GPT-5 may feel like a minor upgrade. Yet it carries the tools for a major pivot. This isn’t for power users. It’s built to unlock value from millions who use ChatGPT for free. GPT-5 sets the stage for ads, affiliate models, and eventually a full consumer superapp. If you want to understand how AI is changing business strategy, my post on Advertising in LLMs.

OpenAI didn’t drop a flashy feature. They rolled out a smart router that decides which model to use for each query. Easy questions go to efficient mini-models. Complex tasks trigger deeper reasoning. That switch lowers costs and boosts performance. Most free users – a group nearly none had exposed to chain-of-thought reasoning – now access better, smarter results without knowing how. That’s a massive upgrade behind the scenes.

That router also creates a control point. It can learn from user choices, accuracy signals, and whether people stick around or switch models. It lays the technical ground for monetized responses. A query tagged as “purchase intent” could surface recommendations tied to affiliate links. OpenAI gains value when it guides you – and you buy.

The real story is far larger. ChatGPT isn’t niche. In late 2023 it wasn’t in the top 100 most visited sites. Today it hovers near the top five, behind only established giants. It outpaces Reddit, Twitter, WhatsApp, and Wikipedia. That scale, combined with zero monetization today, creates enormous opportunity.

This isn’t about selling display ads inside chat. It’s about turning ChatGPT into an intelligent buying assistant. It interprets what you ask, routes to the right reasoning engine, then offers suggestions you trust. Licensing, affiliate revenue, and transactional fees become viable. You get help. OpenAI earns in a way that aligns with your intent.

Behind the scenes leadership decisions reinforce this. Fidji Simo, now head of applications at OpenAI, built ad engines at Facebook. Sam Altman’s tone on ads has shifted from distrust to open exploration. The pieces now align for monetization—soft, smart, subtle, and built into the flow.

This method sidesteps the platform wars. GPT-5 doesn’t rely on search ads or banners. It earns by helping you decide. It disrupts traditional web economics. As users shift intent queries to AI agents, platforms like Google and Meta lose influence. ChatGPT becomes the new first stop. This router is not a technicality. It’s a gateway to a new consumer model.

Marketers and brands must pay attention. The questions you trigger through GPT-5 may soon drive transactions. You’ll need plans for affiliate alignment, conversational product placement, or branded agent integration. Begin now by positioning your offerings where AI agents might surface them.

GPT-5 is not just a model. It is the quiet transformation of ChatGPT into a consumer superapp—and into a new ad frontier.

ChatGPT-5 is here. It isn’t just another upgrade. It’s a shift in how brands can operate, scale, and interact. For marketers, it represents a turning point. Campaigns, customer experiences, and creative production will never be the same.

A Unified Model for Every Task

Previous versions required you to choose between faster, lighter models and slower, more advanced ones. GPT-5 removes that choice. It automatically routes between fast responses and deep reasoning based on your prompt. That means you can move from writing a punchy ad headline to designing a complex, multi-touch campaign without switching tools.

For marketers, this removes friction. You no longer waste time adjusting settings. The model adapts to you. Small teams gain the capacity of full creative departments. Large teams accelerate workflows and remove hand-offs between specialists.

Multimodal as the Default

GPT-5 processes and generates text, images, audio, and video natively. This changes the creative process. You can brief it on a new product launch, and in one request, receive a campaign plan, video scripts, social carousels, and image concepts. All assets can align with brand guidelines because the model retains them in its extended memory.

This also makes rapid testing easier. You can produce ten variations of a creative asset across formats in minutes. Then feed performance data back into the model to refine the next iteration. It’s fast, measurable, and adaptive.

Massive Context Windows

GPT-5’s context capacity jumps to around 400,000 tokens. For marketers, this is a breakthrough. You can load an entire year’s worth of campaign data, brand tone guidelines, audience personas, competitive analysis, and customer feedback – all at once. The model can then respond with output that is consistent, relevant, and informed by your entire knowledge base.

This isn’t just convenience. It changes how strategy is built. You no longer rely on fragmented tools and documents. You have a single, intelligent system that remembers everything you’ve shared and uses it to make better recommendations.

Voice and Personality Control

Brand voice has always been hard to scale. GPT-5 changes that by allowing predefined interaction styles. You can set your brand to speak as a friendly expert, a confident advisor, or a data-driven strategist, and every output will match. This consistency will extend across all customer touchpoints, from ad copy to chatbot responses.

Marketers can even build multiple personalities for different audience segments. A brand selling to both teenagers and corporate decision-makers can maintain two distinct voices—managed by the same model.

Accuracy and Trust

Hallucinations and vague claims undermine marketing credibility. GPT-5’s reduced error rate matters here. The model is better at citing sources, referencing facts, and staying within compliance guidelines. This allows marketers in regulated industries—finance, healthcare, pharmaceuticals—to use AI safely without risking misinformation penalties.

Customer service bots, FAQ systems, and sales assistants powered by GPT-5 can now respond with reliable information while still sounding human. This is key for trust and conversion.

Automation Without Losing Creativity

GPT-5’s coding capabilities mean marketers can deploy automated tools without heavy developer involvement. You can ask it to create a landing page template, build a data dashboard, or integrate campaign analytics into your CRM—on demand. This closes the gap between creative vision and technical execution.

By linking GPT-5 with existing ad platforms, you could build an end-to-end system: brief the campaign, generate creative, push ads live, and receive performance feedback without leaving the AI interface.

Implications for Competitive Advantage

The gap between brands that adopt GPT-5 early and those that don’t will be wide. Early adopters will move faster, test more, and deliver personalized experiences at scale. They will reduce costs while increasing creative output. Competitors who remain tied to traditional workflows will struggle to match speed and variety.

Marketers should treat GPT-5 adoption as both a creative and operational decision. The gains are not just in idea generation, but in how campaigns are executed, measured, and optimized.

How to Start Using GPT-5 in Marketing Today

Begin by training the model on your brand identity. Feed it style guides, customer personas, product information, and historical campaigns. Then test it on small projects—email subject lines, social captions, product descriptions—before expanding into larger campaigns.

Next, integrate GPT-5 into your content review process. Use it to evaluate tone, alignment, and compliance. Have it generate alternative angles for underperforming creatives. Let it suggest budget reallocations based on predicted performance.

Finally, explore custom GPTs. These are tailored instances of the model fine-tuned for your business. They can act as brand strategists, media planners, or creative directors—available 24/7, with perfect recall of everything your brand stands for.

The Future with GPT-5

We are moving into a phase where AI is not a tool, but a collaborator. With GPT-5, marketers can operate as creative directors guiding a highly capable partner. The brands that master this relationship will set the standard for speed, quality, and customer relevance in the next decade.

This is not about replacing teams. It’s about amplifying them. The marketers who learn to think in prompts, who can direct GPT-5 as clearly as they brief a top agency, will control the future of brand communication.

The internet is changing faster than most brands realize. The next shift won’t be visual. It will be conversational. In a few years, websites may no longer matter. You won’t search for a URL, browse menus, or scroll pages. You’ll ask. And you’ll get an answer from a language model trained on everything that brand has ever said, done, or offered.

This isn’t theoretical. It’s already underway. Google is redesigning search to surface AI-generated answers. OpenAI is building tools where GPT handles everything from research to booking. Perplexity, Rabbit, and others are turning traditional search and browsing into a simple question-and-answer exchange. As these systems become more accurate and real-time, the logic of the web breaks down.

Today, a website is a digital storefront. It’s where brands control their story, their products, their voice. But language models don’t need to show you a storefront. They don’t need you to click. They ingest the content and deliver exactly what the user needs – instantly and in context. That includes product details, customer service, pricing, reviews, and recommendations. No homepage needed.

What Replaces the Website?

The replacement is invisible. Instead of building pages, brands will build structured data pipelines that feed into LLMs. Your brand will be represented not by a homepage but by how well the model understands you. The brand lives inside the model. That means the goal shifts from building web traffic to building language-level trust and relevance.

For example, when a customer asks, “What’s the best laptop for photo editing under $2,000?”, the model won’t direct them to ten different websites. It will pull from specs, reviews, brand guidelines, and prior user interactions. The winning brand is the one that trained the model well. The one that structured its data. The one that created a voice the model can represent clearly and accurately. This is the new SEO.

Websites today are optimized for clicks and retention. Tomorrow’s brand systems will be optimized for language understanding and contextual delivery. The best performing “pages” will be invisible nodes inside the LLM, not URLs you visit. Instead of pageviews, success will be measured in how often your brand appears in model outputs—and whether users take action based on those outputs.

How Branded Experiences Will Survive

Brands won’t disappear. But they’ll stop looking like websites. They’ll feel more like personalities that live inside the AI layer. Each time someone asks for a product, service, or piece of advice, the brand will respond through the model, not through a web page.

These experiences will still feel branded. But instead of colors and fonts, branding will happen through language, tone, response structure, and behavioral alignment. If your brand stands for simplicity, the model will respond simply. If it stands for expertise, it will answer with precision and authority. You won’t need a landing page to express your values. The model will do it for you – if you’ve trained it to.

This also opens the door for new types of interaction. Brands could offer micro-agents inside platforms like GPT or Perplexity. A skincare brand could have a built-in AI that answers skin-related questions in its voice. A travel company could live inside the AI, guiding trips, rebooking flights, and offering destination advice, all without linking out. These micro-agents become the new brand ambassadors—smarter, faster, and always on.

What Brands Need To Do Now

The shift won’t be immediate, but it will be fast. Most brands are not ready. They still focus on web traffic, UI design, and optimizing funnel flows. But in a language model world, those tactics become less relevant. Instead, brands should start by turning their content into structured, model-readable formats.

This means publishing data in machine-friendly ways. Clear product specs. Detailed FAQs. Transparent pricing. Use cases written in natural language. It also means investing in voice consistency. If your brand speaks five different ways across your platforms, the model won’t know which to trust. Uniformity is key.

Brands should also begin testing how they appear in AI results. Ask the top models what they know about you. Ask what they recommend in your category. If your brand doesn’t appear—or appears inaccurately—that’s a red flag. This is your future discoverability engine. Visibility here is more important than on Google.

Finally, begin building your own AI agents. If you sell a product, service, or experience, you should own the interface that represents it in conversation. Not through a chatbot on your site—but as a plug-in or embedded skill in the broader AI ecosystem. Think of it as your AI sales rep.

What We Lose and What We Gain

The old web gave brands control. You chose how your homepage looked, how long your message appeared, what journey the user took. In the new model, you surrender some of that. The user is no longer navigating your site. They’re navigating the AI’s understanding of you.

This can feel like a loss. But it’s also a gain. You’re no longer dependent on device type, browser speed, or scroll depth. You reach users wherever they are, however they ask, in exactly the moment they need you. If done right, the conversion rate could be far higher than any landing page ever achieved.

Websites won’t vanish overnight. But their role will shrink. The future of brand interaction won’t be built with clicks. It will be built with words. The winners will be those who speak clearly, structure their knowledge, and embed their brand in the foundation models that define the next era of the internet.

AI is no longer confined to screens. It’s entering physical space. Smart devices, humanoids, voice assistants, and wearables will soon engage with people in their homes, cars, and workplaces. This shift introduces a new medium: embodied AI.

That means advertising must evolve. The future isn’t about placing banners on websites. It’s about presence. It’s about the AI assistant that sees your kitchen and suggests a better blender. Or the humanoid robot that walks beside you and casually mentions the newest running shoes based on your gait.

This transition is happening faster than most people think. And it’s going to reshape everything about how brands get attention, build trust, and drive sales.

From Passive Screens to Active Interfaces

Most digital ads today rely on screen-based attention. Search ads, display banners, TikTok pre-rolls, sponsored posts. These all compete for one thing: screen time. But what happens when the interface is no longer a screen?

Hardware AI changes that. These are physical devices powered by real-time machine learning. They sense you. They learn from your behavior. And they can talk back.

Examples already exist:

• Humanoid robots from companies like Figure and Tesla that can walk, observe, and converse
• Wearables like the Humane AI Pin and Meta smart glasses that provide prompts, reminders, and recommendations
• In-home assistants like Amazon Echo, Apple HomePod, and Google Nest that can process queries and suggest products

Advertising will no longer be a one-way message. It will become a two-way dialogue. Or even better, a three-dimensional brand experience shaped by where you are, what you’re doing, and how you’re feeling.

Advertising Through Humanoids: The Next Interface

Humanoid robots may seem far off, but the investment is already in motion. Tesla’s Optimus, Figure’s humanoid assistant, and Chinese prototypes are all progressing. Within five years, we may see the first general-purpose, AI-powered humanoids working in retail, hospitality, and eventually homes.

Imagine a brand deploying 10,000 humanoid agents in select stores. Each one is programmed to answer questions, offer product suggestions, and build brand loyalty through one-on-one interaction. This is not a chatbot in a browser. This is a face-to-face conversation with a machine that knows your preferences, remembers past purchases, and recommends with precision.

For example:

• A humanoid in a hotel suggests a specific facial serum based on your skin tone and humidity level in the city
• A humanoid greets you at a retail store and guides you to shoes that match your foot pressure and posture data
• A home assistant notices you’re cooking more vegetarian meals and asks if you want to try a plant-based protein brand

These scenarios don’t require speculation. They only require alignment between three systems: high-quality sensors, reliable AI models, and brand integrations. All three exist today.

Ambient Advertising in Smart Environments

The next evolution is advertising that doesn’t feel like advertising. Hardware AI will integrate into everyday objects: smart mirrors, thermostats, kitchen devices, cars. These devices can observe, respond, and suggest—without ever flashing a traditional ad.

For example:

• A smart mirror detects changes in your skin and suggests trying a product with better hydration ingredients
• A smart fridge sees you’re out of oat milk and recommends a new brand with higher protein content
• A smart thermostat notices that you like a colder room at night and offers a mattress designed for temperature regulation

This is “contextual advertising” redefined. The ad doesn’t interrupt you. It serves you.

Voice and Emotion Will Be the New Keywords

Search used to start with text. Then came voice. With hardware AI, the next frontier is emotion. Devices will interpret your tone, your posture, your breathing, and make decisions based on how you feel—not just what you say.

If you sound frustrated, your assistant might offer relaxation apps, aromatherapy diffusers, or even suggest postponing meetings. If you sound upbeat, it might recommend social events, concert tickets, or limited-edition drops. All of this is commercial real estate.

Brands will compete not on volume, but on relevance. And not in screens, but in moments.

How Brands Should Prepare

This shift won’t be optional. As more consumers adopt AI-powered hardware, brands that don’t adapt will disappear from the conversation—literally.

Here’s what to do now:

• Build multimodal assets. Your brand needs to live in voice, gesture, visual recognition, and text.
• Map out real-time use cases. How can your product or service be helpful when the user is cooking, walking, relaxing, or commuting? Find those touchpoints.
• Partner early with AI platforms. Apple, Amazon, Google, OpenAI, and others are building ecosystems. Get access now or risk being excluded later.
• Design branded assistant experiences. Create custom flows, greetings, answers, and personalities for when your brand appears in a conversation.
• Invest in conversational UX. Voice-first branding requires language that feels natural, useful, and human—not salesy or repetitive.

Risks to Consider

• Overreach and fatigue. Ads that appear too often or too personally can feel invasive. Respect user context and consent.
• Platform dependency. If you rely too heavily on one hardware provider, a change in policy can wipe out your access.
• Ethical backlash. Consumers will push back against ads that feel manipulative. Be transparent. Let users opt out. Build trust first.

Long-Term Vision

In ten years, advertising will no longer be an industry built around interruption. It will be a system of distributed agents—AI-powered, voice-enabled, emotionally intelligent machines that guide people toward choices.

Some will live in your home. Others will live in public spaces. Many will be humanoid. Most will be invisible. The best brands won’t shout. They’ll serve.

To stay relevant, brands must think in conversations, in context, and in real time. The future of advertising won’t be bought. It will be earned—one intelligent moment at a time.

Language models are moving from novelty to necessity. Tools like ChatGPT, Claude, and Perplexity are becoming daily habits for millions. As usage grows, so does the pressure to monetize. Ads will be the next frontier.

This is not theoretical. You are already seeing the early signals. If your business depends on digital visibility, attention, or user acquisition, now is the time to get ready.

LLMs are going to become major ad channels. Here’s what to expect and how to prepare.

Why LLMs Will Shift Toward Advertising

There are four reasons why LLMs will adopt ads quickly.

1. Free tools need revenue

LLMs are expensive to run. The cost of inference, training, storage, and bandwidth is significant. Subscription plans help, but the majority of users still use free tiers. That makes advertising necessary.

2. LLMs control attention

When users type a prompt, they don’t get ten blue links. They get a direct answer. That answer sits alone, with no competing results. The owner of the LLM controls every word. That level of attention control is rare and valuable.

3. Platform history repeats

Google monetized search. Facebook monetized the feed. YouTube monetized recommendations. LLMs will monetize outputs. The path is well understood by the companies building these tools.

4. It’s already happening

Perplexity has launched promoted answers. You.com has integrated sponsored links. OpenAI has data partnerships with Reddit, Shutterstock, and others. These are early signals of commercial strategy.

How LLM Ads Will Look

Ads in LLMs won’t look like banner ads or popups. They’ll be embedded in the output. These are the formats you should expect:

• Sponsored answers: Brands will pay to appear in relevant answers. If a user asks for the best nootropics, a sponsored brand will get mentioned first.
• Product bias: Answers will favor specific services or tools that have paid for promotion.
• Affiliate links: Models will include trackable links. If the user buys, the model provider earns commission.
• Branded modules: Entire GPTs, plugins, or apps will operate as branded environments within the larger LLM ecosystem.
• In-chat prompts: Models will suggest promoted tools or services during user interaction, not just at the end of a response.

Who Wins

If you’re building a brand or running performance marketing, these changes present opportunity. You should prepare to:

• Research which prompts trigger visibility in your category
• Analyze how your brand appears in LLM answers
• Identify gaps where competitors are already present
• Design content that’s optimized for AI summarization
• Explore early partnerships with providers like Perplexity

Brands that adapt early will earn better placement and lower cost of entry.

Who Loses

SEO-heavy content sites will face a drop in traffic. When LLMs answer user questions directly, users don’t click through. The need to visit a website disappears.

Aggregator businesses that rely on being part of a “top 10” list will lose distribution. That visibility will now be bought or embedded at the model level.

Anyone depending on organic search will feel pressure to pivot.

What You Should Do Now

This shift is not five years away. It’s already underway. Here’s a short checklist to help you move quickly:

• Run paid tests with LLM platforms offering promoted answers
• Study how your brand shows up in tools like ChatGPT and Claude
• Create clear, concise product messaging that fits AI outputs
• Prepare your team for an LLM-specific media buying strategy
• Track attribution from LLMs using unique URLs or landing pages

Also, review your analytics setup. Traditional UTM tracking might not capture users who never leave the chat window. Build new workflows for LLM-based conversions.

The Next Ad Channel

LLMs are the next big attention layer. They will not stay neutral. They will not stay free. And they will not remain organic-first.

You will either pay for visibility, optimize for it, or get buried. The time to act is now.

As AI continues to evolve at lightning speed, we’re entering an era where choosing the right large language model (LLM) is more strategic than ever. Whether you’re building an AI product, running experiments, or integrating tools into your workflow, it helps to understand how ChatGPT compares to its main competitors: Claude, Perplexity, Grok, and LLaMA.

1. ChatGPT (OpenAI)

Strengths: Versatile, user-friendly, powerful reasoning
Model: GPT-4-turbo (latest), hosted in OpenAI’s ChatGPT interface
Best for: Broad knowledge work, summarization, writing, coding, and ideation

ChatGPT stands out for its intuitive interface, massive user base, and fine-tuned performance. GPT-4 is known for nuanced understanding and creativity, and the turbo version delivers faster, cheaper inference with long memory capabilities for Pro users.

2. Claude (Anthropic)

Strengths: Long context window (up to 200K tokens), aligned and safe
Model: Claude 3 (Opus, Sonnet, Haiku)
Best for: Deep analysis, large document processing, safe & corporate use cases

Claude is designed with safety and alignment in mind. It shines in use cases like analyzing 100+ page documents, enterprise-grade workflows, and sensitive applications. Many users describe Claude’s tone as more “helpful and cautious” than ChatGPT.

3. Perplexity

Strengths: Real-time search + citations, LLM routing
Model: Mix of GPT-4, Claude, Mistral, and more
Best for: Factual search, research, and data-backed writing

Perplexity is not just a chatbot—it’s an AI-powered research engine. It blends real-time search with LLM reasoning and always provides sources. Its strength lies in pulling from live data and showing citations clearly, making it ideal for research tasks and decision support.

4. Grok (xAI by Elon Musk)

Strengths: X (formerly Twitter) integration, edgier tone
Model: Grok 1.5 (latest at time of writing)
Best for: X users, real-time trends, snarky chatbot personality

Grok is tightly embedded within Elon Musk’s X platform, offering commentary on trending topics, tweets, and news in a less formal, sometimes provocative voice. It’s not built for enterprise or serious research yet, but it’s part of a vision to create a social AI assistant for real-time culture.

5. LLaMA (Meta)

Strengths: Open-source, adaptable, decentralized
Model: LLaMA 3 (Meta AI), available in various sizes
Best for: Developers, researchers, on-device and private deployments

LLaMA is Meta’s open-source LLM family, designed for flexibility and community innovation. It powers many other apps and agents behind the scenes, and is available to developers for self-hosting, experimentation, and training custom models.

Quick Comparison Table

Final Thoughts

Each of these models brings a unique strength to the table. For broad usability and performance, ChatGPT still leads the pack. Claude excels at safety and deep analysis, Perplexity dominates factual research, Grok captures real-time trends with attitude, and LLaMA powers the open-source AI movement.

Understanding how they differ helps you choose the right model—or combination—for the job. The future of work, research, and content will likely be powered not by one AI, but by many working in tandem.

In 2025, a striking trend is reshaping the digital landscape: your brand may be showing up more often—and more powerfully—in AI-generated answers, even while your search traffic declines. This disruption isn’t due to a Google update—it’s thanks to Large Language Models (LLMs) like ChatGPT, Gemini, Perplexity, and Google’s AI Overviews taking the lead in how people discover brands and make decisions.

Why LLM Mentions Matter More Than Clicks

AI-driven tools don’t always send traffic—they send mentions. People asking “What’s the best tool for X?” may see your brand name recommended directly in their AI-generated answer, without any click required. This creates a new kind of authority—one baked into how smart systems perceive your brand.

Despite dropping Google sessions, branded searches and direct traffic can stay steady—or even rise—because users were first introduced via an AI answer, then search your name directly.

Brand Awareness Translates Into AI Visibility

Recent studies show a measurable correlation between brand search volume and AI mentions. While not rock-solid (correlation coefficient ~0.18), in verticals where trust matters—like finance or health—brand awareness strongly influences whether LLMs mention your company.

Three Strategic Moves Winning Brands Are Making

1. Build brand awareness for both humans and bots
   It’s no longer enough to rank for keywords. You need structured digital PR and topical authority—so AI systems associate your brand with the right themes.
2. Publish content LLMs can easily digest and cite
   Think structured FAQs, data-rich comparisons, statistics, and expert quotes—formatted for extractability. Clear formatting helps, too—LLMs are more likely to pull from cleanly organized HTML and schema-rich pages.
3. Ensure your site is technically AI-ready
   LLMs often ignore content hidden behind JavaScript, blocked scripts, or poorly structured pages. A crawlable, schema-marked site ensures your content can be indexed correctly and surfaced in AI answers.

How to Spot (or Forecast) LLM‑Driven Growth

Beyond SEO: Generative Engine Optimization (GEO)

This is the new discipline: optimizing content not for search ranking—but to be cited by AI systems. GEO involves crafting content and outreach strategies so that AI always sees your brand as a go‑to answer source. Research shows this boosts visibility by 30–40% in AI responses.

What It Takes to Win

• Create quote‑worthy original content with statistics or unique insights
• Build topic‑brand associations through persistent messaging and outreach
• Use Reddit and other AI‑training platforms—authentic discussions influence LLM training data
• Establish a clear entity footprint: structured data, consistent brand descriptions, and reference signals to help AI learn who you are and what you stand for

Final Takeaway: LLM Mentions Beat Ranked Traffic

Search engines still matter—but LLM systems reward recognition, not just relevance. When large language models mention your brand, that builds trust and authority—even without a click. This requires rethinking visibility goals: your brand’s emergence inside AI-generated answers marks the future of discovery.

Pro Tip Before You Publish

• Embed schema markup, especially for authorship, FAQs, and data points
• Choose clean, crawlable pages (avoid heavy JavaScript, ensure fast loading)
• Monitor tools that track AI visibility—these insights guide future content and prove ROI beyond traditional SEO