking, jailbreak detection, and other safeguards.

You know what’s more expensive than building guardrails? Explaining to your board why your agent leaked customer data.

The Apps SDK: Agents That Live Inside ChatGPT

While AgentKit helps you build agents for your own products, OpenAI also launched something else this week: Apps in ChatGPT, powered by the new Apps SDK, letting developers create conversational apps that reach over 800 million ChatGPT users.

Early partners include Booking.com, Canva, Coursera, Figma, Expedia, Spotify, and Zillow.

The interesting bit? The Apps SDK builds on the Model Context Protocol (MCP), making it an open standard that can run anywhere adopting this standard.

What this means: If you’re building B2C agent experiences, you can now distribute them where people already spend time. No need to convince users to download yet another app.

What This Changes (Practically Speaking)

Let’s be real about what just shifted.

For solo founders and small teams: You can now build and ship agent-powered features without hiring a specialist AI team. The visual tools and pre-built components lower the barrier dramatically.

For mid-sized companies: Your existing engineering team can move faster. ChatKit already powers use cases from internal knowledge assistants and onboarding guides to customer support and research agents. Pick one workflow that’s currently manual, build an agent, ship it this quarter.

For enterprises: The Connector Registry consolidates data sources across ChatGPT and the API into a single admin panel, working with pre-built connectors like Dropbox, Google Drive, SharePoint, and Microsoft Teams. Finally, governance that doesn’t require rebuilding everything.

The pattern I’m seeing: Teams that struggled for months to get one agent working are now shipping multiple agent workflows in weeks.

The Honest Challenges (Because Nothing’s Perfect)

A few things worth considering:

Learning curve still exists: Visual tools help, but you still need to understand agent architectures, evaluation metrics, and failure modes. AgentKit makes it easier, not automatic.

Costs can escalate: More agents mean more API calls. Monitor your usage carefully, especially during testing. One founder told me their eval runs alone cost $2,000 before they optimised.

Integration complexity: While ChatKit can be embedded into apps or websites and customised to match your theme, connecting your existing data sources and workflows still requires planning. It’s faster than before, but not instant.

What To Do This Week

If you’re considering AI agents (and you probably should be), here’s where to start:

1. Identify one workflow in your business that’s repetitive but requires some judgment. Customer support, lead qualification, data entry with validation — something that’s currently eating hours.

2. Test with Developer Mode — OpenAI’s documentation includes guidelines and example apps you can test using Developer Mode in ChatGPT. Try building a simple agent before committing resources.

3. Start with evaluation — Before building anything fancy, set up basic evals. Define what “good” looks like for your use case. The teams getting real value from agents aren’t the ones with the fanciest architecture — they’re the ones who can reliably measure if their agent is improving.

4. Budget for iteration — Plan for 3-4 rounds of testing before you ship. Every team I’ve talked to says their first version wasn’t good enough. The difference now is you can do those iterations in days instead of months.

The Bigger Picture

Here’s what matters beyond the tooling…

We’re moving from “can we build an AI agent?” to “how fast can we ship one that actually works?”

Since releasing the Responses API and Agents SDK in March, developers have built end-to-end agentic workflows for deep research and customer support, with Klarna handling two-thirds of all tickets with their support agent.

That’s not a demo. That’s production. At scale.

The companies winning next year won’t be the ones with the best AI strategy deck. They’ll be the ones who shipped three agent-powered workflows this quarter, learned what worked, and are shipping three more next quarter.

AgentKit just made that playbook available to everyone.

YC Startup Spotlight: Atla

If you’re building AI agents, you need to know about Atla (YC S23).

Here’s the problem they solve: Agent failures hide inside long traces and are difficult to spot at scale, with teams manually reviewing thousands of traces without clear signal.

What Atla does: Their LLM judge evaluates your agent step-by-step, uncovers error patterns across runs, and suggests specific fixes with real-time monitoring and automated error detection.

Why it matters: The gap between “my agent works in testing” and “my agent works in production” is where most projects die. Atla helps you find and fix critical failures in hours instead of days.

They’ve trained purpose-built evaluation models (Selene and Selene Mini) that have been downloaded 60,000+ times. If AgentKit is how you build agents, Atla is how you make sure they don’t break.

Worth checking out → atla-ai.com

One Question For You

What’s the one workflow in your business you’d trust an AI agent to handle tomorrow if you knew it would work reliably?

Hit reply and let me know. I’m genuinely curious what operators are thinking about here.

Let’s Keep This Going

Building AI agents is moving fast. Too fast to figure everything out alone.

If you’re navigating the shift to AI-powered operations — whether you’re building agents, evaluating tools, or just trying to figure out what matters — I’m here to help.

I work with founders, operators, and teams who are tired of hype and want practical guidance on what actually works. Real conversations about real challenges.

Book a call → topmate.io/chalkmeout

Or connect with me on LinkedIn → linkedin.com/in/chalkmeout

Always happy to chat about what you’re building.

— Narayanan

Everyone talks about Steve Jobs like he was some kind of genius who just "got it." But what if I told you his biggest secret wasn't about innovation or design?

What if it was something way simpler - and way more brutal?

The signal vs noise revelation

Through my research into successful leaders, I discovered something that completely changed how I approach my day.

Jobs had this concept he lived by: signal-to-noise ratio.

Here's how it worked:

Signal = the top 3-5 things you absolutely must get done in the next 18 hours. Not next week, not next month. Today.

Noise = literally everything else that tries to distract you from those things.

Jobs operated at an 80/20 ratio - 80% signal, 20% noise.

Most of us? We're running at 50/50 at best. And that's why we're stuck.

The uncomfortable truth about focus

I know what you're thinking - "But Rayn, what about work-life balance?"

Here's the thing Jobs understood that most people don't: there is no balance when you're building something extraordinary.

Noise includes:

• Checking your phone every 5 minutes

• That "quick" coffee chat that turns into an hour

• Scrolling through social media "for research"

• Even saying hi to friends sometimes

Sounds harsh? It is.

But look at what Jobs achieved. Look at what Elon Musk achieves running multiple companies simultaneously.

"Focus on the signal. Everything else is just noise trying to steal your future."

The people who change the world understand this at a cellular level.

How I apply this today

Every morning, I write down 3-5 things on a sticky note and put it on my mirror. These are my non-negotiables for the day.

Everything else - every email, every "urgent" request, every social media notification - I ask myself one question:

"Is this signal or is this noise?"

If it doesn't directly contribute to my 3-5 critical things, it waits.

This reminds me of conversations I have during my 1:1 coaching sessions. People often tell me they feel overwhelmed, like they're drowning in tasks. But when we break it down, 80% of what they're doing is just noise masquerading as productivity.

If you're struggling to identify your true priorities and eliminate distractions, I offer personalised guidance: https://topmate.io/chalkmeout

My top content from last week

LinkedIn: "You're studying Physics, do you really think this is a fit for you?" - My story about proving yourself regardless of your background and why the best hires aren't always the ones with the shiniest resumes.

The genius wasn't being nice

Jobs wasn't known for being friendly. He was direct, sometimes brutal, always focused.

People always ask if you need to be difficult to be successful. The answer isn't about being mean - it's about being uncompromising with your signal.

When someone tried to pull Jobs into noise, he'd say: "That's not what we're doing today."

When market research teams wanted to survey customers, he'd say: "They don't know what they want until I tell them."

Was he right? Apple became one of the most valuable companies in history.

The lesson isn't to be rude. The lesson is to protect your signal like your life depends on it.

Because honestly? Your future does depend on it.

You can always write to me by simply replying to this newsletter. I read every single email, even if I can't reply to them all. Your signal is calling - but only you can choose to answer it over the noise.

Staying focused,

Rayn

I used to think anyone could start a business if they just worked hard enough. That entrepreneurship was just about having a good idea and the guts to go for it.

Then I dug deeper into what actually separates successful entrepreneurs from everyone else, and honestly, what I discovered might surprise you.

The uncomfortable truth about entrepreneurship

Through my research, I've learned that only about one-third of people can become truly successful entrepreneurs.

I know that sounds harsh, but hear me out.

There are specific attributes that successful entrepreneurs share, and they're not what most people think. It's not about having the best idea or the most money to start with.

It comes down to three core things:

Risk tolerance - Can you handle the constant uncertainty? The sleepless nights when everything could fall apart tomorrow?

Laser focus - This is huge. Can you identify the 3-5 most critical things that need to happen today and ignore literally everything else?

Luck and timing - And yes, this matters more than we'd like to admit. Even Napoleon said his favourite generals were his lucky ones.

But here's the thing that really got me thinking...

The freedom question

The real question isn't whether you can become an entrepreneur. It's whether you want the specific type of freedom that comes with it.

Because entrepreneurship isn't about work-life balance. It's about choosing your own chains.

"It's not about the pursuit of money or greed. If you do that, you'll fail. It's the undying love of freedom."

You can have a fantastic life as an employee. You can build wealth, have security, and enjoy your weekends. There's absolutely nothing wrong with that path.

But you'll never be completely free.

Entrepreneurship is the only path to true personal freedom - the freedom to make your own decisions, control your own time, and build something that's entirely yours.

The question is: do you want that freedom badly enough to give up everything else for it?

This reminds me of conversations I have during my 1:1 coaching sessions. So many people say they want to be entrepreneurs, but when we dig into what that actually means - the sacrifice, the uncertainty, the relentless focus required - they realise they're not ready for that level of commitment.

If you're working through whether entrepreneurship is right for you, I offer personalised guidance: https://topmate.io/chalkmeout

The real test

So can anyone be an entrepreneur?

The honest answer is no - but not for the reasons you think.

It's not about your background, your education, or even your initial idea. It's about whether you have the specific psychological makeup to handle constant ups and downs while staying laser-focused on what matters most.

The good news? If you're asking this question, you're already thinking like an entrepreneur.

You can always write to me by simply replying to this newsletter. I read every single email, even if I can't reply to them all. Remember, the path isn't for everyone - and that's perfectly okay. The key is being honest about what you actually want from life.

Have an amazing week ahead.

Building something meaningful,

Rayn

This isn't a skills problem. It's an agency problem.

The Paradox of Infinite Choice

Psychologist Barry Schwartz's research on "choice paralysis" reveals a counterintuitive truth: more options often lead to less action. When everything seems possible, nothing feels urgent.

Modern professionals face unprecedented choice complexity:

• Thousands of career paths across emerging industries

• Unlimited learning resources with conflicting advice

• Constant exposure to others' success stories across every possible field

• Pressure to optimize every decision for maximum outcome

The result: analysis paralysis disguised as careful planning.

The Illusion of External Control

Many capable people unconsciously wait for permission to start pursuing meaningful work. They seek the perfect opportunity, the ideal circumstances, or the complete skill set before taking action.

Common Permission-Seeking Patterns:

• Waiting for company leadership to recognize their potential

• Accumulating credentials before attempting challenging projects

• Seeking consensus before pursuing innovative solutions

• Postponing action until market conditions improve

But research from Stanford's psychology department shows that agency—the belief that your actions matter—is the strongest predictor of professional satisfaction and success.

From Overwhelm to Action: The Agency Framework

Step 1: Control Audit List everything in your professional life you can actually influence. Focus exclusively on inputs you control, not outcomes you hope for.

Step 2: Minimum Viable Progress Identify the smallest possible action that moves you toward a meaningful goal. Perfect planning is procrastination in disguise.

Step 3: Daily Agency Practice Take one deliberate action daily that reinforces your belief that your choices matter. Track this consistently.

Step 4: Momentum Recognition Celebrate evidence that your actions create results, however small. This reinforces the agency mindset.

The Compound Effect of Small Actions

Amazon started as a bookstore. Facebook began in a dorm room. Tesla's first car was a modified sports car with limited range.

None of these companies started with perfect products or complete strategies. They started with imperfect action and improved through iteration.

The same principle applies to individual careers: consistent small actions compound into significant capabilities over time.

Why Starting Imperfectly Beats Waiting for Perfection

Research Insights:

• The "planning fallacy" shows people consistently overestimate preparation time needed

• "Implementation intention" studies prove that specific action planning dramatically increases follow-through

• "Progress principle" research demonstrates that small wins create disproportionate motivation

Practical Application:

• Launch the imperfect project rather than perfecting the plan

• Schedule specific action times rather than waiting for inspiration

• Measure progress in experiments attempted, not just successes achieved

Building Anti-Fragile Agency

Nassim Taleb's concept of "anti-fragility" applies to professional development: systems that get stronger under stress rather than weaker.

Anti-Fragile Agency Characteristics:

• Views failures as data rather than defeats

• Treats uncertainty as opportunity rather than threat

• Uses constraints to spark creativity rather than limit options

• Builds capabilities through action rather than accumulating credentials

Your Agency Recovery Plan

Week 1: Reclaim Control Focus Document every area where you have genuine influence. Ignore everything else temporarily.

Week 2: Define Minimum Action Choose one capability that would meaningfully improve your professional situation in 90 days. Define the smallest possible daily practice.

Week 3: Build Action Momentum Practice your chosen capability daily, regardless of external circumstances. Track consistency over intensity.

Week 4: Expand Agency Awareness Notice how taking deliberate action affects your confidence, opportunities, and results. Use this evidence to fuel continued action.

The Agency Advantage

In an economy increasingly rewarded innovation and adaptability, agency becomes a competitive advantage. Organizations value people who create solutions rather than wait for direction.

Your next career breakthrough won't come from having perfect information or ideal circumstances. It will come from taking imperfect action based on what you know right now.

The most successful professionals aren't those with the best opportunities—they're those who create opportunities through consistent, deliberate action.

Stop waiting for permission. Start building momentum.

The only person who can give up on your professional future is you. Choose not to.

Your personal brand isn't a revenue stream. It's a trust mechanism that amplifies actual business value.

The Attention Economy Trap

Social media platforms profit from engagement, not outcomes. This creates an incentive structure that rewards content creation over value creation. The result: professionals spending more time talking about work than actually doing exceptional work.

Research from Harvard Business School shows that sustainable business success comes from demonstrated competence, not content marketing. Yet entire industries have emerged around "personal branding" that prioritize visibility over capability.

Trust: The Only Currency That Matters

Warren Buffett's investment philosophy centers on one principle: invest in management teams you trust to execute. This same principle applies to every professional relationship.

Trust Indicators vs. Attention Metrics:

• Consistent delivery on commitments vs. viral content moments

• Problem-solving under pressure vs. engagement optimization

• Results achieved for others vs. personal story amplification

• Competence in crisis vs. content calendar consistency

The professionals who build lasting success focus on trust indicators, even when attention metrics seem more immediately rewarding.

The Competence Compound Effect

McKinsey's research on high-performing professionals reveals a pattern: they become known for solving specific problems exceptionally well, not for being generally inspiring online.

Case Study Framework:

• Depth Over Breadth: Specialists command premium pricing and respect

• Execution Over Expression: Results speak louder than content

• Consistency Over Virality: Reliable competence beats occasional brilliance

• Value Creation Over Self-Promotion: Helping others builds lasting reputation

Building Professional Trust at Scale

The most effective approach to professional reputation isn't traditional "personal branding"—it's systematic trust building:

Document Real Work: Share insights from actual problem-solving, not theoretical frameworks. People trust practitioners over philosophers.

Demonstrate Process: Show how you approach challenges, iterate on solutions, and learn from outcomes. This builds confidence in your methodology.

Deliver Before Promoting: Create value for others before asking for anything. Trust builds through consistent giving, not strategic taking.

Choose Meaningful Problems: Associate your reputation with solving problems that matter to people you want to work with.

The Business Reality Test

Ask yourself: if your social media presence disappeared tomorrow, would your professional opportunities disappear with it?

If yes, you've built a content dependency, not a business asset.

The most valuable professional reputations survive platform changes, algorithm updates, and industry shifts because they're based on demonstrated competence, not content strategy.

Strategic Implementation

Audit Your Current Approach:

• What percentage of your time goes to content creation vs. skill building?

• Do people seek you out for your expertise or your entertaining content?

• Would colleagues recommend you for challenging projects based on your demonstrated abilities?

Reframe Your Strategy:

• Instead of "building an audience," focus on "building competence worth talking about"

• Instead of "creating engaging content," focus on "documenting valuable work"

• Instead of "growing followers," focus on "earning respect from people who matter"

The Long-Term Advantage

Attention is temporary. Trust compounds.

Professionals who optimize for trust rather than attention build careers that survive industry changes, economic downturns, and technological disruptions.

Your professional reputation should be your most valuable business asset. Build it like one.

Most people think Monday motivation is about finding the energy to start. But successful professionals understand something different: Monday isn't about motivation—it's about choosing what type of suffering you're willing to embrace.

The Myth of the Easy Path

Modern culture sells us the illusion that success should feel effortless. Social media showcases the highlights while hiding the struggle. Business gurus promise shortcuts and life hacks that bypass the fundamental truth: meaningful achievement requires meaningful effort.

But here's what research in behavioral psychology confirms: there are two types of suffering in professional life, and you'll experience one of them regardless of your choice.

Understanding Productive vs. Destructive Suffering

Discipline Pain: The temporary discomfort of doing difficult things that build long-term capability. Learning new skills, having challenging conversations, taking calculated risks, maintaining standards when it's inconvenient.

Regret Pain: The lasting ache of missed opportunities, unexplored potential, and the compound cost of choosing comfort over growth.

The crucial difference: discipline pain has an expiration date, while regret pain compounds over time.

The Science Behind Sustainable Growth

Research from Stanford's Behavior Design Lab shows that sustainable behavior change comes from systems, not willpower. The most successful professionals don't rely on motivation—they design environments that make good choices inevitable.

The Monday Momentum Principle: How you start Monday determines your entire week's trajectory. Studies show that early wins create psychological momentum that carries forward for days.

Energy Management Over Time Management: Peak performers focus on when they do things, not just what they do. They align their most challenging work with their highest energy periods.

Building Your Monday Choice Framework

Instead of hoping for motivation, create systems that automatically guide you toward productive suffering:

The Sunday Setup Protocol: Prepare three specific actions for Monday morning before you check any messages. Research shows that decision fatigue depletes willpower, so pre-deciding eliminates choice paralysis.

The First Hour Rule: Protect the first hour of Monday from reactive tasks. Use it for proactive work that moves your most important projects forward.

The Progress Tracker: Monitor which type of suffering you're choosing daily. Awareness transforms unconscious patterns into conscious choices.

Why This Choice Defines Professional Success

Companies like Apple, Amazon, and Google didn't become industry leaders by avoiding difficult decisions. They succeeded by consistently choosing the harder path that built long-term advantage.

The same principle applies to individual careers. Professionals who advance consistently are those who embrace the temporary discomfort of growth over the permanent discomfort of stagnation.

Your Implementation Strategy

This Monday, before checking your phone, ask yourself: "What am I choosing to suffer for today?"

If your answer involves building capabilities, solving important problems, or creating value for others, lean into that productive suffering.

If your answer involves avoiding discomfort or maintaining the status quo, recognize that you're choosing the compound pain of missed opportunities.

Every Monday is a choice between two types of suffering. Choose the one that builds your future rather than limiting it.

Chinese video models surge ahead of Google's Veo 3

Google's Veo 3 has dominated video generation since its release last month, appearing in primetime television spots and Hollywood-backed short films. However, its reign as the undisputed champion appears to be ending.

ByteDance's Seedance 1.0 has claimed the top position on Artificial Analysis' crowd-sourced leaderboard, ranking ahead of Veo 3 across multiple evaluation categories. This represents a significant achievement for Chinese AI development, demonstrating competitive parity with leading Western models.

Adding to the competitive landscape, MiniMax revealed that a mysterious video model called "Kangaroo" - which has been outperforming Veo in image-to-video generation - is actually their new model called Hailuo 02.

Key implications:

• Chinese AI companies are maintaining technological competitiveness with US counterparts

• Video generation models are advancing rapidly across global markets

• Competition is driving faster innovation cycles in AI video technology

The developments underscore how quickly leadership positions can shift in the AI space, with multiple companies pushing the boundaries of what's possible in video generation.

TikTok launches comprehensive AI video toolkit for marketers

ByteDance has introduced new AI-powered video creation tools specifically designed for advertisers on TikTok. The platform now allows marketers to generate five-second video clips using either text or image prompts, which can then be combined to showcase entire product catalogues.

The system offers several capabilities:

• Product visualisation: Upload an image of a car and generate video of it driving through various landscapes

• AI avatar integration: Create videos featuring AI avatars discussing and demonstrating products

• Clothing modelling: Generate videos showing AI models wearing brand clothing

This development represents TikTok's push to capture more advertising revenue whilst providing brands with sophisticated content creation tools. The integration of AI generation directly into the advertising platform could significantly lower barriers to video content creation for smaller businesses.

The timing coincides with increasing competition amongst social media platforms to offer advanced AI-powered advertising solutions.

Adobe tackles AI-driven search transformation

Retail companies experienced a staggering 3,500% increase in traffic from generative AI sources over the past year, but analytics tools have struggled to keep pace with this shift in consumer behaviour.

Adobe's new LLM Optimizer addresses this challenge by:

• Tracking performance: Monitoring how content performs across various AI platforms

• Boosting discoverability: Functioning like SEO for large language models

• Improving search results: Increasing likelihood of products appearing in AI-driven searches

The tool reflects growing recognition that AI chatbots are becoming primary search interfaces for consumers. Companies that optimise for AI-driven discovery may gain significant competitive advantages as search behaviour continues evolving.

This development highlights how traditional digital marketing strategies must adapt to accommodate AI-mediated consumer interactions.

Microsoft and OpenAI partnership faces potential strain

Reports suggest that Microsoft and OpenAI's six-year partnership - one of the closest collaborations in the technology industry - may be reaching a critical juncture.

Background of the partnership: Since 2019, Microsoft has provided OpenAI with cloud access and billions in investment funding in exchange for exclusive rights to the ChatGPT-maker's models. This arrangement has been mutually beneficial, with Microsoft gaining AI capabilities whilst OpenAI received crucial infrastructure support.

Current tensions: OpenAI has been attempting to transform into a public-benefit corporation for months, seeking greater independence and flexibility to accelerate growth. Microsoft reportedly remains reluctant to modify the partnership terms, concerned about potential negative impacts on its interests.

According to the Wall Street Journal, tensions have escalated to the point where OpenAI is considering:

• Making public accusations against Microsoft

• Requesting federal antitrust investigations

However, both companies issued a joint statement expressing optimism about continuing their collaboration "for years to come," suggesting negotiations may still resolve the dispute.

Industry implications: This potential partnership strain could significantly impact the AI industry structure, particularly regarding:

• Model access and distribution

• Cloud infrastructure arrangements

• Competitive dynamics between major tech companies

Web browsing automation advances with Genspark AI

AI-powered web browsing automation has taken a practical leap forward with Genspark AI browser, which offers several automated capabilities:

Content processing:

• Summarise YouTube videos instantly

• Generate presentation slides from video content

• Extract specific information from video content

Enhanced browsing:

• Automatic ad blocking using AI detection

• Popup and tracker elimination without manual configuration

Product research:

• Compare products across multiple websites

• Aggregate pricing, features, and reviews from various sources

• Access information from Reddit, Amazon, and YouTube simultaneously

Social media automation:

• Transform social media feeds into podcast scripts

• Summarise top posts by topic

• Export content as MP3 files

These capabilities represent practical applications of AI that can immediately impact daily productivity and research efficiency.

Additional industry developments

Reddit expands AI advertising tools: The platform launched tools allowing advertisers to track trending topics across subreddits and test campaigns before full deployment.

Medical research acceleration: Harvard and MIT researchers reportedly saved 12 years of equivalent work time using OpenAI's GPT-4.1 and o3-mini-high to automate systematic medical research reviews.

WhatsApp advertising expansion: Meta is introducing advertisements to WhatsApp's "Updates" tab, which receives 1.5 billion daily visitors, as the company seeks to recoup its $14.3 billion investment in Scale AI.

Voice agent integration: ElevenLabs now supports Model Context Protocol (MCP), enabling voice agents to connect with Salesforce, HubSpot, and Gmail without additional setup requirements.

Looking ahead: industry trajectory

These developments illustrate several key trends shaping the AI landscape:

Global competition intensifying: Chinese companies are matching or exceeding Western AI capabilities, particularly in video generation and content creation.

Practical applications expanding: AI tools are moving beyond experimental phases into everyday business and productivity applications.

Partnership dynamics evolving: Traditional tech alliances face pressure as AI companies seek greater independence and flexibility.

Search and discovery transforming: Consumer behaviour is shifting toward AI-mediated interactions, requiring new optimisation strategies.

The pace of development suggests that competitive advantages in AI may be increasingly temporary, with breakthrough capabilities quickly matched by competitors across global markets.

As these trends continue, businesses and consumers should expect continued rapid evolution in AI capabilities, pricing models, and integration approaches across multiple industries.

There's a fundamental problem with this approach: it assumes all success is worth pursuing, regardless of the cost.

This week, five insights completely shifted how I think about success, decision-making, and daily productivity. These aren't your typical "wake up at 5 AM" tips. They're deeper principles that can transform how you choose your path forward.

🧠 Here's what studying winners really looks like—and why most people get it completely wrong.

The winner's paradox: when success looks like failure

Before diving into any competitive field, smart people study the top performers. Their win rates look impressive. Their bank accounts appear healthy. But what about their lifestyles?

Consider professional poker players. The top 1% earn substantial money, but many spend eight hours a day alone in casinos. No meaningful relationships outside the game. No hobbies. No life beyond cards and calculations.

This observation reveals a crucial principle: "Before you play the game, study the winners. If you don't want their life, don't play their game."

The pattern appears across industries:

→ Restaurant industry: Successful restaurant owners work 70-hour weeks, miss family dinners, and live in constant stress about razor-thin margins.

→ Investment banking: Top performers earn massive salaries but sacrifice weekends, relationships, and mental health for decades.

→ Startup founders: Many successful entrepreneurs spend years isolated, stressed, and emotionally drained before any breakthrough. “ask me about it”

→ Professional sports: Elite athletes dedicate their entire youth to training, often missing normal social development and educational opportunities.

This doesn't mean these paths are wrong. It means being honest about what you're signing up for.

The most common mistake is studying only the highlight reel. Social media shows the vacation photos, not the 60-hour work weeks that paid for them. LinkedIn celebrates the promotion, not the years of 6 AM meetings.

The ones worth emulating aren't necessarily the most "successful" on paper—they're the ones whose daily reality you actually envy.

Before committing to any path, spend time with people who've achieved what you want. Ask about their typical Tuesday, not their best achievement. Understand their regrets, not just their victories.

The morning and night decision framework

Here's the simplest decision-making tool you'll encounter: "Do what makes mornings exciting and nights peaceful."

Most decision-making advice focuses on ROI, opportunity cost, or logical analysis. But this approach cuts straight to what actually matters: how will this choice affect your daily emotional state?

The framework works by connecting logical decisions to emotional outcomes. You might rationalise a choice that looks good on paper, but your morning energy and evening peace are honest indicators of whether it's right for you.

Business decisions: Will taking this client make you excited to start work tomorrow? Will you sleep peacefully knowing you're building something aligned with your values?

Career moves: Will this role energise you for daily tasks? Can you maintain this pace without constant anxiety?

Daily choices: Will saying yes to this meeting energise you for the rest of the day? Will working late on this project leave you satisfied or anxious when you try to sleep?

Relationship decisions: Does spending time with this person make you look forward to future interactions? Do you feel calm and centred after being with them?

💡 This simple test eliminates more bad decisions than any complex framework.

The beauty isn't in its complexity—it's in its honesty. Most people ignore their emotional responses to decisions, focusing only on logical benefits. But emotional sustainability determines long-term success more than any spreadsheet analysis.

Try applying this framework to your last five major decisions. How many passed both tests? The correlation between framework alignment and satisfaction is usually striking.

The 3 PM energy cliff and state change strategy

Every professional knows the 3 PM slump. Energy crashes. Motivation disappears. Productivity plummets.

Most people fight through it with coffee, willpower, or guilt. But there's a smarter approach: designing an intentional state change routine.

Research shows that 3 PM represents a "biological breeding ground for depressive thoughts and low agency." Instead of pushing through low energy periods, successful people engineer deliberate breaks.

The key insight: you can't think your way out of a low energy state. You have to physically change your state.

Effective 3 PM routines include:

Physical movement:

• 20-30 minute walk outside

• Quick gym session or bodyweight exercises

• Stretching or yoga routine

• Dancing to favourite music

Mental reset activities:

• 10-20 minute meditation

• Reading fiction for 15 minutes

• Creative activities like drawing or writing

• Listening to podcasts or audiobooks

Social connection:

• Quick call with a friend or family member

• Coffee chat with a colleague

• Brief video call with remote team members

• Even texting someone you appreciate

Environment change:

• Working from a different location

• Rearranging your workspace

• Changing lighting or background music

• Moving to a café or outdoor space

The specific activity matters less than the intentional interruption of declining mental state. This approach can increase late-afternoon productivity by 40% or more. More importantly, it eliminates the afternoon anxiety spiral that can derail entire days.

The inflation reality check: tracking what actually matters

One fascinating observation was someone tracking inflation specifically on dry-aged rib eye steak—12% annual inflation, up 62% in five years.

Most people would dismiss this as absurd. But there's genius in tracking specific, meaningful indicators rather than abstract data that doesn't affect your life.

This principle applies beyond financial metrics. What you measure in your personal and professional life reveals what you actually value.

Most people track vanity metrics that don't correlate with satisfaction:

• Social media followers instead of meaningful relationships

• Income instead of financial security and freedom

• Hours worked instead of meaningful impact created

• Achievements unlocked instead of skills developed

Meaningful measurement requires specificity and personal relevance.

Consider tracking indicators that reflect your actual priorities:

Professional growth:

• Problems solved rather than tasks completed

• Skills developed rather than hours logged

• Positive feedback received rather than emails sent

• Creative ideas generated rather than meetings attended

Personal development:

• Days feeling genuinely excited about work

• Quality conversations with important people

• New experiences or learning opportunities pursued

• Moments of genuine contribution to others

Health and wellbeing:

• Energy levels throughout the day

• Sleep quality rather than just quantity

• Stress management rather than stress avoidance

• Physical activities that bring joy

The steak inflation tracker understands something crucial: meaningful measurement requires choosing indicators that actually affect your life, not what you think you should be measuring.

The compound effect of daily micro-decisions

These insights might seem disconnected, but they share a common thread: small, consistent choices compound into dramatically different life outcomes.

Studying winners before choosing your game saves years of misaligned effort.

Using the morning/night framework eliminates thousands of energy-draining decisions.

Managing your 3 PM energy state improves half your productive hours.

Tracking meaningful personal metrics keeps you aligned with your actual values.

The most successful people aren't just talented—they're intentional. They've figured out which games align with their desired lifestyle. They've developed frameworks for making decisions that serve their long-term wellbeing. They've engineered their days for sustained energy rather than short-term productivity sprints.

Most importantly, they've learned to measure what actually matters to them, not what looks impressive to others.

The path forward

The most successful people didn't accidentally stumble into their careers. They studied the winners, understood the true cost of success in their chosen field, and made conscious decisions about whether they wanted that specific life.

This isn't about finding the "perfect" path—it's about finding the path that aligns with your authentic priorities and energy patterns.

Success requires sacrifice. The question is whether you're willing to make the specific sacrifices required for your chosen definition of success.

Before you commit to any game, study not just the winners' achievements, but their daily reality. If you don't want their life, don't play their game.

➡️ Your future self will thank you for being honest about what you actually want, not what you think you should want.

Success without alignment is just expensive misery.

The relationships we cultivate become the foundation of everything meaningful we create in life.

There's a moment in every person's life when they realise that success without meaningful relationships feels surprisingly hollow. The difference between simply surviving life's challenges and truly thriving through them often comes down to one factor: the quality of friendships we cultivate.

The science behind friendship and wellbeing

🧠 Here's what most people misunderstand about friendship: They view it as recreational rather than essential infrastructure for a meaningful life.

Research reveals that people with strong social connections have a 50% increased likelihood of longevity compared to those with weak social ties. That's a stronger predictor than obesity, smoking, or lack of physical activity.

The physiological benefits include reduced inflammation, improved immune function, lower blood pressure, and decreased risk of cardiovascular disease. Friends literally help keep us alive longer.

But the psychological benefits matter just as much. When friends listen without judgement, they create space for us to process emotions authentically and separate temporary setbacks from permanent identity shifts.

💡 The friendship framework that supports resilience:
• Celebration amplifiers: Friends who genuinely celebrate your wins
• Reality mirrors: Friends who reflect your true character during difficult moments
• Growth catalysts: Friends who challenge you to become better
• Anchor points: Friends who provide stability when everything else feels uncertain

Can friendships really make you more successful professionally?

The answer is unequivocally yes, but not in the transactional way most people imagine.

Authentic friendships contribute to professional success through enhanced decision-making, stress management, sustained motivation, and creative collaboration. Friends provide diverse perspectives that help you see blind spots and maintain focus during difficult periods.

Studies show that individuals with robust social networks demonstrate greater resilience, make better decisions under pressure, and recover more quickly from setbacks.

Personal growth through authentic connection

🤔 Consider this paradox: We often discover who we truly are through the eyes of people who know us best.

Good friends serve as external mirrors that reflect our authentic selves back to us. They help us recognise strengths we take for granted and blind spots we need to address.

This reflection process facilitates personal growth in ways that self-reflection alone cannot achieve. Friends observe patterns in our behaviour that we're too close to see ourselves.

The belonging factor: why connection creates purpose

What does it really mean to belong somewhere?

Belonging isn't just about feeling accepted. It's about feeling understood, valued, and connected to something larger than individual achievement.

When we feel genuinely connected to others, we're more likely to take on meaningful challenges, persist through difficulties, and find purpose in our work. The psychological safety created by authentic friendship becomes the foundation for taking risks and pursuing ambitious goals.

The contagion effect: how happiness spreads

Research from Harvard's Framingham Heart Study reveals that happiness spreads through social networks up to three degrees of separation. Your friend's friend's friend can influence your emotional wellbeing.

The implications are profound: choosing friends isn't just about personal compatibility. It's about choosing the emotional and intellectual environment that will shape your daily experience.

When surrounded by friends who approach challenges with curiosity rather than complaint, we naturally adopt similar perspectives.

How do you identify friends worth investing in long-term?

Not all relationships deserve the same level of investment. Here's a framework for identifying friendships worth cultivating:

The authenticity test: Do they show up as themselves, or perform a version of who they think you want them to be?

The growth mindset indicator: Do they celebrate your successes and support your challenges without trying to solve everything for you?

The reciprocity balance: Does the relationship feel mutual over time?

The values alignment check: Do your core values complement each other?

The energy test: Do you feel energised or drained after spending time with them?

Building friendships that last

How do you maintain meaningful friendships when life pulls you in different directions?

The key insight: consistency matters more than frequency.

Practical strategies that actually work:
→ Schedule regular check-ins that aren't tied to specific events
→ Share meaningful updates about personal growth, not just achievements
→ Remember and follow up on important events in their lives
→ Express gratitude for their friendship explicitly

The investment mindset: friendship as essential infrastructure

🌏 Friendship isn't a luxury for people with abundant time. It's essential infrastructure for anyone serious about creating meaningful impact.

Whether developing professional projects, building businesses, or pursuing personal goals, the quality of our work directly correlates with the quality of relationships that support it.

Friends provide perspective when we're too close to problems, encouragement when confidence wavers, and celebration when we achieve meaningful milestones.

The compound effect over time

Authentic friendships compound in ways that aren't immediately obvious. Colleagues who become genuine friends often evolve into professional collaborators years later. These weren't calculated networking moves—they were authentic relationships that naturally developed opportunities because they were built on genuine care and mutual respect.

The compound effect extends beyond professional benefits:
→ Shared experiences create deeper understanding over time
→ Mutual support through life stages builds unshakeable trust
→ Different perspectives combine to create collective wisdom
→ Emotional investment in each other's success multiplies individual achievements

The philosophy that guides everything

What if the secret to a wholesome life isn't about what you achieve, but about who you become through relationships?

In our hyper-connected yet increasingly isolated world, the ability to build and maintain meaningful friendships becomes both a competitive advantage and a source of genuine fulfilment.

When we invest in authentic relationships, we're creating the emotional and intellectual infrastructure that will support every meaningful goal we pursue. We're developing the support system that will help us navigate challenges we can't yet imagine.

➡️ The truth about wholesome living: It's not about optimising individual performance metrics. It's about creating a web of relationships that support, challenge, and inspire everyone involved to become more than they could become alone.

Your friendship legacy

The quality of your friendships will ultimately determine not just how successful you become, but how meaningful that success feels when you achieve it.

You're not just building a social network. You're participating in the ancient human tradition of growing together rather than growing alone.

Start there. Everything else will follow.

We're no longer competing for attention. We're competing to become the truth.

The philosophical shift behind AI overviews

🧠 Here's what most marketers miss: AI Overviews aren't just another SERP feature. They represent a fundamental shift in how knowledge gets distributed online.

When I started building content strategies for startups, I noticed people weren't scrolling through multiple search results anymore. They wanted immediate, authoritative answers. Google's AI Overviews responded to this behaviour change by surfacing the most trustworthy, comprehensive information directly at the top.

This reminds me of something I often discuss in my Whatever Matters newsletter: the principle of earned authority.

In traditional SEO, we could game systems with keywords and backlinks. But AI systems evaluate deeper signals—authenticity, expertise, and genuine value creation.

→ The brands winning in AI Overviews aren't just optimising for algorithms. They're becoming the definitive source of truth in their domain.

Why most content fails to rank in AI overviews

During my Y Combinator journey, I learned that the biggest mistakes happen when you try to be everything to everyone.

The same principle applies to AI Overview optimisation.

Most content fails because it:

• Tries to rank for too many keywords at once • Provides generic advice available everywhere else
• Lacks the depth of expertise AI systems recognise • Doesn't directly answer the searcher's specific intent

📊 The data tells a story: Pages already ranking in the top 5 organic results are 67% more likely to be featured in AI Overviews. But here's the twist—ranking alone isn't enough.

The authentic expertise framework for AI overviews

After analysing hundreds of AI Overview appearances across different industries, I've identified a pattern that mirrors everything I learned about building trust in business relationships.

1. Lead with definitive answers

The philosophical truth: People don't want more information. They want better decisions.

When I write content for startup blogs, I follow what I call the "Coffee Shop Test." If I can't explain the core concept to someone during a quick coffee break, it's too complex for AI Overviews.

Structure your content like this:

Opening paragraph: Direct answer to the search query Second paragraph: Context that demonstrates expertise
Remaining content: Supporting evidence and actionable steps

2. Demonstrate lived experience, not researched knowledge

💡 This is where most content marketers lose: They write about topics they've researched, not experienced.

AI systems are becoming sophisticated at detecting authentic expertise versus surface-level content creation.

For example, when I write about startup growth, I reference specific moments from building companies—the day we pivoted our entire business model, the conversation with our first enterprise client, the mistakes that nearly killed the venture.

These aren't just stories. They're proof points that I've actually navigated these challenges.

3. Update content based on evolving truth

The brutal reality: Your content isn't just competing with other websites. It's competing with the entire internet's knowledge base.

I update our most important articles every quarter, not because Google demands it, but because the landscape keeps evolving.

When writing about technical topics, I include recent case studies, latest performance benchmarks, and emerging considerations. This consistent updating signals to AI systems that our content remains the most current authority.

4. Structure for both human readers and AI understanding

Here's something I learned whilst building content systems at scale:

Humans scan. AI systems parse.

Your content needs to work for both.

Use these structural elements:

H2 headings → Clear topic divisions that include semantic keywords
Bullet points → Scannable information chunks
FAQ sections → Direct responses to related queries
Data citations → Credibility signals AI systems recognise

Technical implementation that actually works

🌏 Global perspective: Working across different markets taught me that technical excellence transcends cultural boundaries.

Schema markup priorities

Not all schema markup carries equal weight for AI Overviews. Focus on:

• FAQ Schema → Directly answers related questions
• HowTo Schema → Provides step-by-step guidance
• Article Schema → Establishes content authority
• Organisation Schema → Builds entity recognition

The E-E-A-T integration approach

Google's Experience, Expertise, Authoritativeness, and Trustworthiness signals matter more in AI Overview selection than traditional rankings.

Experience: Reference specific projects, failures, and lessons learned
Expertise: Demonstrate deep domain knowledge through nuanced insights
Authoritativeness: Earn citations from industry publications and thought leaders Trustworthiness: Include author bios, contact information, and transparent sourcing

Content types that consistently rank in AI overviews

Through my Whatever Matters podcast, I've interviewed dozens of content creators who've cracked the AI Overview code. The patterns are clear:

Definitive guides with personal frameworks

Not "How to do X" but "The framework I used to achieve Y specific result."

A question I get often: Can new websites actually rank in AI Overviews?

Absolutely. But focus on very specific, long-tail queries where you can demonstrate clear expertise. I've seen newer sites rank for detailed technical questions where their content provides the most comprehensive answer.

The key is becoming the definitive source for a narrow topic rather than competing broadly.

Contrarian perspectives backed by data

Challenge conventional wisdom, but support your position with evidence and experience.

Here's something that surprises people: AI Overview features actually increase overall organic traffic by 200-400% in my experience. Whilst some users get their answer directly from the overview, many click through to read the full content, and branded searches increase significantly.

This counters the common fear that AI Overviews hurt organic traffic.

Process documentation with real examples

Share the exact systems, tools, and approaches you've used to solve specific problems.

Comparative analyses from practical experience

"I've used both approaches extensively. Here's what actually works in different scenarios."

Measuring AI overview performance

🤔 The question that changes everything: How do you track success when traditional analytics don't capture AI Overview traffic attribution?

I've developed a monitoring system that tracks:

Keyword positioning → Which queries trigger AI Overviews featuring our content Traffic patterns → Unusual spikes that correlate with AI Overview appearances
Brand search volume → Increased branded searches following AI Overview features Content engagement → Time on page and subsequent page views from AI Overview visitors

Use tools like SEMrush or Ahrefs SERP feature tracking to identify opportunities, but also watch for those unusual traffic spikes and increased branded search volume—these often indicate AI Overview features before the tracking tools catch up.

The timeline reality: Based on my experience with startup content, pages with strong domain authority can appear in AI Overviews within 2-4 weeks of publication. However, the content must already demonstrate ranking potential in traditional organic results.

For newer websites, the timeline extends to 6-8 weeks, but the opportunity definitely exists if you focus on specific expertise areas.

Should you optimise differently for AI Overviews than traditional SEO?

The fundamentals remain the same—create valuable content that directly addresses user intent. However, AI Overviews favour more concise, authoritative answers presented early in the content. Think of it as traditional SEO with enhanced clarity and expertise demonstration.

The mindset shift that changes everything

Here's what building startups taught me about content strategy:

Success comes from solving problems so well that you become irreplaceable.

AI Overviews reward content that demonstrates this irreplaceability through:

• Unique insights unavailable elsewhere
• Practical frameworks developed through real experience
• Authentic expertise proven through consistent value creation
• Current information that reflects the latest developments

→ Stop trying to rank in AI Overviews. Start becoming the definitive answer to your audience's most important questions.

The future belongs to authentic experts

The internet doesn't need more information. It needs better curators of truth.

AI Overviews aren't just changing how we optimise content—they're changing how we think about expertise itself. The winners won't be those who game the system, but those who become genuinely indispensable sources of insight and guidance.

When someone asks me how to know if their content appears in AI Overviews, I tell them to focus less on the tracking tools and more on becoming the type of expert that AI systems naturally want to cite.

The metrics will follow the value, not the other way around.

🧠 The deeper truth: Every ranking in AI Overviews is a vote of confidence from artificial intelligence in your human expertise.

It sparked a memory of an old story that I'd like to share with you today.

The emperor moth's lesson

A man once found a cocoon of an emperor moth and took it home to witness its transformation.

One day, a small opening appeared. For hours, he watched the moth struggle to force its swollen body through that tiny crack.

The moth fought. It wriggled. It pushed with everything it had.

But progress was painfully slow.

Feeling compassionate, the man decided to help. He carefully cracked open the cocoon, creating a wider path for the moth.

The moth emerged quickly, but something was wrong.

Its body remained swollen. Its wings were shrivelled and motionless.

Days passed. The moth never flew.

Only later did the man learn a powerful truth: the struggle to break free from the cocoon serves a crucial purpose. It forces fluid from the moth's body into its wings, preparing them for flight.

By "helping," he had inadvertently crippled the moth.

🧠 The painful struggle wasn't the problem—it was the solution.

The comfort paradox in our lives

How often do we see this pattern repeating in our own journeys?

When I dropped out of Loyola College in 2015, everyone around me was concerned. My parents worried I was avoiding the necessary struggle of completing my education.

What they couldn't see was that I was exchanging one struggle for another—one that aligned better with my path - AIESEC, where I willingly embraced difficulties that others avoided:

➡️ Cold connections with potential partners when my peers were afraid of rejection
➡️ Taking on the challenge of leading Youth Speak Forum when no one else would step up
➡️ Moving to Nepal as Country Director despite the cultural and professional uncertainties

Each struggle shaped me in ways that comfort never could.

Growth requires resistance

Your entire life transforms when you recognise that meaningful growth feeds on purposeful struggle.

Think about how muscles develop. They don't grow during comfortable activities—they grow when pushed to their limits, when microscopic tears force them to rebuild stronger.

Our minds, capabilities, and resilience follow the same pattern.

💡 Growth isn't a byproduct of ease. It's a byproduct of struggle.

The struggles we commonly avoid

It's easy to opt out of the struggles that catalyse growth:

• We procrastinate on an important project
• We avoid the difficult conversation
• We hide from the internal work
• We skip the challenging workouts
• We dodge the deep work
• We run from the hard questions
• We numb ourselves with cheap dopamine

Each avoidance might feel like self-care in the moment, but it's self-sabotage in disguise.

I've seen this pattern repeatedly in the youth employment space through my work with Unschool. Students choose comfortable paths with immediate rewards like getting a certificate, over challenging journeys which require practice and learning.

🤔 What growth are you starving with the struggle you're avoiding?

The freedom-struggle connection

Long-term freedom is earned through a willingness to endure short-term struggle.

This truth has been a cornerstone of everything I discuss on my Whatever Matters podcast. When interviewing entrepreneurs and thought leaders, I'm consistently struck by how their greatest breakthroughs emerged from their most challenging periods.

Consider these connections:
➡️ Financial freedom requires the discipline of delayed gratification
➡️ Relationship depth comes from working through conflicts
➡️ Creative breakthroughs follow periods of frustration
➡️ Professional mastery demands years of deliberate practice

The most beautiful transformations require the most significant tensions.

Embracing the right struggles

Not all struggles are created equal. The key is discerning which challenges will forge you and which will simply break you.

When moving to Germany last year, I faced countless technical and cultural challenges that were initially frustrating. Yet these very obstacles have pushed us to create something more robust and innovative than we initially envisioned.

The resistance wasn't just an obstacle—it was our teacher.

The way forward

The next time you find yourself in a struggle—feeling stuck, overwhelmed, and tempted to quit—remember the emperor moth.

The resistance may be the very thing shaping you into someone who can fly.

When facing a challenge, ask yourself:

1. Is this struggle connected to growth that matters to me?

2. Am I avoiding it primarily because it's uncomfortable?

3. What capabilities might this struggle be developing in me?

Remember: The growth you requested is hidden in the struggle you avoid.

➡️ Your future self is forged in the fires of today's challenges.

Finding your meaningful struggle

How do you distinguish between productive struggles and unnecessary suffering?

Productive struggles have purpose and direction—they're challenging but lead toward growth in areas that matter to you. Unnecessary suffering lacks meaning or progress. I find that productive struggles, while difficult, often come with a sense of engagement and forward movement, even when progress is slow.

Isn't some comfort necessary for well-being?

Absolutely. The goal isn't to eliminate comfort but to recognise when comfort becomes a cage rather than a resource. Comfort should be a place we rest, not a place we hide. In my newsletter, I often explore this balance between pushing ourselves and sustainable self-care.

Remember: The growth you requested is hidden in the struggle you avoid.

➡️ Your future self is forged in the fires of today's challenges.

But the data tells a story that's impossible to ignore.

🧠 The combine harvester moment for code

Tom's farming analogy cuts deep because it's historically accurate. The combine harvester didn't just make farming more efficient—it eliminated 90% of farming jobs whilst increasing food production by orders of magnitude.

"The first computer programmers that existed didn't do a job that was anywhere like today's software engineers," Tom noted. "They were writing machine code. They were making punch cards."

The progression is clear: each generation of programming tools has abstracted away the previous layer of complexity. Now, AI coding agents represent the next—and perhaps most dramatic—abstraction yet.

The Y Combinator data reveals everything

The numbers from Y Combinator cohorts tell the story of transformation in real-time:

→ Two cohorts ago: Approximately 0% of founders used AI as their primary coding method
→ Last cohort: 25% of companies used AI tools for most of their code
→ Current cohort: 33-50% primarily write code using AI agents

This isn't gradual adoption—it's exponential transformation happening at the cutting edge of entrepreneurship.

💡 What Tom's experiment revealed

Tom Blomfield, co-founder of Monzo Bank, decided to test these tools himself. What he discovered shocked even him.

Starting with simple games built using no-code tools like Lovable and Replit, he quickly moved to more sophisticated platforms like Cursor, Windsurf, and Claude Code. His breakthrough project: rebuilding his 20-year-old Tumblr blog.

"In 90 minutes, I set up hosting, I wrote new blogging software, and I migrated 15 years of blog posts over to the new platform," Tom revealed.

But the real test came with recipes.ai—a serious application with 35,000 lines of code, thousands of users, and a full interactive voice agent.

The kicker? Tom wrote zero lines of those 35,000.

"After about the first 5,000 lines, I stopped even reading the code," he admitted. "I just prompt, I'd auto accept, I'd go and make a coffee and I'd come back and a new feature was built."

This from someone who hadn't written code professionally for 10 years—yet found himself more productive than when he was actively coding a decade ago.

📊 The Jevons Paradox argument (and why it misses the point)

Critics quickly deployed the Jevons Paradox defence: as the cost of software development plummets, demand will increase exponentially, maintaining or increasing employment for programmers.

Tom acknowledges this partially: "I basically agree with that as well. But my counterargument is that it won't be humans meeting that demand."

The mathematics are stark. Even if software demand increases 10x or 100x, when productivity per person approaches infinity (dividing by zero, as Tom puts it), the human workforce required shrinks dramatically.

The future of on-demand software

We're moving toward what Tom calls "ephemeral programs"—custom software that spawns when you have a problem, solves it, then dissolves back into the digital ether. ChatGPT already hints at this future, spinning up mini-applications to solve specific user queries.

🌏 Beyond programming: the knowledge work revolution

The transformation extends far beyond software engineering. Tom and David discussed examples across industries:

Legal: Swedish company Lora proved that even lawyers—historically resistant to efficiency tools—will adopt AI when competitive pressure mounts. The old wisdom "lawyers never buy software" crumbles when AI becomes a competitive necessity.

Marketing: Companies like scriptbee.ai represent an entirely new category—marketing agencies run by AI agents, built using similar coding tools Tom demonstrated. These autonomous systems handle campaigns, content creation, and client management with minimal human oversight.

Medicine and Finance: Y Combinator has seen a surge in companies automating knowledge work across these sectors. What started as "fringe" ideas two years ago are now thriving businesses.

"We see plenty of examples of these companies succeeding and actually being used in these industries," David noted.

The physical work advantage

Some professions enjoy natural protection from this wave:

→ Surgeons, plumbers, electricians: Physical presence and dexterity remain human domains
→ Trade unions and regulatory bodies: May create artificial barriers to protect jobs
→ Niche specialisations: Complex, human-judgment-heavy roles will persist longer

But even these face eventual disruption as robotics advances.

🤔 The agency and taste question

A crucial question emerged in Tom and David's discussion: what remains uniquely human?

David argues it's problem identification and taste: "If you look at the best software that you use in your life, very likely there is a single human being behind that team that built that and that person obsesses over making that product excellent for the user."

The challenge isn't technical capability—it's programming AI to have that obsession, that taste, that drives exceptional user experiences.

➡️ The founder's unprecedented opportunity

Both Y Combinator partners agree: there's never been a better time to start a company.

"These tools give high agency individuals superpowers," Tom emphasised. "If you are a potential founder thinking about starting something, I don't think there's been any point in history that's been better than today."

Why now is exceptional

1. Smaller teams, bigger impact: Two-person teams build what previously required 40 engineers

2. Faster profitability: Companies reach sustainability quicker, often without Series A funding

3. Lower barriers: Technical complexity no longer blocks great ideas

4. Industry transformation: Law, education, medicine—formerly software-resistant sectors—are suddenly open to disruption

Tom's advice for aspiring founders

Tom's recommendations are practical and urgent:

"The first thing I would do, and I'm encouraging all of my friends to do this, is just to stay up to date with the latest tools. They might not be perfect for your industry yet, but I'm betting a lot of money that at some point they will cross that tipping point."

David adds the human element: "Get good at identifying human problems to go solve... if you overindex and get really good at just understanding people and seeing problems... that skill relative to all the other skills needed to be a good founder is going to be the one that is more important."

The transition challenge

Both acknowledge the darker implications. David highlighted the potential for "hundreds of millions of people displaced" during what could be an extremely rapid transition.

"The idea that they're going to retrain as in a different job I think is going to be extremely painful," Tom admitted. "I can see the societal impact and turmoil being very very grave for 10 or 20 years as that transition happens."

Yet they maintain optimism. Tom argues: "If you just look at each step along this progression and you had to choose as an individual, do I want to live in this world or the world one click further into the future? I think in all cases the world and your experience and what you can do with your life is going to be better in the next click forward."

You might have these questions

Are these AI coding tools actually reliable for production systems?

Tom addressed this directly: while not perfect today, the rate of improvement makes reliability questions irrelevant. "The argument that these tools are never going to be good enough I think is just a losing proposition."

How can current software engineers prepare?

Both partners recommend immediate experimentation with AI tools, even if they're not perfect for your specific use case yet. The competitive advantage belongs to early adopters.

What about code quality and maintainability?

Tom's experience suggests this concern may be overblown. He stopped reading the AI-generated code after 5,000 lines and still successfully built a complex application. The focus shifts from code craftsmanship to system architecture and user experience.

Will there be any programming jobs left?

"There will be demand for smart people who know how to wrangle these AI coding machines," Tom predicts. "If we want to call those people software engineers, so be it. But I think the job is dramatically, dramatically different."

How fast is this transition happening?

The Y Combinator data suggests very rapid adoption among startups. Tom believes software engineering jobs as they exist today "will not exist in five or 10 years."

➡️ A future worth building

The conversation between Tom and David illuminates both the promise and peril of our current moment. We're witnessing the kind of technological shift that happens perhaps once in a generation—the mechanisation of human intellect itself.

Tom's final insight resonates: "I genuinely do believe right now and probably the next 5 years is the best time in the history of humanity to build something from scratch."

As someone building tech companies across continents—from Unschool in India to caisy.io in Germany—I see this transformation happening in real-time. The founders who embrace these tools aren't just building faster; they're reimagining what's possible when human creativity meets unlimited execution capability.

The question isn't whether this revolution will happen. Tom's Twitter thread may have sparked controversy, but the Y Combinator cohort data provides undeniable evidence: it's already here.

The question is whether you'll be part of shaping it, or watching it happen to you.

This analysis draws from the Y Combinator "Breakdown" episode featuring Tom Blomfield and David Lieb.