GPT 5.4 - Run Your Whole Business With AI

0:00OpenAI just dropped their newest model GPT 5.4 and honestly no one saw this coming. While everyone was out focused on the other models being released on a daily basis, they quietly released what might be the biggest breakthrough in AI reasoning we've seen this year. I've been testing this thing for the last 48 hours in OpenClaw in other build projects and I'm going to tell you the results. So, this changes everything. Not to mention the 1 million context token window for better memory. There are some other details that you might be missing out on. So, I'm going to break it down for you because there is a lot to unpack here. Honestly, most people are going to miss how significant this actually is and how it kind of impacts the future. So, first off, GPT 5.4 isn't

0:45just another incremental update. This is completely different architecture under the hood. OpenAI has been working on what they call structured reasoning. Basically teaching the model to think step by step in a way that is more humanlike than anything we've seen before. Here is what's wild about it. The model doesn't spit out answers anymore, like a person eating crayons in the corner. It actually shows you its thinking process. So when you ask it to solve a complex problem, you can literally watch it break down the problem, consider different approaches, and then work through the solution methodically. It's like having a conversation with someone who's actually thinking through a problem with you rather than just regurgitating a lot of

1:31blah data. I tested this yesterday with some coding problems inside of OpenClaw, getting some more automations built out. And I've been working really every day on my mission control dashboard and trying to really focus on stepbystep automations that help me kind of buy back my time. So, I was eager to try this model out. I have been using Opus 4.6 for everything as well as Sonnet 4.6. And for me, nothing really compares. But I do say that this is one of the better models that can be in comparison to something like a sonnet 4.6, especially since Opus does consume a lot of usage. So this AI system runs the entire YouTube channel, emails, schedule, cutting up long form into

2:18shorts, cold outreach for people to get on the podcast, solving a lot of problems in my workflow. So, I threw everything at this model and instead of just giving me code and snippets like previous models, it walked me through why certain approaches would work better than others. It explained the trade-offs between different database queries, talked about caching strategies, and even suggested architectural improvements I hadn't considered. So, right now, I use a lot of the frameworks that I use to build my apps for building out my mission control dashboard. you know, putting everything like on an actual database, running it all through Nex.js, you know, things like that. And it's been interesting because I'm so used to Opus being, you know, the more

3:04personable, you know, friend, like you're talking to like a best friend, right? So, chat GPT always felt different. It always felt off. But with this newest release, it's a bit different. It just can think through the problems a lot better. Similar to kind of like how Opus works. So, this is where it gets pretty interesting is the reasoning capabilities extended way beyond just like technical problems that I'd come into. I asked it to help me think through some of like the business strategies for my resell business and as well as how to like market my SAS products like Easy Flip, the Magic Hand, and Snaptastic, different types of content strategies. And it didn't just give me like generic advice like normally it would. It actually reasoned through my specific situation, considered my constraints, and gave me a

3:50step-by-step plan that was generally insightful. And this is a term you're going to hear more and more, but reverse prompting, you know, telling the LLM like, "Here is all my information. Now, build out the automations on what you would think that would serve my business best rather than us trying to come up with all the specific things that we want to do." because these LLMs have gotten so good at analyzing context and knowing what the new releases are and how to optimize for performance much better than we are and those models are only getting better. So this models seem to have this ability to hold like multiple concepts in working memory simultaneously and reason about the relationships between them. So when you're dealing with like complex

4:35multiaceted problems, which to be honest, most real world problems, GPT 5.4 can actually keep track of all the moving pieces and think through how they interact. So especially from like a proumer consumer point of view, a lot of newer people getting into AI, this can really help people know how to work with AI much more clearly and not get lost in the sauce. So, let me explain some of the examples on where it gets really mind-blowing. I asked chat GBT54 to help me optimize my agent workflow in OpenClaw. You know, the system I have atlas as the brain, which is Opus 4.6, and then I have other agents basically named after the Wuang clan. We have

5:20Inspector Deck, the Jiza, uh the Rizza. Um, and all these agents do different things, right? One writes my newsletter, one sends uh scrapes Twitter. Um the Rizza's in control of all the content and these cron jobs fire off at 6 a.m. in the morning. So previous models would either give me generic advice or get confused by the complexity of having multiple agents coordinating with each other. So for example, like this morning the cron job fired off but it didn't complete you know the clips that we were supposed to generate. So things do break and I think that's why people get frustrated with open claw. But 5.4 actually did this from start to finish on the first try. So it mapped out the entire workflow. It identified the bottlenecks that I had no clue that even

6:06existed. And then it suggested specific optimizations for each agent. It reasoned through the data flow. It considered the timing dependencies and even caught a potential race condition that could have caused problems down the line. What's crazy is how it explained its reasoning. It said something like,"I noticed that the Rizza agent is trying to access the database at the same time as Atlas that might be writing trend data which could cause conflicts." So, here is why this happens here and three ways to solve it. Then, it walked through each solution. It explained the pros and cons and it recommended the best approach for my specific use case. So you can tailor these agents for your specific use case. So that's how I have it running in my business. I'm tired of

6:52manually inputting things into spreadsheets. I'm tired of doing constant research in the AI space. So I'm able to optimize these agents that are on scheduled cron jobs to do a lot of the work for me. And the thing is though, they break. They break. Things break. It's a daily thing. So, and that's why there is a lot of frustration about getting Open Cloth set up is because it's not all the way there yet. I do believe it's like a glimpse into the future, but 5.4 is kind of like a step in the right direction. It also uses a lot less tokens than something like Opus 4.6. The kind of multi-step reasoning with the context awareness, that's not something I've seen from previous models, especially not from OpenAI. But it's not just the technical stuff. I also tested it on some of the

7:38content strategy problems for the YouTube channel. I asked it to help me think through how to balance educational content with more entertainment value given my specific audience and goals instead of the generic know your audience type of stuff. It actually reasoned through my situation. It looked a lot of the transcripts. We have it connected through the YouTube API and this is with other YouTube channels as well for the resell content. It just kind of formulated a better strategy for me that I couldn't have really come up with without doing hours and hours of research. It looked at the content so far and so in-depth. It identified the patterns that actually worked, thumbnails that had the higher click-through rates, the the tweets that I tweeted that had better, you know, hooks and taglines, and it started to

8:26actually like get to know me better and build my content strategy based on like my voice, right? So it considered like all my business goals, the 100K subscribers, which is the target for this channel within a year, five 50k MR target, and then it gave me like strategic frameworks that were actually actionable. Whenever you have like a big goal, you want to break it down into tiny small steps and reverse engineer it like how many videos need to be produced a day, how many outreaches need to be done a day to kind of build up to the bigger goal. So, it thought through all of that seasonal trends, uh, built out charts, graphs on what exactly I need to do. It gave me a competitor analysis. It even suggested specific video formats that work for different styles and

9:11different audiences. The reasoning was pretty sophisticated enough and it felt like it was brainstorming with a really smart business partner, which is why I like Claude and Claude projects is because I can kind of segment my different businesses and have like a business advisor for different businesses that I'm running, which I love. So, I'm going to talk about like the performance because this is where it gets a little shaky. Sometimes the model can be really good but be really slow which we're kind of used to these models be getting faster and faster. That's where I think after models release there's kind of like a diminishing return. So even though this was just released a few days ago, it could get worse and I think that's what we see time and time again. And I'm sure you've

9:56witnessed it not just with anthropic but open AI. So this is where the interesting thing happens is 5.4 before is noticeably faster, but what what was already impressive was the reasoning that happens in real time. They'll be able to step by step break down problems and work through the problems with you. You know, I don't mind that being a fast response just as long as I get the end result and that it's accurate. So, going through the content creation pipeline and having it look at all the agents and how it schedules the cron jobs and things like that. Um, it was like I'm was talking to Opus 46 but just much cheaper, right? So, this happens in real time. So, you're not waiting 30 seconds while you work through problems. It's fast enough that you can actually have a

10:41back and forth conversation where it's reasoning through complex problems is amazing, which is really nice. That's what I was hesitant going back and forth. So I tested it some I tested it against some of the other coding agents in the challenges that I had in the past with like even Quinn Miniax Kimmy K2 which I used through open router to run some of the smaller uh jobs that we have in the dashboard. So on algorithmic problems it consistently outperformed GPT4 um and it was competitive with 01 preview but way faster. on creative problem solving tasks. It was in a league of its own. But here's what was the most impressive part out of it all. And when I tested it yesterday, I was testing it for up to three four hours.

11:27And I have switched back to Opus. But it's something that you should at least try out in your workflow, whether you're using it in Open Claw or in some other IDE. So I gave it like a business case study in hypothetical SAS company with specific metrics and challenges and I asked it to develop a growth strategy. Previous models would give you like a generic framework, copy paste advice from the internet and blogs that are outdated. GBT 5.4 actually reasoned through specific situation and it considered the constraints and opportunities and developed a custom strategy that made sense for the exact scenario. I'm thinking of ideas for like my resell niche. You know, I have a channel of almost 20,000 subscribers, email list of 4,000 people, school

12:13community of almost 2,000 people, trying to figure out how can I capture that audience with products that I can build with AI that can be helpful and bring value. So, that that's kind of like the spin-off of what I have going on with OpenClaw um as like little test projects. So the quality of the reasoning was good enough that I felt comfortable using its output as a starting point for real business decisions. That's a big deal to me because most AI generated business advice is pretty surface level level, pretty generic, pretty like, yeah, I've heard that before. From like a technical perspective, if you're building AI applications like I am, 5.4 opens up some really interesting possibilities. The structured reasoning means that you can build more sophisticated agent workflows where the AI actually thinks

12:59through the complex decisions rather than just pattern matching. For example, in my mission control system, I could potentially replace some of the hard-coded logic with GPT54 and the reasoning will kick in. Instead of having rigid rules about when to post the content or how to optimize the thumbnails, I can have AI reason through each decision based on current context, performance data, and strategic goals. This could make my automation so much more adaptive, intelligent, plugandplay. Rather than just following the scripts, my agent can actually think through the decisions, adapt to the challenging circumstances. The other big opportunity in the customerf facing applications is your if you're building an AI product for businesses, the reasoning

13:45capabilities can enable much more sophisticated problem solving tools. Instead of just proving instead of just providing information, your AI could actually help users think through the complex decisions. I'm already experimenting with this on one of the SAS products, Magic Hand. So instead of just giving users data, I'm testing whether GPT54 can actually help reason through what the data means, what actions should I take, and the early results are promising. So for example, like customer outreach, I know that real estate agents are on Instagram, but real estate agents are not available on email because they get thousands of emails a week. So, I'm trying to figure out how can I do cold outreach for some of my

14:30SAS products, specifically the real estate magic hand. It's like a photo edit, photo AI editor where you just click a button and it edits the photos for you. So, that's kind of what I'm working through as a side project in addition to building up the mission control. So, I'm going to talk about the costs and access because this is where it gets a little more practical for a lot of people getting into AI. GBD54 is available through the OpenAI API right now and surprisingly the pricing is pretty competitive with 4 Turbo. Given the performance improvements, that's actually a pretty good deal. For context, I've been running tests for two days, making hundreds of API calls. The costs are definitely manageable for production use, but this is with using it with OpenAI and Claude. So, I'm just

15:17noticing that with Claude, I'm going through the OOTH token. I'm running out of usage on the $200 a month plan within just a couple hours, though I am using it pretty heavily. So, if you're running the models through OpenClaw, I think it's important to know that if you're using Claude, make sure you use have it trigger to switch over to maybe sonnet 446 or maybe a model through open router if you're hitting those limits. I have a 50% warning to go off in my dashboard to be able to let me know, hey, you're going to run out of context. Your your token limit will be reached. Switch over to these models so you can get better um results by not going through all of your tokens. So, um I've already hit my limit

16:02for the day already and it's only 12 o'clock. So um running local models through open claw I do believe is the future and that's where it gets competitive with open AAI and anthropic as these open- source models get better. That's uh a video for another day but I do think we are headed in that direction. So the rate limits are reasonable as well if you're using OpenAI through the API. I haven't hit any throtting throttling issues in the 3 hours of testing even when making the rapid fire requests and that was on launch day too. So, OpenAI seems to have learned from the 01 launch where rate limits were pretty restrictive initially. One thing to note is that there are some additional parameters you can tune for reasoning depth. You can basically tell the model how much time

16:47to spend thinking through problems. For simple queries, you can use fast mode for complex problems. You can enable deep reasoning mode, which does take a bit longer, but gives much more of a thorough analysis. Think of like Ultra Think within Claude. If you're a heavy claw user, you can have think, think think harder, ultrathink, which did disappear momentarily for a couple months, but they did just bring that back recently. And that's just the thinking capability on working through the codebase or problems that you're trying to get solved. So, I've been experimenting with this in my agent workflows. Like I said, I am back to using my original agent workflow with Claude and Opus 46 as the front runner for the brain. Anyways, but for routine

17:33tasks like generating social media posts, I use fast mode to keep things snappy and it generated pretty good results. But for strategic decisions like content planning or business analysis, you would enable deep reasoning mode and the quality improvement would be noticeable. So what does this mean for you as a builder? I think that we're at an inflection point here. The reasoning capabilities in 54 are good enough that you can actually start building applications that were just not feasible before. Think about it this way. Previous AI models were great at pattern recognition and information retrieval, but they struggled with complex reasoning and decision-making. 54 changes that equation. Now you can actually build applications that actually think through problems and actually iterate on the process and

18:19execute predefined workflows. So this opens up opportunities in areas like business intelligence, strategic planning, complex technical troubleshooting, and really any domain where you need sophisticated problem solving rather than just information lookup. If you're already building a applications, I'd strongly recommend getting your hands dirty. Go through the API, get 54 going for your most complex uses. If you're just getting started with AI development, this might actually be a great time to jump in because the reasoning capabilities make it easier to build sophisticated applications without needing too much machine learning expertise. The key is to think beyond simple question and answer applications. With 54, you can build tools that

19:04actually help users think through complex decisions, analyze multiaceted problems, and develop sophisticated strategies. For my own projects, I'm already planning to integrate 54 later down the road with several other workflows because I know I'm going to be hitting these limits really quickly using Opus Sonnet even. So, use it where I can within reason and maybe it analyzes trends for me. Maybe it helps with audience data. But like in my SAS products, I'm exploring using it to help customers, to help reduce churn, to make more strategic decisions on how to handle customer queries. So the reasoning capabilities also make it much easier to like build reliable agent workflows instead of having to

19:49anticipate every possible scenario and write explicit rules. You can rely on the model to reason through the edge cases and unexpected situations. So, if you want to start building with 54 yourself, here's what I'd recommend. First, get the API access. Sign up for a developer account. It's free and see how it works. I actually do think it's actually not free. It's $20 a month. You have to have the GPT $20 a month plan. Go sign up for the developer account, get an API key, plug it in, and just see how it works for what you're trying to build. The key is learning how to ask questions that encourage the model to show its work instead of just asking for answers. Like I said, that reverse prompting, you know, like give it all the information and then ask the LLM what it thinks about how to go about

20:35solving these problems, grow your business, optimize your life. The quality of output is much better when you explicitly request step-by-step thinking. Second, think about the existing workflows you already go through and identify places where better reasoning could add value. Like maybe it's customer support where the AI could think through the complex issues rather than just matching keywords. Maybe it's data analysis where the AI can actually interpret the trends and suggest actions rather than just generating reports. Third, start small and think big. Build simple prototypes to test reasoning capabilities, but keep in mind the larger possibilities. This technology is good enough now that you can actually build applications that were science fiction just a few months ago. Which is why going back to Opus is so magical

21:21because it's as close as one-shotting stuff as I've seen. It's it's really something special. But this space changes so fast that you got to test the new models that come out because you really don't know what they're capable of until you use them. You could read hundreds of bunch uh benchmarks that come out, but they really don't mean anything until you actually use it for your personal workflows and your personal projects. So, if you want to learn how to build stuff like this yourself, this is exactly what I'm trying to build inside of Shipping School. I help you get from idea to a shipped product. We cover everything from setting up your development environment to integrating cutting edge a AI models like 54. We do three live calls a week where we actually build together, review your project, help it get it shipped, help you get customers.

22:08It's not just a theory. It's hands-on development. I learn by doing and I think a lot of other people do getting into this space learn the same way as well. You kind of just got to start dirty and you got to be around people that are like-minded building and shipping together. Also included is a starter kit that helps you get database, payments, everything all connected, au uh authentication and getting that all ready for your app to get built. So all that's included in there as well as a setup course to get you going. I think 54 represents like a fundamental shift in what's possible with AI applications. the reasoning capabilities are sophisticated enough that can we can start building tools and shipping them quicker, faster, and more detailed. So

22:53the question is like whether this technology will be transformative, it's whether you'll be building with it or watching from the sidelines. The opportunities are right there for builders who are willing to experiment and ship. You got to get your hands dirty. You got to start before you're ready. A lot of people look from the outside in like I was just the beginning of last year watching everyone build and I think that's the best way to learn is to just use the models. Build a walking app, you know, build your, you know, calendar reminder app. Build something that you actually use in your day-to-day process. So like that's the 54 breakdown from my few hours of experience. I think it's really good. What concerns me is that OpenAI almost feels like it's trying to play catch-up with all these other models releasing. And I don't know

23:39if that's a good thing or a bad thing, but I do think that 54 is a pretty good step in the right direction. It is a pretty big deal. Most people don't realize it. So, if you're building anything with AI, you should be experimenting with it. The reasoning capabilities alone make it much more better than the previous installment of GPT. So, if you want to start shipping AI applications that leverage this kind of technology, you can join shipping school down below. I'll put a link for you. And if you guys like daily updates just like this, make sure you subscribe for more content. Be blessed.