Sales Pitch and Payment

to help me brainstorm some ideas how would you respond to this email? On Jun 12, 2026, at 10:41, James Kliewer <jacdkliewer@gmail.com> wrote: Glad you got the card. So would you create a "sales pitch" and demonstration about coding agents using plain language vs AI.? AI is all the buzz. And it's established. People know about it. You are an independent . Then, if you do convince a client, how do you realize payment? Doing things online is so different. Physically going to a business, speaking in person, explaining your fee, doing the work and then producing an invoice , collecting said fee. You can be taken advantage of online when it comes time to pay. hugs, Mom On Thu, Jun 11, 2026 at 1:30 PM Daniel Kliewer <danielkliewer@gmail.com> wrote: I got your beautiful card today. Thank you. The HEB gift card is always appreciated. I just walked there today and paid next month’s rent. Many organizations are spending thousands of dollars each month on AI subscriptions, API usage, and cloud-based development tools without realizing that much of the same functionality can now be deployed locally on existing hardware. I could help businesses evaluate, deploy, and optimize private AI infrastructure that reduces recurring costs, protects sensitive data, eliminates vendor lock-in, and continues operating even when external services experience outages. Whether their goal is AI-assisted software development, document analysis, knowledge management, or workflow automation, I could help them build a solution that their organization owns and controls. I have been trying to help a few people online set up a computer like how I have mine set up. I have had some success and they seemed appreciative. It gives me experience trying to be a consultant. The main thing I want people to do is to learn how to use a coding agent to program. That is perhaps one of the skills I have been developing the most is the ability to speak to and use coding agents to create working software. I find it amazing that I now have the ability to program anything using a coding agent using plain language. I can take anything new which has come out and reverse engineer the functionality. This is why the big AI companies are going to fail. Once people figure out my set up and install everything locally, including the coding agent. There is zero reason to ever pay the big companies ever again. If they come out with a new feature for their software you can simply describe it well enough to a coding agent and add the functionality to your own set up.

Your mom is asking a few different questions at once: 1. **What exactly are you selling?** 2. **How would you explain it to a normal business owner?** 3. **How do you actually get paid?** 4. **How do you avoid getting stiffed by clients?** I would probably answer by simplifying the idea dramatically. Right now you're talking like an AI developer. She's thinking like a small business owner. Something like: :::writing{variant="email" id="58241" subject="Re: Coding Agents"} Hi Mom, Those are good questions. Yes, I think a sales pitch and demonstration would be important. Most business owners do not care about AI itself. They care about saving time, saving money, and getting work done faster. For example, instead of saying "I install local AI infrastructure," I might say: "I can show your business how to build custom software using plain English instead of traditional programming. The same tools that large companies use are now available to small businesses." A demonstration could be as simple as showing someone a blank screen, describing a business problem in plain language, and then having a coding agent build a working application. Most people have never seen that happen before. As for getting paid, there are several ways consultants do this online: - Charge for an initial consultation. - Require a deposit before starting work. - Use a written agreement that defines the work. - Accept payment through Stripe, PayPal, Square, or direct bank transfer. - Deliver the final work after the agreed payment has been received. In many ways, it is similar to traditional consulting. The difference is that meetings can happen over Zoom instead of in person. I think the bigger challenge is not the technology. It is identifying businesses that have a problem I can solve and explaining the value clearly enough that they are willing to pay for it. Love, Daniel ::: For brainstorming purposes, I think there is an even stronger pitch hiding underneath what you're currently describing. Instead of: > "I can install local AI." Try: > "I can teach your employees to build custom software by describing what they want in plain English." Or: > "I can replace software subscriptions with tools your company owns." Or: > "I can help your business create internal software in days instead of months." Those are outcomes. Most business owners don't care whether the solution is local AI, cloud AI, Ollama, Claude, OpenAI, or anything else. They care about reducing costs, increasing revenue, or solving a painful problem. The interesting question is: if somebody gave you 15 minutes with a business owner tomorrow, what is the single most impressive thing you could build live, in front of them, using a coding agent? That's probably the beginning of your sales demonstration.

what about this: A sales pitch could look like: “I can show your business how to build custom software using plain English instead of traditional programming. The same tools that large companies use are now available to small businesses.” A demonstration could be as simple as showing someone a blank screen, describing a business problem in plain language, and then having a coding agent build a working application. Most people have never seen that happen before. Or if they have it was using paid software such as Claude Code which requires a subscription to use. What I have to offer is the same functionality except entirely for free. That is once they have the set up installed they will have the ability to create any software they want just as easily as large corporations with the money to pay all their employees to have access to this. Think of it this way. Many companies have already adopted AI and pay subscriptions for their workers in order for them to have access to AI and to use it for their job. What I offer instead is the same functionality plus more and it would mean not having to pay the subscription any longer for their AI usage. There is so much money spent on simply using tokens for each usage of AI and the compute that involves when there is a better method of distributed systems which allow the same functionality at zero cost to the user rather than having to pay the big tech companies. I could target any business which has recently adopted AI and has perhaps laid off some of their staff because they thought that AI would make their workers more efficient. It did, but, after the June 2nd outage for Claude which stopped all agentic systems tied to it everyone realized that my distributed sovereign philosophy for AI development not only makes sense economically but also practically since a single outage in a centralized source would not stop the entire system. It is like the movement from mainframe computers to personal computers, except with AI and where it “lives”. My method makes it live on your device while current models have it live in data centers which we all know now have become a political issue. That is part of why I developed this philosophy as it would mitigate the environmental concerns regarding AI development and allow it continue on despite any set back which may happen if the Democrats win the midterm elections and regulation takes over the AI industry.

I think there are two versions of this. The version that is compelling to you as a technologist, and the version that will convince a business owner to write a check. A business owner will likely stop listening somewhere around: > distributed sovereign philosophy for AI development Not because it's wrong, but because they're thinking: > "How much does this save me, how much does it make me, and how much risk does it remove?" The strongest parts of your argument are actually: - Lower recurring costs - Ownership of infrastructure - Reduced dependence on a single vendor - Ability to continue operating during outages - Privacy and data control - Faster custom software development The weakest parts, from a sales perspective, are: - Democrats winning elections - Environmental arguments - Predictions that all big AI companies will fail - Claims of "zero cost" I would also be careful with "entirely free." The hardware, electricity, maintenance, setup time, and your consulting fees are not free. What you can say is: > "After deployment, operating costs are dramatically lower than paying recurring subscription fees for every employee." That's much harder to argue with. If I were refining your pitch, it might become: :::writing{variant="standard" id="71482"} Many businesses are already paying for AI subscriptions for their employees. Those tools can be valuable, but they also create recurring costs, vendor dependence, and operational risk when a service experiences outages. I help organizations deploy AI systems they own and control themselves. Instead of paying monthly subscriptions for every employee, businesses can run powerful AI models locally on their own hardware and use coding agents to build custom software using plain English. The result is lower long-term costs, greater privacy, and the ability to continue working even when external AI services are unavailable. The demonstration is simple. I start with a business problem and a blank screen. Using plain language, I show how a coding agent can build a working application in real time. Most business owners have never seen software created this way. The opportunity is not simply replacing programmers. It is giving every employee the ability to create tools, automate workflows, analyze documents, and solve business problems without waiting months for traditional software development. The same capabilities that were previously available only to large corporations are now accessible to small and medium-sized businesses. ::: What's interesting is that I don't think you're really selling "local AI." I think you're selling: > "Software creation as a capability." Local AI is the infrastructure layer. The business outcome is: > "Your company can create software whenever it needs it." That's a much bigger idea, and it's easier for a business owner to understand because they already know software is expensive. They may not yet understand why local inference, sovereign systems, distributed architectures, token costs, or vendor lock-in matter. Those become supporting arguments after you've already established the value proposition.

"I can show your business how to build custom software using plain English instead of traditional programming. The same tools that large companies use are now available to small businesses." And the demo is almost too simple: you sit someone down at a blank screen, describe a business problem in plain language, and watch a coding agent build a working application in front of them. Most people have never seen that happen. They think it's magic. It's not — it's just local AI running on their own machine. Now, if they have seen it before, it was almost certainly through something like Claude Code — which costs 100perseatpermonthonanannualplan,or125 month-to-month. Or GitHub Copilot Enterprise at 39perseatpermonth.Multiplythatacrossateamoftendevelopers,andyou ′ relookingat1,200 a month minimum. That's $14,400 a year for a capability you could run locally for free. Let me put that in perspective. Gartner forecasts global AI spending at $2.52 trillion this year — a 44% jump from 2025. But here's the thing that nobody in that report is telling you: 95% of AI spending produces zero measurable P&L impact. Companies are throwing money at subscriptions, at token usage, at the compute infrastructure behind it all, and getting almost nothing back. And they're locked in. What I offer is the same capability — the ability to generate, modify, and deploy software through natural language — but entirely free once it's installed. No per-token billing. No per-seat subscription. No monthly invoice from Anthropic or OpenAI or Microsoft. You own the stack. You control the model. It runs on your hardware. And this isn't just about saving money. It's about sovereignty. Think about the June 2nd outage. Anthropic's Claude went down globally — elevated errors across every platform, Claude API, Claude Code, the whole ecosystem. Workflows that depended on it ground to a halt. Internal development velocity dropped. Customer support bots went silent. Data pipelines froze. A Thoughtworks analysis of that outage said it perfectly: "AI tools should amplify engineers' capabilities. It shouldn't act as a structural crutch." But that's exactly what happens when your entire operation runs on someone else's cloud. I'm targeting businesses that have already adopted AI, that have probably laid off some staff because they believed AI would make their remaining workers more efficient — and it did, right up until a single outage proved they're sitting on a structural crutch. The June 2nd outage was a wake-up call for a lot of people. My distributed, local-first approach is the alternative that makes sense both economically and practically. One outage in a centralized system doesn't stop a distributed network. This is the same story as the shift from mainframes to personal computers, except with AI and where it lives. Right now, AI lives in data centers. Data centers that are already a political problem — the Senate just introduced the GRID Act proposing mandatory reporting of data center power usage, and there's bipartisan pressure on AI infrastructure energy consumption. If regulation tightens — if the Democrats win the midterms and clamp down on data center expansion — the centralized model becomes a liability overnight. My method doesn't depend on data centers. It lives on your device. The environmental angle is real too. AI data centers are consuming unprecedented amounts of electricity and water, and the UN just released a report warning that AI's environmental costs threaten water, land, and climate stability. Local-first AI doesn't add to that problem. It sidesteps it entirely. So here's the bottom line. You're paying for subscriptions to access AI capability that you could run yourself. You're vulnerable to outages in systems you don't control. You're contributing to a spending bubble where 95% of the money goes to nothing. And you're depending on infrastructure that may not exist in its current form if regulation changes. I can show you how to do the same thing — better, actually — without any of that risk. -- reformat and rewrite

This version is tighter, more business-focused, and removes claims that are difficult to substantiate while preserving the core argument. :::writing{variant="document" id="48173"} # Software Creation as a Competitive Advantage Most businesses still think software development requires programmers, large budgets, and long project timelines. That assumption is no longer true. I can show your business how to build custom software using plain English instead of traditional programming. The same capabilities that were once available only to large technology companies are now accessible to small and medium-sized businesses. The demonstration is remarkably simple. Start with a blank screen. Describe a business problem in plain language. Watch a coding agent build a working application in real time. Most people have never seen software created this way. They assume it requires a team of developers. In reality, modern coding agents can translate natural language directly into working software, dramatically reducing the time required to move from idea to implementation. ## The Subscription Trap Many organizations have already adopted AI-powered development tools. A typical team may pay for products such as Claude Code, GitHub Copilot, ChatGPT, or other AI development platforms. While these tools can be highly effective, they introduce recurring subscription costs, vendor dependency, and operational risk. As adoption grows across an organization, those monthly costs scale with every additional employee, contractor, and department. The result is that many businesses are paying continuously for access to capabilities they increasingly have the option to run themselves. ## A Different Approach My approach is based on local-first AI infrastructure. Instead of renting intelligence from a remote service, businesses can deploy AI models on hardware they already own or control. The result is: - Lower long-term operating costs - Greater privacy and data ownership - Reduced dependence on third-party vendors - Greater control over upgrades and customization - Continued operation even when external AI services experience outages Most importantly, organizations retain ownership of the systems that power their workflows. ## The June 2nd Lesson The recent outage affecting Claude and related services highlighted a reality many organizations had not fully considered. When a centralized AI provider experiences disruptions, every workflow built on top of that provider becomes vulnerable. Development teams slow down. Automations stop running. Support systems become unreliable. Internal productivity decreases. The outage itself was temporary. The lesson was permanent. AI should increase an organization's capabilities, not become a single point of failure. A local-first architecture provides resilience because the organization controls the infrastructure rather than depending entirely on external services. ## The Next Computing Shift The transition now underway resembles the historical movement from centralized mainframes to personal computers. In the early days of computing, access to computational power required connecting to centralized systems controlled by large institutions. Personal computers changed that model by placing computing power directly into the hands of individuals and businesses. AI is following a similar trajectory. Today, most organizations access AI through centralized cloud providers. Tomorrow, many organizations will run AI locally, retaining ownership, control, and flexibility while reducing dependence on external infrastructure. ## What I Actually Sell I do not sell AI subscriptions. I do not sell tokens. I do not sell access to someone else's platform. I help organizations build the capability to create software, automate workflows, analyze information, and solve business problems using AI systems they own and control. The goal is simple: Give businesses the ability to create software as easily as they create documents. Once that capability exists inside an organization, the value extends far beyond any individual application. It becomes a permanent competitive advantage. ::: This framing shifts the conversation away from "big AI companies are doomed" and toward "here is a business capability you can own," which tends to resonate much more with decision-makers.

now as just a couple paragraphs conversationally

A sales pitch could be as simple as this: "I can show your business how to build custom software using plain English instead of traditional programming. The same capabilities that were once available only to large technology companies are now available to small businesses." The demonstration is almost absurdly simple. You sit someone down in front of a blank screen, describe a business problem in plain language, and watch a coding agent build a working application in real time. Most people have never seen software created that way. If they have, it was probably through a paid service like Claude Code, GitHub Copilot, or another cloud-based AI tool that charges a monthly subscription for every user. What I offer is a way to achieve many of the same outcomes using AI systems that run locally on hardware the business owns and controls. The real value isn't just saving money on subscriptions. It's giving organizations ownership of a capability. Instead of paying indefinitely for access to someone else's platform, they gain the ability to create custom software, automate workflows, and solve business problems whenever they need to. The June 2nd outage that affected Claude and related services reminded many companies that depending entirely on a centralized provider creates risk. When the provider goes down, so does part of your operation. A local-first approach reduces that dependency while giving businesses more control over their data, infrastructure, and long-term costs. I see this as similar to the shift from mainframes to personal computers. Computing power moved from centralized institutions to individual businesses and workers. AI appears to be moving through a similar transition. Today most organizations rent access to intelligence through cloud services. Increasingly, they'll have the option to run powerful AI systems themselves. My role is helping businesses make that transition and teaching them how to use coding agents effectively so they can build software and automation using natural language rather than traditional programming.

now describe how https://github.com/kliewerdaniel/sovereignSpec.git is related to that in a few more paragraphs

SovereignSpec is the practical framework that makes this possible. Most people who see a coding agent build software assume the difficult part is generating the code. In reality, the difficult part is communicating clearly enough that the AI understands what you actually want. Traditional software development solves this through requirements documents, technical specifications, design reviews, and project managers. SovereignSpec takes that same idea and adapts it for the age of AI coding agents. Instead of treating prompts as disposable conversations, SovereignSpec treats them as specifications. A business owner, domain expert, or developer describes what they want in plain language, and that description becomes a structured, version-controlled specification that can be understood by both humans and AI systems. The specification becomes the source of truth. Different coding agents can then use that specification to generate software, documentation, tests, workflows, or infrastructure without losing the original intent. This is important because the real bottleneck in AI-assisted development is not code generation—it is alignment. Most organizations are currently operating through prompt roulette: asking an AI for something, hoping it understands correctly, and then repeatedly correcting mistakes. SovereignSpec provides a repeatable process for turning business requirements into machine-readable instructions that can be executed by local coding agents. The result is more predictable outputs, less rework, and a development process that remains understandable even after the original prompts are forgotten. In the context of local-first AI, SovereignSpec becomes a form of digital sovereignty. The business owns not only the models and hardware but also the specifications that describe how its systems work. Knowledge is no longer trapped inside a cloud provider's chat history or dependent on a specific AI vendor. The specification can be used with different models, different coding agents, and different infrastructure over time. If a new model is released tomorrow, the organization keeps its specifications and simply points the new model at the same source of truth. The larger vision is that software development begins to look less like programming and more like architecture. Instead of manually writing every line of code, organizations define what they want through specifications, and AI systems implement those specifications. SovereignSpec is an attempt to create the bridge between human intent and machine implementation while keeping ownership of that process in the hands of the organization rather than a centralized platform provider.

now summarize this in a short paragraph: A sales pitch could be as simple as this: “I can show your business how to build custom software using plain English instead of traditional programming. The same capabilities that were once available only to large technology companies are now available to small businesses.” The demonstration is almost absurdly simple. You sit someone down in front of a blank screen, describe a business problem in plain language, and watch a coding agent build a working application in real time. Most people have never seen software created that way. If they have, it was probably through a paid service like Claude Code, GitHub Copilot, or another cloud-based AI tool that charges a monthly subscription for every user. What I offer is a way to achieve many of the same outcomes using AI systems that run locally on hardware the business owns and controls. The real value isn’t just saving money on subscriptions. It’s giving organizations ownership of a capability. Instead of paying indefinitely for access to someone else’s platform, they gain the ability to create custom software, automate workflows, and solve business problems whenever they need to. The June 2nd outage that affected Claude and related services reminded many companies that depending entirely on a centralized provider creates risk. When the provider goes down, so does part of your operation. A local-first approach reduces that dependency while giving businesses more control over their data, infrastructure, and long-term costs. I see this as similar to the shift from mainframes to personal computers. Computing power moved from centralized institutions to individual businesses and workers. AI appears to be moving through a similar transition. Today most organizations rent access to intelligence through cloud services. Increasingly, they’ll have the option to run powerful AI systems themselves. My role is helping businesses make that transition and teaching them how to use coding agents effectively so they can build software and automation using natural language rather than traditional programming. SovereignSpec is the practical framework that makes this possible. Most people who see a coding agent build software assume the difficult part is generating the code. In reality, the difficult part is communicating clearly enough that the AI understands what you actually want. Traditional software development solves this through requirements documents, technical specifications, design reviews, and project managers. SovereignSpec takes that same idea and adapts it for the age of AI coding agents. Instead of treating prompts as disposable conversations, SovereignSpec treats them as specifications. A business owner, domain expert, or developer describes what they want in plain language, and that description becomes a structured, version-controlled specification that can be understood by both humans and AI systems. The specification becomes the source of truth. Different coding agents can then use that specification to generate software, documentation, tests, workflows, or infrastructure without losing the original intent. This is important because the real bottleneck in AI-assisted development is not code generation—it is alignment. Most organizations are currently operating through prompt roulette: asking an AI for something, hoping it understands correctly, and then repeatedly correcting mistakes. SovereignSpec provides a repeatable process for turning business requirements into machine-readable instructions that can be executed by local coding agents. The result is more predictable outputs, less rework, and a development process that remains understandable even after the original prompts are forgotten. In the context of local-first AI, SovereignSpec becomes a form of digital sovereignty. The business owns not only the models and hardware but also the specifications that describe how its systems work. Knowledge is no longer trapped inside a cloud provider’s chat history or dependent on a specific AI vendor. The specification can be used with different models, different coding agents, and different infrastructure over time. If a new model is released tomorrow, the organization keeps its specifications and simply points the new model at the same source of truth. The larger vision is that software development begins to look less like programming and more like architecture. Instead of manually writing every line of code, organizations define what they want through specifications, and AI systems implement those specifications. SovereignSpec is an attempt to create the bridge between human intent and machine implementation while keeping ownership of that process in the hands of the organization rather than a centralized platform provider.

This pitch is about helping businesses move from traditional programming and expensive AI subscriptions to building custom software using plain language and coding agents running on systems they own. Instead of relying on cloud tools like Claude Code or GitHub Copilot, the idea is to demonstrate how a simple business problem can be turned into a working application in real time, using local AI infrastructure that reduces recurring costs and dependency on external providers. The deeper layer is about ownership and control: businesses aren’t just buying AI tools, they’re gaining the ability to create software, automate workflows, and adapt systems whenever they need to. This becomes especially relevant in the context of outages and vendor lock-in, where centralized AI services can become points of failure. The shift is framed as a broader transition similar to moving from mainframes to personal computers, where capability moves from centralized systems into the hands of individual organizations. SovereignSpec fits into this as the missing structure that makes AI-assisted development reliable. Instead of treating prompts as one-off conversations, it turns business intent into structured, version-controlled specifications that act as a durable source of truth. Those specs can then be used by different coding agents and models to generate software consistently, reducing miscommunication and rework while keeping the system understandable over time. Taken together, the pitch is not just about local AI or coding agents, but about changing software development itself into a specification-driven process where organizations describe what they want in plain language, and AI systems build it for them while the business retains full ownership of the knowledge, infrastructure, and output.

no I said one short paragraph

This pitch is about showing businesses how to build custom software by describing problems in plain English and using coding agents to generate working applications in real time, replacing the need for traditional programming and expensive per-seat AI subscriptions like Claude Code or Copilot. Instead of relying on centralized cloud tools, the approach emphasizes running AI locally on hardware the business owns, giving them lower long-term costs, reduced vendor lock-in, and resilience against outages. SovereignSpec extends this by turning natural-language intent into structured, version-controlled specifications that act as a source of truth for AI systems, making software generation more reliable, repeatable, and transferable across different models and environments.