In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the AI wars, switching AI, and why relying on a single AI vendor can jeopardize your business continuity. You’ll discover how to build an abstraction layer that lets you swap models without rebuilding your workflows and see practical no‑code tools and open‑weight models you can use as a safety net. You’ll understand the essential documentation and backup practices that keep your AI agents running. Watch the full episode to protect your AI strategy.

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What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn: In this week’s In Ear Insights, it is the AI Wars. Katie, you had some thoughts and some observations about the most recent things going on with Anthropic, with OpenAI, with Google XAI and stuff like that. So at the table, what’s going on?

Katie Robbert: I don’t want to get too deep into the weeds about why people are jumping ship on OpenAI and moving toward the cloud. That’s in the news, it’s political, you can catch up on that. The short version is that decisions from the top at each of these companies have been made that people either agree with or don’t based on their own values and the values of their companies. When publicly traded companies make unpopular decisions that don’t align with the majority of their user base, people jump ship. They were like, okay, I don’t want to use you.

We’ve seen it with Target and many other companies that made decisions people didn’t feel aligned with their personal values. Now we are seeing people abandoning OpenAI and signing on to Anthropic’s Claude. That’s what I wanted to chat about today because we talk a lot about business continuity and risk management. What happens when you get too closely tied to one piece of software and something goes wrong? We’ve talked about this on past episodes in theory because, up until now, software outages have generally been temporary. You don’t often see a mass exodus of a very popular piece of software that people have built their entire businesses around.

Before we get into what this means for the end user and possible solutions, Chris, I would like to get your thoughts, maybe your cat’s thoughts on what’s going on.

Christopher S. Penn: One of the things we’ve said from very early on in the AI space, because it changes so rapidly, is that brand loyalty to any vendor is generally a bad idea. If you were a hater of Google Bard—for good reason—Bard was a terrible model. If you said, I’m never going to touch another Google product again, you would have missed out on Gemini and Gemini 3 and 3.1, which is currently the top state‑of‑the‑art model. If you were all in on Claude, when Claude 2.1 and 2.5 came out and were terrible, you would have missed out on the current generation of Opus 4.6 and so on.

Two things come to mind. One, brand loyalty in this space is very dangerous. It is dangerous in tech in general. Not to get too political, but the tech companies do not care about you, so there’s no reason to give them your loyalty. Second, as people start building agentic AI, you should think about abstraction layers. This concept dates back to the earliest days of computing: we never want to code directly against a model or an operating system. Instead we want an abstraction layer that separates our code from the machinery. It’s like an engine compartment in a car—you should be able to put in a new engine without ripping apart the entire car.

If you do that well when building AI agents, when a new model comes along—regardless of political circumstances or news headlines—you can pull the old engine out, install the new one, and keep delivering the highest‑quality product.

Katie Robbert: I don’t disagree with that, but that is not accessible to everybody, especially smaller businesses that view software like OpenAI or Google’s Gemini as desperately needed solutions. We’ve relied on Claude and Co‑Work, its desktop application, heavily. Over the weekend I realized how reliant I’ve become on it in the past two weeks. If it stopped working, what does that mean for the work I’m trying to move forward? That’s a huge concern because I don’t have the coding skills or resources to replicate it right now.

What I’ve been doing in Co‑Work is because we’re limited on resources, but Co‑Work has advanced to the point where I can replicate what I would need if I hired a team of designers, developers, and marketers. It shook me to my core that this could go away. So what does that mean for me, the business owner, in the middle of multiple projects if I can’t access them? This morning Claude had an outage—unsurprisingly, the servers were overloaded because people are stepping away from OpenAI and moving into Claude. Claude released an ad: “Switch to Claude without starting over. Brief your preferences and context from other AI providers to Claude. With one copy‑paste, Claude updates its memory and picks up right where you left off. Memory is available on all paid plans.”

For many people the ability to switch from one large language model to another felt like a barrier because everything built inside OpenAI couldn’t be transferred. Claude removed that barrier, opening the floodgates, and their servers were overloaded. Users who had been using the system regularly were like, what do you mean? I can’t get the work done I planned for this morning.

Christopher S. Penn: There are two different answers depending on who you are. For you, Katie, as the CEO and my business partner, I would come over, say we’re going to learn Claude code, install the terminal application, and install Claude code router, which allows you to switch to any model from any provider so you can continue getting work done. Unfortunately, that isn’t a scalable option for everyone in our community.

My suggestion for others is that it’s slightly harder but almost every major company has an environment where you can install a no‑code solution that provides at least some of those capabilities. Google’s is called Anti‑Gravity. OpenAI’s is called Codex. Alibaba’s can be used within tools like Client or Kil. If you have backed up your prompts and workflows, you can move them into other systems relatively painlessly. For example, Google’s Anti‑Gravity supports the skills format, so if you’ve built skills like the Co‑CEO, you can bring them into Anti‑Gravity. It’s not obvious, but you can port from one system to another relatively quickly.

Katie Robbert: That brings us to the point that software fails—it’s just code. What is your backup plan if the system you’re heavily reliant on goes away? We’ve always said hypothetically, “if it goes away…,” and now we’re at that point. Not only are people leaving a major software provider, they are also struggling with switching costs. They’re struggling to bring their stuff over because everything lives within the system. A lot of people are building and not documenting, and that’s a problem.

Christopher S. Penn: It is a problem. If you’ve been in the space for a while and understand the technology, backups and fallback systems have gotten incredibly good. About a month ago Alibaba released Quinn 3.5 in various sizes. The version that runs on a nice MacBook is really good—scary good. It’s about the equivalent of Gemini 3 Flash, the day‑to‑day model many folks use without realizing it. Having an open‑weights model you can install on a laptop that rivals state‑of‑the‑art as of three months ago is nuts.

The challenge is that it’s not well documented, but it’s something we’ve been saying for two or three years: if you’re going all in on AI, you need a backup system that is capable. The good news is that providers like Alibaba, Quinn, Kimmy, Moonshot, and Jipu AI—many Chinese companies—ensure the technology isn’t going away. So even if Anthropic or OpenAI went out of business tomorrow, you have access to the technologies themselves. You can keep going while everyone else is stuck.

Katie Robbert: If it’s not a concern for executives mandating AI integration, it should open eyes to the possibility of failure. Let’s be realistic—it’s not going to happen tomorrow, but it makes me think of the panic when Google Analytics switched from Universal Analytics to GA4. The systems aren’t compatible, data definitions changed, and companies lost historic data. Fortunately we had a backup plan. Chris, you always ran Matomo in the background as a secondary system in case something happened with Google Analytics, so we still had historic data.

We’re at a pivotal point again: if you don’t have a backup system for your agentic AI workflows, you’re in trouble. Guess what? It’s going to fail, it will come crashing down, and you won’t know what to do. So let’s figure that out.

Christopher S. Penn: If you’re building with agentic autonomous systems like Open Claw and its variants and you’re not building on an open‑weights model first, you’re taking unnecessary risks. Today’s open‑weights models like Quinn 3.5 and Minimax M2.5 are smart, capable, and about one‑tenth the cost of Western providers. If you have a box on your desk, you can run your life on it. You’d better use a model or have an abstraction layer that allows you to switch models so you can continue to run your life from this box. I would not rely on a pure API play from one major provider because if they go away, the transition will be rough.

Now is the best time to build that level of abstraction. If you’re using tools like Claude code or other coding tools, you can have them make these changes for you. You have to be able to articulate it, and you should articulate with the 5B framework by Trust Insights. Once you do that, you can be proactive about preventing disasters.

Katie Robbert: Is that unique to coding tools or does it also apply to chats and custom LLMs people have built? Obviously we have background information for Co‑CEO well documented, but let’s say we didn’t. Let’s say we built it and it lived as a skill somewhere. That’s a concern because we’ve grown to heavily rely on that custom agent. What if Claude shuts down tomorrow? We can’t access it. What do we do?

Christopher S. Penn: The Co‑CEO—those fancy words like agents and skills—they’re just prompts. You can take that skill, which is a prompt file, fire up Anything LLM, turn on Quinn 3.5, and it will read that skill and get to work. You can do that in consumer applications like Anything LLM, which is just a chat box like Claude. The only thing uniquely missing right now is an equivalent for Claude Co‑Work, but it won’t be long before other tools have that. Even today you can use a tool like Klein or Kelo inside Visual Studio Code, install those skills, and have access to them.

So even with Co‑CEO, you can drop that skill because it’s just a prompt and resume where you left off, as long as you have all data backed up and not living in someone else’s system, and you have good data governance. The tools are almost agnostic. All models are incredibly smart these days, even open‑weights models. I saw an open‑weights model over the weekend with 13 billion parameters that runs in about 12 GB of VRAM, so a mid‑range gaming laptop can run it. Co‑CEO Katie could live on perpetuity on a decent laptop.

Katie Robbert: But you have to have good data governance. You need backups and documentation, then you can move them to any other system to make it more tool‑agnostic. If you don’t have good data governance or the basic prompts you’re reusing, we’ve been talking about this since day one. What’s in your prompt library? What frameworks are you using? What knowledge blocks have you created? If you don’t have those, you need to stop, put everything down, and start creating them, because you’ll be in a world of hurt without the basics.

If you have a custom GPT you use daily, is it well documented—how it works, how it’s updated, how it’s maintained—so that if you can no longer subscribe to OpenAI, you can move to a different system.

Katie Robbert: That move, especially if you’re using client‑facing tools, is not going to be overly traumatic. It’s not going to bring everything to a screeching halt. Many companies think everything will halt, but we haven’t explored personally what Claude meant by a copy‑paste migration. It feels like an oversimplification of what you actually have to do to replicate your system in Claude.

Katie Robbert: But the fact they’re thinking about it, knowing people are panicking, is a good thing for Claude. It’s probably more complicated. The more you build, the deeper you are in the weeds, the more complicated it will be to port everything over. That’s why, as you build, you need documentation.

Katie Robbert: That’s for nerds.

Katie Robbert: I’m a nerd. I need documentation because it makes my life easier. You’re the first to ask, “where’s the documentation?” Do you have the PRD? Do you have the business requirements? I’m not touching anything until we have that. It makes me incredibly happy because look how much more you’ve accomplished with these systems and how zero panic you have about the AI wars—you can use whatever system you feel like that day.

Christopher S. Penn: Exactly. For folks listening, you can catch this on YouTube. This is my folder of all stuff—my Claude environment. It lives outside of Claude, on my hard drive, backed up to Trust Insights’ Google Cloud every Monday and Friday. It includes agents, document reviewers, the CFO, Co‑CEO, Katie, documentation, rules files for code standards, reference and research knowledge blocks, individual skills, and a separate folder of knowledge blocks. All of this lives outside any AI system—just files on disk backed up to our cloud twice a week. So no matter what, if my laptop melts down or gets hit by a meteor, I won’t lose mission‑critical data. This is basic good data governance.

No matter what happens in the industry, if all the Western tech providers shut down tomorrow, I can spin up LM Studio, turn on the quantized model, and run it on my computer with my tools and rules. Our business stays in business when the rest of the world grinds to a halt. That will be a differentiating factor for AI‑forward companies: have a backup ready, flip the switch, and we’re switched over.

Katie Robbert: If we look at it in a different context, it’s like the panic when a human decides to leave a company. You have that two‑week window to download everything they’ve ever done—wrong approach. It’s the same if you don’t have documentation for a human and no redundancy plan. If Chris wants to go on vacation, everything can’t come to a screeching halt. We’ve put controls in place so he can step away. We want that for any employee.

Many companies don’t have even that basic level of documentation. If each analyst does a unique job and no one else can do it, you have no redundancy, no backup plan. If that analyst leaves for a better job, clients get mad while you scramble. It’s the same scenario with software.

Christopher S. Penn: Now that’s a topic for another time, but one thing I’ve seen is the less you as an individual have fair knowledge, the more irreplaceable you theoretically are. That’s not true. Many protect job security by not documenting, but if everything is well documented, a less competent match could replace you. We saw Jack Dorsey’s company Block cut its workforce by 5,000, saying they’re AI‑forward. There’s a constant push‑pull: if you have SOPs and documentation, what’s to stop you from being replaced by a machine?

Katie Robbert: I say bring it. I would love that, but I’m also professionally not an insecure human. You can’t replace a human’s critical thinking. If the majority of what you do is repetitive, that’s replaceable. What you bring to the table—creativity, critical thinking, connecting the dots before AI, documentation, owning business requirements, facilitating stakeholder conversations—is not easily replaceable. If Chris comes to me and says I’ve documented everything you do, and we give it all to a machine, I would say good luck.

Christopher S. Penn: Yeah, it’s worth a shot.

Christopher S. Penn: All right. To wrap up, you absolutely should have everything valuable you do with AI living outside any one AI system. If it’s still trapped in your ChatGPT history, today is the day to copy and paste it into a non‑AI system, ideally one that’s shared and backed up. Also, today is the day to explore backup options—look for inference providers that can give you other options for mission‑critical stuff. No matter what happens to the big‑name brands, you have backup options. If you have thoughts or want to share how you’re backing up your generative and agentic AI infrastructure, join our free Slack group at Trust Insights AI Analytics for Marketers, where over 4,500 marketers—human as far as we know—ask and answer each other’s questions daily. Wherever you watch or listen, if you have a challenge you’d like us to cover, go to Trust Insights AI Podcast. You can find us wherever podcasts are served. Thanks for tuning in. We’ll talk to you on the next one.

Katie Robbert: Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data‑driven approach. Trust Insights specializes in helping businesses leverage data, AI, and machine learning to drive measurable marketing ROI. Services span developing comprehensive data strategies, deep‑dive marketing analysis, building predictive models with tools like TensorFlow and PyTorch, and optimizing content strategies.

Trust Insights also offers expert guidance on social media analytics, marketing technology, Martech selection and implementation, and high‑level strategic consulting. Encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic, Claude, DALL‑E, Midjourney, Stable Diffusion, and Meta Llama, Trust Insights provides fractional team members such as CMO or data scientist to augment existing teams. Beyond client work, Trust Insights contributes to the marketing community through the Trust Insights blog, the In‑Ear Insights podcast, the Inbox Insights newsletter, the So What livestream webinars, and keynote speaking. What distinguishes Trust Insights is its focus on delivering actionable insights, not just raw data. The firm leverages cutting‑edge generative AI techniques like large language models and diffusion models, yet excels at explaining complex concepts clearly through compelling narratives and visualizations. Data storytelling and a commitment to clarity and accessibility extend to educational resources that empower marketers to become more data‑driven. Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you’re a Fortune 500 company, a midsize business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information.

Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.