🔮 Copy that: The curious case of AI distillation #594 Plus: A prophet of terror & AI doomers; bioweapons, orcas & the Amazon success Hi, Welcome to the latest Sunday briefing! I am off on holiday for a couple of weeks. The team will continue to tend to Exponential View while I’m gone, so you won’t miss a beat. Azeem Distillation grey zone On 1st September 1789, Samuel Slater set sail from England for New York. He’d learned that the Pennsylvania legislature had recently passed an act awarding £100 to a British textile worker who smuggled high-end machinery into the state. America was going to great lengths to acquire industrial know-how, by any means possible. Britain had made it illegal to export textile machinery and technical drawings. The ban extended to prevent textile workers from emigrating. Slater had worked in mechanized textile mills and saw his chance. He committed the entirety of Richard Arkwright’s system – the first factory method for spinning cotton – to memory. He disguised himself as a farm laborer and broke the laws of his nation as he carried himself across the Atlantic, valuable know-how secretly distilled into his brain. By 1790, Slater was a partner in a cotton mill in Pawtucket, Rhode Island, built from the plans he memorized. President Jackson called him the “Father of American Manufactures.” He died in 1835 worth around a billion dollars in today’s terms.1 Today’s question is to what extent are Chinese AI labs distilling the outputs of American AI models – and is it really a problem? The easy answer is the hawkish one: hugely and yes, it is. But it’s not the only answer. First of all, distillation is a decades-old machine learning technique in which a larger model can train a smaller, more efficient model. A lab running distillation internally is not an issue. But it is possible to distill a model from the outside (even without the provider’s permission). This is the accusation against Kimi and other Chinese labs. Diogo Almeida, a four-year veteran of OpenAI, explains that effective distillation is much harder today than a few years ago, when AI models helpfully provided their reasoning traces. What Almeida calls “behavior parroting” is to learn from final answers, the least powerful approach but might still work well to help bootstrap another model. There is substantial evidence that both Chinese and American researchers have trained models on the outputs of frontier systems. Stanford’s Alpaca project has admitted as much. Anthropic has alleged that DeepSeek, Moonshot and MiniMax have used more than 16 million Claude chats via 24,000 fake accounts. Michael Kratsios, Trump’s science chief, says he now has evidence of how Moonshot ran distillation attacks. Anastasios Angelopoulos, the CEO of Arena, a benchmarking company, makes the case that Kimi K3 is exceeding the performance of some of the top US models, something distillation alone doesn’t allow, and he predicts that “American labs will start distilling Chinese intelligence.” It isn’t clear that distillation is illegal yet. Nathan Lambert points out that “[t]here’s no legal precedent that model outputs are IP.” The US Copyright Office’s 2023 statement on AI confirms as much: When an AI technology determines the expressive elements of its output, the generated material is not the product of human authorship. As a result, that material is not protected by copyright. Unlike the case of Samuel Slater, who knew he was breaking British law, the problem here is a case of the exponential gap: the technology has stepped ahead of the law.2 If labs have IP in outputs of their models, they will essentially have IP in every future economic activity of all their users. It also weakens the labs’ own position – why should AI models be prevented from consuming Anthropic’s IP when Anthropic is allowed to consume yours and mine? Bahrad Sokhansanj has some other sensible suggestions, summed up as follows. Address distillation but scope it narrowly, only focusing on the real harm done, with the right instruments. Is it about national security? Or something else? Regulate more broadly, and this will play into the labs’ desire to skew the regulatory field in their favor. Again, there is precedent: in 1824, once he was settled as a prominent American industrialist, Samuel Slater lobbied for protectionist tariffs to stifle foreign competition. By then, he was also known as “Slater the Traitor” back in his hometown. See also: - Will Kimi K3 change the economics of AI? Our analysis. - Satya Nadella was one of many tech leaders to force the case for open-weight models to quieten rumors that the American administration was considering limiting them. Jensen agrees: A MESSAGE FROM OUR SPONSOR, OKTA To get AI security right, take care of identity When you and your team deploy AI agents, identity becomes your strategic infrastructure. An agent is an actor that reads your data, calls APIs, and does things on your behalf. You need to give it clear permissions and to audit its trails. Okta’s AI Identity Readiness assessment will score the security of your AI agents and show exactly what you need to fix to go to production with peace of mind. Want to sponsor Exponential View? Get in touch. China’s chip chase In 2015, Made in China set a target of 70% self-sufficiency in semiconductors within a decade. It was often mocked as fanciful and missed wildly. Data from Morgan Stanley now shows that domestic suppliers will have met about 41% of China’s AI chip demand in 2026, up from 20% in 2023. Beijing may hit its 70% threshold five years late. Compute-weighted, the 41% figure delivers less compute compared to Nvidia, but for the purposes of strategic autonomy, the quality gap is increasingly a non-issue. The spark was Washington’s export restrictions. “If the U.S. hadn’t forced our country, our company and our industry into a corner, we would never have done something like this”, says Huawei’s deputy chairman. It has become an “all-out push” according to this excellent reporting. A leaked conversation between DeepSeek’s boss, Liang WenFeng, and several investors supports this. Liang says: What’s the gap with the U.S.? Only one thing: resources. We don’t have enough GPUs – our count is still small. […] Domestic chips now have a historic opportunity. Previously, adaptation was hindered by poor ecosystem… But that’s changing. NVIDIA CUDA’s moat is eroding rapidly. Full transcript and context at Grace Shao’s blog. We the people Peter McCrory, Anthropic’s Head of Economics, points out that the US labor market has shrugged at AI. Unemployment is at 4.2%, and Anthropic’s data finds no worsening unemployment even in the most exposed occupations. AI augments rather than replaces, for now. Not a single profession has been 100% handed over to machines yet. Every job still needs human effort. It is changing how work gets done, and if workers get more productive, value shifts inside existing roles, and those who use the technology best stand to benefit. The final hard-to-automate tasks, the “weak links”, as Professor Chad Jones calls them, are the things only a human can do. Companies will need people to get them done, and this protects employment, keeping a decent share of income in human paychecks Here is another take. People still matter and will continue to matter. Europe creates far fewer successful innovative companies than the US. One reason I’ve often argued is the simple cost of changing the workforce. I think of startups as exercises in making mistakes and learning from them. Every additional cost to making a mistake means an opportunity to learn not taken. Yoram Wijngaarde finds a simple relationship (correlation is not causation) that shows that the more expensive it is to let go of staff, the lower the rate of unicorns per capita. Prophet motive A new biography of Jean-Paul Marat, one of the leaders of the French Revolution, reviewed in the current LRB, is worth reading for anyone trying to make sense of today’s AI debate. Stanford historian Keith Baker3 has a new biography of the journalist and politician. He argues that Marat hates mediation of any type, from Newtonian formulas to parliamentary assemblies and calm discussion- anything that stands between the people and the truth. He tried to write a daily pamphlet, shouted rather than argued and manufactured intimacy. “By making his journal ‘more interactive, more dynamic, more personal’, he fashioned an intimacy that allowed him to speak for the people.” In amongst this, his paranoia did help him identify real corruption and institutional betrayal. Baker calls him the first modern populist. High-frequency publishing, paranoia as analysis, a parasocial closeness and the constant insistency that any complexity is just conspiracy in disguise… Well, AI discourse is now selecting for exactly this Marat-like temperament. While today’s keyboard warriors carry none of the physical violence of Marat’s Terror, there is a similar underlying logic against nuance and complexity. Doomers and accelerationists share patterns – purge rhetoric, aggressive polemics, and the framing of every whiff of nuance as corrupt. What is left are the extremes. Call to mind imminent economic disaster; catastrophic fraud; utopian abundance… or, simply, the transformation of the human condition. What can get lost in these extremes is the reasoned position that admits and examines evidence. A position that balances probabilities and accepts answers might be complex, incomplete and – contingent. See also: - 💪🏼 Really stoked that AMD’s CEO Dr. Lisa Su opened her keynote with Exponential View data. You can get the same data here. Become a member to receive our Sunday briefing every week in your inbox or the app. Short morsels to appear smart at dinner parties Why AI-assisted bioweapons won’t kill us. via EV member Abi Olvera 🏋🏼♀️ The share of UK businesses using AI has nearly tripled since 2023, but most firms are still dabbling. Arsenal FC is building AI models for football (soccer). Young people are more likely to gamble in financial markets when important life goals (like buying a house) feel out of reach. Global air and sea surface temperatures are headed for a new record. Good news from the Amazon: wildfires are at a record low this year and deforestation is at a 10-year low. h/t EV member Angus Hervey Vintage LLM 😎Training AI models only on pre-1931 texts help researchers study what AI can do without contamination from the modern web. 👀 Stripe is in talks to buy OpenRouter. Intel is shipping the first chips with layers printed on ASML’s $380 million High-NA EUV machines. Know thy maths. Michael Liebreich breaks down why the EU’s 46% electrification target by 2040 is mathematically unachievable. Relevant for anyone working in policy, really. 🐳 Orcas preparing food for their young? Amazing. Thanks for reading! His estate was worth $1 million. On a CPI basis, that is $40 million; on a relative income/wage equivalence, it is about $1 billion; on a share of the US economy, it is about $1.7 billion. It is obviously unseemly to many people that the labs trained on other people’s outputs (like books and essays) en masse. But the courts haven’t yet decided that the training is a breach of copyright law. In Anthropic’s case, despite the settlement, they have decided it wasn’t.

How I Got 100+ People to Pay for My Newsletter (Real Numbers Inside) The exact 5-part system, real attribution data, and the two levers to reach 1,000 paid subscribers. 100+ people pay for this newsletter. No ads. No viral moment. No launch week where everything changed. I checked the dashboard this morning and the number sat at 108, up from 97, thirty days ago. Most newsletter growth posts show you the finish line and hide the system, because they probably are trying to sell you the system. This post shows the full system. Real numbers, five parts, and the two levers doing most of the work. Access your FREE Solopreneur Success Hub - your subscribers-only comprehensive command center for building and scaling a successful one-person business. I created this all-in-one toolkit for building a profitable one-person business, something I wish existed when I first started, and it saves me 20+ hours a week. Now, it’s yours… FREE! The numbers, all of them Pulled from my Substack dashboard on 25 July 2026: - Paid subscribers: 108 - Free subscribers: 6,421 - Free to paid conversion: 1.7% (I need to increase the conversion rate!) - Views in the last 30 days: 32,527, up 8,966 from the prior 30 days - Paid subscribers added in the last 30 days: 11 - Pricing: $29 monthly, $79 annual, $149 for the Mastery tier A 1.7% conversion rate sounds small to me. On 6,395 readers it pays for the tools, the coffee, and the time to keep going. On 20,000 readers with the same system, it becomes a full income. Where every subscriber came from Here is the part most people get wrong. They chase traffic from outside the platform. My attribution data for the last 90 days says otherwise: - Substack App: 386 signups, 48% - Other Substack publications (recommendations and cross-posts): 272 signups, 34% - Existing Substack accounts: 133 signups, 16% - Brand new accounts from outside: 14 signups, 2% Read the last line again. Two percent. Ninety-eight percent of my growth happens inside Substack. Not on Threads. Not on LinkedIn. Not through SEO. Inside the app and inside other people’s newsletters. Once I saw those numbers, I stopped splitting my attention across five platforms and put everything into building in Substack. The five-part system Every one of the 100+ came through one of these five doors. - Daily Notes, batched weekly. I write more than 60 Notes every Sunday and space them across the week, around 10 per day. Batching removes the daily decision. Notes feed the app, and the app drives 48% of signups. I use SubflowAI by Dheeraj Sharma to manage my Notes. Check it out :) - The 10-10-1 rule. Every day: like at least 10 Notes, leave at least 10 real comments, start at least 1 real conversation in the DM. Thirty minutes. This is how recommendation partnerships begin. Nobody recommends a stranger. - Recommendation partnerships. I now have 130+ publications recommending this one. That is the 34% bucket. Each partnership started as a comment, then a conversation, then a collab live. - A funnel block on every free post. One sentence, one link, one specific resource. Not a menu of options. One next step. Free posts teach the what and why. The paid archive delivers the how. - A personal welcome message inside 48 hours. Every new paid subscriber gets a real message from me asking what they are building. Not a template. This is the cheapest churn insurance available, and it tells me what to write next. What each part actually contributes - Notes and the app do the top of the funnel. - Recommendations do the volume. The funnel block does the conversion. - The welcome message does retention. Pull out any one part and the system still runs, but slower. Pull out the daily 30 minutes of engagement and the whole thing stops, because recommendations dry up and Notes stop reaching new feeds. The unglamorous part carries the most weight. What I stopped doing For many months I promoted Solopreneur Code on four platforms outside Substack. Threads, X, LinkedIn, Medium. Hours every week, attention spread across four feeds and four sets of rules. Then I opened the attribution data and counted 14 signups from brand new accounts in 90 days. Fourteen out of 805. I did not quit those platforms though. I quit doing them by hand. Now NOVA handles them. It takes each published post, writes the platform versions, and queues them. That freed the hours to spend inside Substack, where 98% of the growth already lived. More Notes. More comments. Real conversations with the people who were already listening. Effort is not the same as leverage. Four platforms at 25% attention each lost to one platform at full attention. The fix was not working harder on all five. It was handing four of them to a system and showing up properly for the one that mattered. The compounding math, and why I refuse to settle for it Eleven new paid subscribers a month sounds fine until you run the numbers. At 11 a month, 1,000 paid subscribers takes almost seven years. I am not waiting seven years. So two weeks ago I opened a folder on my desktop called “Solopreneur Code 1K Paid Subs” and spent a weekend building the plan to get there faster. Here is what came out of it. The first thing the numbers told me: traffic is the wrong lever. Watch what happens to the free list I would need at different conversion rates. - Conversion stays at 1.7%: I need about 59,000 free subscribers - Conversion reaches 3%: about 33,000 - Conversion reaches 4%: about 25,000 - Conversion reaches 5%: about 20,000 Same destination, three times the work at the bottom rate. Chasing traffic to fix conversion is the most expensive mistake available to me right now. So the plan runs two levers together: - grow the free list from 6,400 to around 20,000 while - lifting conversion from 1.7% to 5%. Three top fixes come first, before any growth tactics: - Clean the list. Subscriber count is a vanity number when a chunk of the list stopped opening months ago. Anyone cold for 180 days gets a two-email win-back, then a removal. The list gets smaller. A smaller warm list converts better and lands in more inboxes. - Install the funnel block everywhere. One sentence, one link, one specific resource at the end of every free post. Five reusable blocks, one per vault asset, rotated. - Run the paid welcome ritual on a schedule instead of when I remember. Then the engines: daily Notes, the 10-10-1 rule, recommendations from 33% of signups to 45%, and Substack Live twice a week. Substack’s own data says audio and video creators grew 50% faster over 90 days, and going live notifies followers in the app, my strongest channel at 48%. I ran zero lives in the last two weeks. That gap is the easiest one to close. The milestones: - 150 paid by October, - 250 by February, - 500 by November 2027, - 1,000 by the end of 2028. Seven years becomes two and a half. Not by working more hours. By fixing conversion instead of buying traffic. I’ve also built a roadmap to guide me to 1,000 paid subscribers, it’s a 6-step method that turns a generic playbook into a actionable plan built on my own numbers. Check it out here. Start with your own numbers Open your dashboard and find your subscriber source breakdown. If 90% of your growth comes from one place, stop working the other places this week. Then pick one of the five parts above and run it for 30 days. The 10-10-1 rule is the one I would choose. It costs thirty minutes and it feeds the other four. I broke down the full growth system, including the Notes templates, the partnership outreach scripts, and the Friday metrics log I use to catch problems early, inside the Substack Audience Growth System deep dive. You’re doing everything. But nothing is moving? You are doing everything. But nothing is moving. That is not a motivation problem. Most solopreneurs are learning from everywhere and getting nowhere. Too much information. No clear system connecting effort to results. You have everything it takes. You just do not have a clear system yet. That is what paid subscribers get. Every system, playbook, prompt, and template. All inside the Premium Vault. All for $79/year. That’s $6.58/month. Upgrade now and unlock the Premium Vault worth thousands of dollars. Thanks for reading! Ready for the next step? Let’s crack the growth equation and build a thriving one-person business on your terms! Anfernee

Use Kimi K3 to Start an AI Research Service This Weekend A practical, no-code playbook for creating competitor reports that local businesses may pay for Most people will spend this weekend comparing Kimi K3 with ChatGPT, Claude and every other new AI model. They will watch benchmark videos. They will debate parameter counts. They will ask whether a laptop can run it. Then Monday will arrive, and nothing in their life will have changed. Let’s take a different approach. Instead of asking whether Kimi K3 is the best model, let’s ask a more valuable question: What can you create with Kimi K3 that a real person or business might pay for? One practical answer is a competitor-research service. You study a local business and its main competitors. You identify what competitors are doing well, what customers repeatedly mention, what opportunities the market is missing and what the business should do next. Then you turn those findings into a clean report with a 30-day action plan. You are not selling AI. You are selling better business decisions. And you can build the first version this weekend. Why Kimi K3 fits this work Kimi K3 is Moonshot AI’s 2.8-trillion-parameter model. It supports native visual understanding and a context window of up to roughly one million tokens. Moonshot positions it for long-running coding, research, reasoning and end-to-end knowledge work. It is available through Kimi.com, Kimi Work, Kimi Code and the Kimi API. (Moonshot AI) That sounds impressive, but here is what it means in practical language: Kimi can help you work through a complete project, rather than only answering one question. It can help you: - create a research plan; - examine multiple competitors; - compare websites and public reviews; - organize findings into tables; - detect patterns and missing offers; - produce documents, spreadsheets and slides; - review the result before you send it to a client. Kimi’s official Agent tools are designed to turn research into websites, reports, documents, slides and spreadsheets. Kimi Work can also work with local files and run scheduled tasks. (Kimi) That is exactly the kind of workflow we need. Kimi K3 explained without the technical headache Kimi K3 uses a Mixture-of-Experts architecture. Imagine a company containing 896 specialists. When a task arrives, a router selects 16 experts that are most relevant to that step. Those selected experts work together while the rest remain inactive for that token. Moonshot says this sparse architecture effectively activates 16 of 896 experts and improves scaling efficiency compared with Kimi K2. (Kimi) You do not need to understand the mathematics behind it. The practical lesson is simple: Kimi K3 behaves less like one employee and more like a large organization that can route different parts of a project to different specialists. But a powerful model does not automatically create a useful business. For that, we need a clear offer. The service you will sell Your first offer will be called: The Local Competitor Opportunity Report You will study one business and three to five of its competitors. Your final package will include: - a one-page executive summary; - a competitor comparison; - public customer-review patterns; - website and offer observations; - missed market opportunities; - five practical recommendations; - a prioritized 30-day action plan. This service can work for: - restaurants; - dental clinics; - gyms; - real estate agents; - cleaning companies; - home-renovation businesses; - local retailers; - accounting firms; - consultants; - online stores; - small software companies. The owner does not need another 30-page report filled with business jargon. The owner needs answers to questions such as: - Why are customers choosing competitors? - Which service should we promote more clearly? - What complaints appear repeatedly in reviews? - What are competitors offering that we are not? - What opportunity is everyone in this market ignoring? - What should we improve during the next 30 days? That is what you are selling. Step 1: Choose one business category Do not target every kind of business. Choose one category in one city. For example: - dental clinics in Vancouver; - restaurants in Victoria; - real estate agents in Calgary; - cleaning companies in Toronto; - gyms in Surrey. Specializing makes the work easier. After studying five dental clinics, you will understand that market better than someone beginning from zero each time. You will recognize common services, offers, customer concerns and marketing gaps. Use this prompt: I want to create a competitor-research service for small businesses. My city is: [CITY] Suggest five business categories that would be suitable for this service. Evaluate each category using these criteria: 1. The businesses have visible local competitors. 2. Useful public information is available online. 3. Owners could benefit from better positioning or marketing. 4. The businesses can reasonably pay for professional research. 5. I can produce useful recommendations without accessing private information. For each category, explain: - the common business problem; - what information I could research; - what the final report could improve; - how easy it may be to find potential customers. Recommend the best category for a beginner. Do not spend a week making this decision. Choose one reasonable category and continue. Step 2: Select one sample business Before approaching customers, create a sample report. Choose a real business and identify three competitors. Use only public information, such as: - official websites; - service pages; - publicly displayed prices; - Google Business profiles; - public customer reviews; - social-media pages; - public advertisements; - blog posts; - FAQs; - local directories. Create a folder containing the links, notes and screenshots you want Kimi to examine. Then use this master prompt: Act as a careful small-business research analyst. I am creating a competitor opportunity report for: Business: [BUSINESS NAME] Location: [CITY] Industry: [BUSINESS CATEGORY] Main competitors: 1. [COMPETITOR 1] 2. [COMPETITOR 2] 3. [COMPETITOR 3] 4. [COMPETITOR 4, IF APPLICABLE] Research and compare: 1. Business positioning 2. Services and offers 3. Publicly listed pricing 4. Website clarity 5. Calls to action 6. Public customer-review patterns 7. Social-media activity 8. Content strategy 9. Trust signals 10. Opportunities competitors may be missing Evidence rules: - Use only public and verifiable information. - Record a source for every important factual claim. - Separate verified facts from interpretation. - State clearly when information cannot be confirmed. - Never invent prices, reviews, customer opinions or statistics. - Do not make legal, medical or financial conclusions. Create: 1. A one-page executive summary 2. A competitor comparison table 3. Five strengths of the business 4. Five weaknesses or gaps 5. Five practical opportunities 6. A prioritized 30-day action plan 7. A list of claims requiring human verification Write in plain English. Avoid generic advice. Connect every recommendation to evidence found during the research. This is much better than writing: “Research this business and tell me how it can grow.” A good workflow defines the goal, evidence, limitations, output and quality standard. Step 3: Look for problems that matter A report becomes valuable when it finds something the owner can act on. Here are a few examples. The offer is unclear Three competitors advertise a clear new-customer package, but the client does not. Action: Create a visible introductory offer with a clear price, audience and call to action. The website explains too much and sells too little The homepage describes the company but does not tell visitors what to do next. Action: Add one primary call to action, such as booking, requesting a quote or calling the business. Customers reveal an overlooked advantage Public reviews repeatedly praise fast service, but the business barely mentions speed in its marketing. Action: Turn “fast response” into a central positioning message and support it with verified customer evidence. A service has demand but no dedicated page Customers ask about a service in reviews, but the website has no page explaining it. Action: Create a dedicated service page answering the questions customers already ask. Competitors ignore a specific audience Every company uses generic messaging, but none speaks directly to first-time customers, families, newcomers or another relevant group. Action: Create a targeted offer and content page for that audience. Notice the difference between that and: “Post more on social media.” The first recommendations are connected to evidence. The second is generic advice anyone could produce. Step 4: Make Kimi challenge its own work Never send the first AI-generated report to a client. AI can misunderstand a page, combine unrelated information or make an interpretation sound like a proven fact. Run a separate audit: Audit the competitor opportunity report. For every important claim: 1. Identify its supporting source. 2. Check whether the source directly supports the claim. 3. Mark unsupported statements. 4. Mark opinions presented as facts. 5. Identify outdated information. 6. Identify conflicting sources. 7. Identify recommendations that are too generic. 8. List anything requiring manual human verification. Use these labels: VERIFIED FACT REASONABLE INTERPRETATION UNCONFIRMED REQUIRES CLIENT INPUT Then produce a corrected version using only supported information. After that, manually open the important sources. Check: - business names; - locations; - services; - prices; - review dates; - quoted statements; - statistics; - recommendations involving regulated industries. Your reputation will depend more on accuracy than speed. Step 5: Turn the research into something a client can use Do not send the owner a raw AI conversation. Package the work professionally. Your report should contain five sections: 1. Executive Summary Explain the three most important findings. 2. Competitor Comparison Compare positioning, services, pricing, trust signals and calls to action. 3. Customer Review Patterns Show what customers repeatedly praise, question or criticize. 4. Missed Opportunities Identify useful gaps the business could realistically pursue. 5. 30-Day Action Plan Turn the research into weekly actions with priorities, owners and expected outcomes. Use this prompt: Turn the approved research into a professional five-page client report. Audience: A busy small-business owner with limited technical knowledge. Structure: Page 1: Executive Summary Page 2: Competitor Comparison Page 3: Customer Review Patterns Page 4: Missed Opportunities Page 5: Prioritized 30-Day Action Plan Design requirements: - clean and professional; - short paragraphs; - simple tables; - one main message per page; - recommendations ordered by impact and effort; - no unnecessary technical language; - no mention of prompts or internal AI reasoning. Do not introduce new facts. Use only information from the verified report. Kimi’s Agent products can generate reports, documents, spreadsheets, slides and other client-facing assets from research. (Kimi) Step 6: Put a simple price on it Do not begin with complicated enterprise packages. Test three straightforward offers. Starter — $99 - three competitors; - short PDF report; - three recommendations. Standard — $249 - five competitors; - website and public-review analysis; - five recommendations; - 30-day action plan; - report and presentation. Monthly — $399 - monthly competitor updates; - offer and website changes; - new public-review patterns; - content opportunities; - one written briefing or call. These are test prices, not guaranteed market rates. Your experience, location, target industry, report quality and depth will affect what a customer is willing to pay. For your first project, you can offer a pilot price in exchange for: - honest feedback; - a testimonial; - permission to use anonymized results as a case study. Do not promise sales, revenue or specific growth. Promise a careful analysis and a practical set of recommendations. Step 7: Find your first 20 potential clients Create a simple spreadsheet with these columns: BusinessContactWebsiteMain CompetitorVisible OpportunityStatusFollow-Up Look for businesses that have: - several active competitors; - a confusing or outdated website; - no clear offer; - inconsistent public information; - repeated customer concerns; - weak calls to action; - publicly listed contact details. Do not automatically contact everyone. Study each business and record one genuine observation. That observation will make your message relevant. Step 8: Send a useful message Do not write: “I run an AI agency that can transform your business.” Most owners have already received messages like that. Send something specific: Subject: One competitor opportunity I noticed for [BUSINESS NAME] Hi [NAME], I was reviewing local [BUSINESS CATEGORY] businesses and noticed that several competitors are promoting [SPECIFIC OFFER OR ADVANTAGE]. Your business appears to have an opportunity around [SPECIFIC OBSERVATION]. I create short competitor opportunity reports for local businesses. The report compares offers, websites and public customer feedback, then turns the findings into a practical 30-day action plan. I prepared a brief sample showing two possible opportunities for [BUSINESS NAME]. Would it be useful if I sent it to you? Thanks, [YOUR NAME] This message does not pressure the owner to buy immediately. It starts a conversation around a real observation. Send five thoughtful messages, not 500 generic ones. Also respect applicable anti-spam requirements, platform rules and requests not to be contacted. Step 9: Turn a one-time report into a monthly service A one-time report can help a client understand the market today. A monthly service helps the client see what changes. You could monitor: - new competitor offers; - pricing changes; - new services; - website updates; - public-review trends; - seasonal promotions; - new locations; - frequently asked customer questions; - emerging content opportunities. Kimi Work officially supports scheduled one-time and recurring tasks, which could help organize a repeatable monitoring workflow. (Kimi) A monthly update does not need to be enormous. It could be a three-page briefing: - What changed - Why it matters - What the client should do That is how a small report can become recurring revenue. Your 48-hour launch plan Saturday morning Choose one business category and one city. Do not aim for the perfect niche. Choose one where businesses have visible competitors and enough public information to analyze. Saturday afternoon Select one sample business and three competitors. Create a research folder and collect the main websites, public reviews, service pages and publicly listed offers. Saturday evening Run the research workflow. Ask Kimi to create the comparison, detect opportunities and record sources. Save the raw findings separately from the final report. Sunday morning Audit every important claim. Open the sources manually. Remove weak conclusions, unsupported statements and information that may be outdated. Sunday afternoon Package the sample. Create the executive summary, competitor table, review patterns, missed opportunities and 30-day plan. Sunday evening Find 20 potential clients. Research the first five carefully and send five personalized messages. Your weekend goal is not to create a giant agency. It is to complete: - one offer; - one niche; - one sample; - one repeatable workflow; - five real conversations. That is enough to test whether the idea has demand. Final quality checklist Before sending a report, confirm: Research - Every important fact has a source. - Prices and services have been checked manually. - Old information is clearly dated. - Competitors were compared using similar criteria. Analysis - Facts and interpretations are separated. - Recommendations are based on evidence. - The report includes both strengths and weaknesses. - Claims about customers are based only on public feedback. Deliverable - The owner can understand it quickly. - Every recommendation has a clear next step. - The report is visually clean. - No private or sensitive information is included. - The document does not contain raw prompts or AI reasoning. Business - Your offer explains the result, not the technology. - The price and scope are clear. - You do not guarantee revenue. - The client understands what is and is not included. Not interested in selling a service? Use the same system in your own life. You can use Kimi K3 to: - compare training programs before enrolling; - research a career change; - compare expensive products; - organize a large folder of documents; - study a market before launching a business; - compare travel options; - create a structured learning plan; - research tools for your workplace; - turn scattered information into a decision brief. The workflow remains the same: Define the decision → gather evidence → compare options → challenge assumptions → verify claims → create an action plan. That is more valuable than simply asking an AI for “the best choice.” The real opportunity Kimi K3 is impressive because of its scale. But 2.8 trillion parameters will not create a business for you. A useful offer might. A verified report might. A repeatable process might. A conversation with a real customer might. The people who benefit most from the next generation of AI will not necessarily be those who memorize every architecture detail. They will be the people who can take a messy real-world problem and produce something clear: Here is what the evidence shows. Here is the opportunity. Here is what you should do next. Do not spend the entire weekend debating whether Kimi K3 is better than another model. Use the weekend to create something useful with it. Build practical AI systems, not bigger prompts Subscribe to Open Cloud AI for actionable guides on AI, cloud, agents, cybersecurity and real-world business workflows.

9 copy techniques for ad haters Plus: Master Prompt Every month, we run live in-person bootcamps for readers who want to go deeper with AI— small cohorts, taught live, focused on one specific AI skill. If you want to hear about them first (before we announce publicly), click here. Hey, Shlomo here. Let’s be real. Most people HATE ads. Because most ads talk to them like they’re dumb. But these 9 timeless copywriting techniques help me write ad copy that can make even the worst ad haters smile... Let’s dive in (don’t miss the master prompt at the end): 1. Self-Deprecation Admit your flaws to make your brand more relatable. 2. Understatement Find your biggest advantage. Say it like it’s not a big deal. 3. Downsides When your product benefit creates a new problem. 4. Reverse World Swap words and expectations to hook readers and make a point. 5. Self-Aware Ads Break the 4th wall, and admit it’s an ad to disarm skeptics. 6. Chuck Norris Exaggerate the benefit until it becomes a funny scene. 7. Tricky Threes Two things establish a pattern. The third one breaks it. Surprise. 8. Reverse Psychology Tell readers not to do what you want them to do or vice versa. 9. Slap ‘N Hug Start with something negative, then flip it into a positive. Master prompt Run this prompt to try all these techniques at once: ✂️ Paste this aI prompt into Claude or ChatGPT Context [AI, don’t run before user fills in] My website: {e.g., clickup.com} My promise: {e.g., keep all your team’s projects and communication in one place} Pain point I solve: {e.g., work scattered across email, chat, and a dozen tools} –––––––––––– Task: Write 18 ad headlines using the 9 techniques below, two per technique. Label each headline with its technique. Headlines only, no explanations. –––––––––––– Self-Aware Ads: break the fourth wall and admit it’s an ad to disarm ad-haters. Example: Superside: “We’re too busy designing for the top tech companies to care about our billboards.” Chuck Norris: exaggerate the benefit until it becomes a funny, over-the-top scene. Example: Bai: “Flavor so juicy you could sell it to a tabloid.” Tricky Threes: list two normal things to set a pattern, then break it with a surprising third. Example: Bumble: “Tall, dark, and actually reads that book you recommend.” Reverse Psychology: tell readers not to do what you want them to do (or vice versa). Example: Wathan Funeral Home: “Text and drive.” Slap ‘N Hug: open with something negative, then flip it into a positive. Example: Nike: “Some people take tennis too seriously. We make shoes for them.” Self-Deprecation: admit your own flaws or weaknesses with confidence to make the brand more relatable. Example: Buckley’s: “It tastes awful. And it works.” Understatement: take your biggest advantage and say it like it’s no big deal. Example: Uber One: “A membership for people who eat food and go places.” Downsides: joke about an absurd new problem caused by the product working too well. Example: Franklin Park Zoo: “7 Acres. 300 Birds. Wear A Hat.” Reverse World: swap words and expectations around to hook readers and make a point. Example: Evita Anti-Aging Balm: “Tock Tick.” –––––––––––– Go! You made it! These 9 techniques come from my complete library of 63 timeless copywriting techniques at CopyTemplates.com. And as a Write With AI subscribers you get 30% off with code WWAI30. That’s it! —Shlomo