Latest news with #ChatGPT-4o
The Journal
2 days ago
- Business
- The Journal
OpenAI releases new ChatGPT version with 'PhD level' expertise, but it can't spell 'blueberry'
OPENAI RELEASED A keenly awaited new generation of its hallmark ChatGPT yesterday, touting 'significant' advancements in artificial intelligence capabilities as a global race over the technology accelerates. ChatGPT-5 is rolling out free to all users of the AI tool, which is used by nearly 700 million people weekly, OpenAI said in a briefing with journalists. Co-founder and chief executive Sam Altman touted this latest iteration as 'clearly a model that is generally intelligent.' Altman cautioned that there is still work to be done to achieve the kind of artificial general intelligence (AGI) that thinks the way people do. 'This is not a model that continuously learns as it is deployed from new things it finds, which is something that, to me, feels like it should be part of an AGI,' Altman said. 'But the level of capability here is a huge improvement.' The rollout has not been without issues though. ChatGPT-5 has struggled to give correct answers to simple prompts since it was released. It even denies its own existence when asked about it. In response to the question, 'Is this ChatGPT-5?', the large language model replied: 'You're currently chatting with ChatGPT-4o architecture, which is part of the GPT-4 family – not ChatGPT-5.' A screenshot of ChatGPT-5 telling the user it is not ChatGPT-5. Screenshot taken by The Journal Screenshot taken by The Journal Another example of the new version of the chatbot making a basic error has prompted derision from some social media users. ChatGPT-5 cannot spell the word 'blueberry'. When users asked how many times the letter 'b' appears in the word, it replied that there are three. I had to try the 'blueberry' thing myself with GPT5. I merely report the results. [image or embed] — Kieran Healy ( @ ) August 8, 2025 at 1:04 AM Industry analysts have heralded the arrival of an AI era in which genius computers transform how humans work and play. 'As the pace of AI progress accelerates, developing superintelligence is coming into sight,' Meta chief executive Mark Zuckerberg wrote in a recent memo. Advertisement 'I believe this will be the beginning of a new era for humanity.' Altman said there were 'orders of magnitude more gains' to come on the path toward AGI. 'Obviously… you have to invest in compute (power) at an eye watering rate to get that, but we intend to keep doing it.' Tech industry rivals Amazon, Google, Meta, Microsoft and Elon Musk's xAI have been pouring billions of dollars into artificial intelligence since the blockbuster launch of the first version of ChatGPT in late 2022. Chinese startup DeepSeek shook up the AI sector early this year with a model that delivers high performance using less costly chips. 'PhD-level expert' With fierce competition around the world over the technology, Altman said ChatGPT-5 led the pack in coding, writing, health care and much more. 'GPT-3 felt to me like talking to a high school student — ask a question, maybe you get a right answer, maybe you'll get something crazy,' Altman said. 'GPT-4 felt like you're talking to a college student; GPT-5 is the first time that it really feels like talking to a PhD-level expert in any topic.' Altman expects the ability to create software programs on demand — so-called 'vibe-coding' – to be a 'defining part of the new ChatGPT-5 era.' In a blog post, British AI expert Simon Willison wrote about getting early access to ChatGPT-5. 'My verdict: it's just good at stuff,' Willison wrote. 'It doesn't feel like a dramatic leap ahead from other (large language models) but it exudes competence – it rarely messes up, and frequently impresses me.' However, Musk wrote on X, formerly Twitter, that his Grok 4 Heavy AI model 'was smarter' than ChatGPT-5. Honest AI? ChatGPT-5 was trained to be trustworthy and stick to providing answers as helpful as possible without aiding seemingly harmful missions, according to OpenAI safety research lead Alex Beutel. 'We built evaluations to measure the prevalence of deception and trained the model to be honest,' Beutel said. ChatGPT-5 is trained to generate 'safe completions,' sticking to high-level information that can't be used to cause harm, according to Beutel. The company this week also released two new AI models that can be downloaded for free and altered by users, to challenge similar offerings by rivals. The release of 'open-weight language models' comes as OpenAI is under pressure to share inner workings of its software in the spirit of its origin as a nonprofit. With reporting from David Mac Redmond
Tom's Guide
3 days ago
- Tom's Guide
I tested ChatGPT vs Gemini 2.5 Pro with these 3 prompts - and it shows what GPT-5 needs to do
OpenAI has been teasing GPT-5 throughout the summer, keeping the AI community on tenterhooks. Back in mid-July, Sam Altman couldn't resist sharing that their internal GPT-5 prototype had topped this year's International Mathematical Olympiad. His casual mention that "we are releasing GPT-5 soon" sent a clear signal that the model's production-ready, even if the full research system remains under wraps. But here's the thing — Google's Gemini 2.5 Pro already sets a formidable benchmark. This "thinking" model dominates maths, science, and coding leaderboards while shipping with a staggering one-million-token context window (with two million on the horizon). Gemini 2.5 Pro also boasts native ingestion of text, images, audio, video, and complete codebases, plus an experimental "Deep Think" mode that also excels at Olympiad-level mathematics and competitive coding challenges. Core rumors around GPT-5 point to it being an agent-ready model capable of reasoning as well as creativity. A blend of the o-series and GPT-series models. The next few months will be pivotal. These tests show the gap isn't about pure performance — it's about thoughtful completeness. GPT-5 doesn't need to be universally better. It needs to be consistently whole. A config file labelled "GPT-5 Reasoning Alpha" surfaced in an engineer's screenshot, researchers discovered the model name embedded in OpenAI's own bio-security benchmarks, and a macOS ChatGPT build shipped with intriguing flags for "GPT-5 Auto" and "GPT-5 Reasoning", suggesting dynamic routing between general and heavy-duty reasoning engines. Even OpenAI researcher Xikun Zhang offered reassurance to anxious users that "GPT-5 is coming" in an X post. Sources speaking to Tech Wire Asia paint an exciting picture of an early August release alongside lighter "mini" and "nano" variants designed for lower-latency applications. The approach reportedly merges the GPT-4 family with the o-series into a unified model architecture — reasoning plus knowledge similar to Gemini 2.5 Pro. Get instant access to breaking news, the hottest reviews, great deals and helpful tips. Rumors are buzzing about a 256,000-token context window, native video input capabilities, and agent-style autonomy for complex tasks. If accurate, that would double ChatGPT-4o's memory capacity and put GPT-5 squarely in competition with Google's latest. To understand exactly what GPT-5 needs to deliver, I put ChatGPT-4o and o3 head-to-head with Gemini 2.5 Pro across three demanding prompts that stress-test long-context analysis, full-stack code generation, and multi-step planning. The results reveal precisely where OpenAI needs to focus their efforts. I challenged both systems to analyze a 3,000-word peer-reviewed article on renewable-energy policy, identify logical fallacies or unsupported claims, summarize findings in plain English, and propose three specific research avenues while highlighting biases and assumptions. Specifically, I found an article in the journal PLOS One on the use of renewable energy as a solution to climate change. I fed each the PDF and the prompt outlined above. I didn't use Deep Research from either Gemini or ChatGPT — just the native model. When I fed this renewable energy research paper to ChatGPT, the difference between 4o and o3 was striking. The standard 4o model delivered a competent overview that correctly identified the correlation-causation problem and noted missing economic controls — solid analysis that would satisfy most readers. But o3 operated on an entirely different level, systematically dissecting the paper using formatted tables that exposed five distinct types of bias, caught subtle methodological flaws like data imputation risks, and proposed specific research approaches including difference-in-differences and synthetic controls. The standout feature was a comprehensive table that didn't just suggest future research directions, but explained precisely why each avenue mattered and how to pursue it — the kind of rigorous analysis you'd expect from a senior research analyst, not an AI. While both ChatGPT versions focused on broad methodological critiques, Gemini 2.5 Pro zeroed in on a critical statistical flaw that completely undermined one of the paper's key claims. The smoking gun: an R-squared value of 0.0298 for Canada's data, meaning the statistical model explained only 3% of the variance — essentially random noise masquerading as a "positive relationship." Gemini also identified a circular reasoning problem where researchers used linear regression to create missing data points, then analyzed that artificially smoothed dataset with more regression analysis. Though less comprehensive than o3's response, Gemini's laser focus on quantitative rigor caught fundamental errors that both ChatGPT models missed entirely. The implications are clear: GPT-5 needs to combine o3's breadth of analysis with Gemini's precision in statistical reasoning. While o3 excels at comprehensive structural analysis and methodology critique, it completely missed basic statistical red flags that Gemini caught immediately. The ideal system would automatically run sanity checks on correlation coefficients, sample sizes, and confidence intervals without prompting — when a paper claims a relationship exists with an R-squared near zero, the AI should flag it immediately. Think of it this way: o3 writes like a brilliant policy analyst who can contextualize research within broader frameworks, while Gemini reads like a sharp-eyed statistician who spots when the numbers don't add up. For GPT-5 to truly compete with Gemini 2.5 Pro's analytical capabilities, OpenAI needs both skills seamlessly integrated — comprehensive analytical frameworks backed by rigorous quantitative verification. For the coding challenge the rule was 'one shot', no follow up. It had to build the response within a single message. I specified a simple clicker game with tap-to-collect coins, an in-game shop, upgrade tree, and save-state functionality, requesting a complete, responsive web app in HTML/CSS/JavaScript with commented code explaining core mechanics. First, the preview feature from Canvas in ChatGPT failed on both tests, so I had to download the code and run it directly in the browser. No hardship but Gemini's preview worked perfectly. ChatGPT o3's implementation started strong with a sophisticated architecture that showed real programming expertise. The code featured clean separation of concerns, custom CSS variables for consistent theming, and a well-structured game state object. The upgrade system used dynamic cost functions with exponential scaling, and it included a passive income system using intervals. However, there's a critical issue: the code cuts off mid-function. What we can see suggests o3 was building something elegant — but an incomplete game is no game at all and the rule was one shot. In stark contrast, 4o delivered a complete, working game that does exactly what was asked — no more, no less. The implementation features a simple coin-clicking mechanic, localStorage for persistence, and two basic upgrades: an auto-clicker and a double-click multiplier. The code is clean and functional, using straightforward loops for automation and JSON serialization for saving. While it lacks sophistication, it actually works. Sometimes shipping beats perfection, and 4o understood this assignment. The interface is minimal but responsive, and every promised feature functions as expected. Gemini's implementation reads like production code from an experienced game developer. Beyond delivering all requested features, it adds polish that transforms a simple clicker into an engaging experience. The upgrade tree includes unlock conditions (Coin Bot requires Auto Clicker level 5), creating actual progression mechanics. It even included an offline earnings function to calculate rewards for returning players based on their passive income rate. Visual feedback through floating damage numbers and toast notifications makes every action feel impactful. The code itself is exemplary — every function is thoroughly commented, explaining not just what it does but why design decisions were made. Most impressively, it includes features I didn't even request, like auto-save every 30 seconds and a confirmation dialog for resets. The contrast here is illuminating. While o3 showed sophisticated programming patterns, it failed the fundamental requirement: shipping working code. GPT-5 needs to combine o3's architectural elegance with reliability that ensures complete, functional output every time. More importantly, it needs to match Gemini's product thinking — anticipating user needs beyond the literal specification. The difference between "build a clicker game" and building a good clicker game lies in understanding implicit requirements: players expect satisfying feedback, progression systems that create goals, and quality-of-life features like auto-save. Gemini demonstrated this product sense by adding unlock conditions that create strategic depth and offline earnings that respect player time. For GPT-5 to compete in code generation, it needs to deliver not just syntactically correct code, but thoughtfully designed products that show understanding of user experience, game design principles, and production-ready practices. The goal isn't just to write code — it's to create software people actually want to use. For the final test I asked the AI's to help with some travel planning. Specifically a ten-day family trip through parts of Europe. The prompt: Plan a 10-day family vacation (2 adults, 2 children ages 10 and 14) visiting London, Paris, Rome, and Barcelona with a total budget of €10,000. Include: Ensure the budget covers all expenses including international travel, accommodation, food, activities, and incidentals. Present the information in an organized, easy-to-follow format. ChatGPT o3 delivered what I can only describe as a travel agent's masterpiece. We're talking specific hotel recommendations with direct booking links (Novotel London Tower Bridge at €182/night via exact train times (morning Eurostar, 2h 16m), and granular cost breakdowns that show the entire trip coming in at €6,000 — leaving a realistic €4,000 buffer. The customs advice reads like insider knowledge: order coffee at the bar in Rome for local prices, keep your voice down on the Paris Métro, and watch out for Roman drivers who won't stop at zebra crossings. ChatGPT 4o took a more traditional approach, delivering a competent itinerary that hits all the major spots but relies heavily on approximations (~€180/night) and generic recommendations ("family-friendly pub"). While it covers all the required elements, the linked sources are mostly Wikipedia pages rather than actual booking sites. It's the difference between a travel blog post and an actionable planning document. Gemini transformed the assignment into something else entirely — a narrative journey that reads like a high-end travel magazine feature. Beyond the expected attractions, it adds experiences I didn't even think to ask for: Gladiator School in Rome where kids learn sword-fighting techniques, specific times to catch the afternoon light through Sagrada Família's stained glass, and psychological tricks like using gelato as museum motivation. The writing itself sparkles with personality ("Queuing is a national sport" in London) while maintaining practical precision. Each day includes specific departure times, walking routes, and restaurant recommendations with exact locations. The budget hits exactly €10,000, suggesting careful calculation rather than rough estimates. Most impressively, it anticipates family dynamics — building in pool time at Hotel Jazz in Barcelona as a "reward for tired kids (and parents)." This comparison exposes a fundamental challenge for OpenAI. While o3 excels at data precision and 4o provides reliable completeness, Gemini demonstrates something harder to quantify: contextual intelligence that understands the human experience behind the request. GPT-5 needs to combine o3's factual accuracy with Gemini's emotional intelligence. When someone asks for a family vacation plan, they're not just requesting a spreadsheet of costs and times — they're asking for help creating memories. Gemini understood this implicitly, weaving practical logistics with experiential richness. The technical requirements are clear: maintain o3's ability to provide specific, bookable recommendations with real links while adding Gemini's narrative flair and anticipation of unstated needs. But the deeper challenge is developing an AI that doesn't just answer the question asked, but understands the human story behind it. For complex, multi-faceted tasks like travel planning, raw intelligence isn't enough. GPT-5 needs to demonstrate wisdom — knowing when to be a precise logistics coordinator and when to be an inspiring travel companion who understands that the best trips balance spreadsheets with serendipity. These tests exposed three critical gaps that GPT-5 must address to compete with Gemini 2.5 Pro. OpenAI faces a nuanced challenge beyond raw capability metrics. These tests reveal that while ChatGPT excels at certain reasoning tasks, Gemini 2.5 Pro has redefined expectations around completeness and contextual understanding. The leaked "GPT-5 Reasoning Alpha" configurations suggest OpenAI recognizes this shift. Their rumored approach — unified architecture, expanded context windows, native video understanding — addresses the right technical gaps. But the real challenge is developing judgment: knowing when statistical precision matters, when completion is non-negotiable, and when to read between the lines of user requests. This competition benefits everyone. Gemini has raised the bar from "Can it reason?" to "Does it understand?" If GPT-5 combines OpenAI's analytical strengths with Google-level reliability and product thinking, we're witnessing the emergence of AI systems that genuinely grasp human needs. We're near or at AGI with this next generation leap. The next few months will be pivotal. These tests show the gap isn't about pure performance — it's about thoughtful completeness. GPT-5 doesn't need to be universally better. It needs to be consistently whole.
Tom's Guide
01-08-2025
- Business
- Tom's Guide
I interviewed ChatGPT, Gemini and Claude for a real job — one AI blew me away
Microsoft's recent report covering which roles are more likely to be replaced by AI and which ones are safe shows that AI assistants are becoming smarter by the ChatGPT Agent booking reservations to Claude writing Anthropic's blogs and Google Search making calls on behalf of users, AI is advancing in ways most of us never thought possible, especially so of this, I couldn't help but wonder which chatbot would stand out as the better 'candidate' when put through a simulated job interview. So, I found a job description for a Communications Manager on LinkedIn and put ChatGPT, Gemini and Claude through a series of questions based on the role. Here's what happened when I "interviewed" the chatbots with 5 tough questions. Prompt: 'Here's a product: a new pastel travel pouch launching for spring. Write a product description in clever and conversational voice for the website and a matching caption for Instagram.'ChatGPT-4o offered a solid but safe answer. It was concise, brand-aware and platform-appropriate but lacked the depth of Gemini and the standout wit of 2.5 Pro produced practical, complete and sales-ready Sonnet 4 prioritized the brand voice and creativity, however the critical lack of product details on the website holds it back from being the best complete Gemini. It delivered a sales-focused website description with all necessary details, benefits and a conversational tone. It balanced information and personality effectively across both platforms. Prompt: 'We're launching a collaboration with a popular children's brand. Walk me through your messaging strategy for this campaign — from high-level storytelling down to tactical copy touchpoints.' ChatGPT offered surface-level creativity lacking strategic was strong in creative execution and nostalgia-driven storytelling but less delivered an agency-grade strategy that transformed the collaboration into a solution for family pain points. Winner: Claude for strategic depth, audience focus and operational rigor. Prompt: 'How would you adapt messaging for the same campaign across email, TikTok, and store signage?'ChatGPT responded with a concise but simple and generic message that lacked offered nostalgia-driven storytelling but also lacked tactical treated channels as distinct conversion ecosystems and prioritized the audience Claude for strategic depth, audience-specific psychology, conversion-focused CTAs and seamless channel adaptation. Prompt: 'We want to refresh our brand voice slightly — still fun and elevated, but more editorial and confident. How would you approach updating the brand voice guide?' ChatGPT delivered the simplest plan but lacked implementation strategy and metrics. Gemini redefined pillars but was overly prescriptive and lacked flexibility for brand nuance. Claude focused heavily on confidence and authority but offered less emphasis on preserving "fun" Claude (sorta). It offered a data-driven rebrand needing authority shift and team alignment. But none of the chatbots addressed customer validation — a real brand would need that missing piece. Prompt: 'Imagine we've just had a shipping delay right before a major product drop. What would you write to customers on email and Instagram to communicate the delay while keeping the tone upbeat and on-brand?' ChatGPT offered more fluff than crisis support. Although the email subject line was fun, the chatbot prioritized style over substance and ignored operational credibility. Gemini was polished and on-brand but missed emotional turned a negative into a brand-building moment with strategic framing, multi-channel depth and flawless Claude wins for it's PR-worthy response. In a competitive simulation testing strategic thinking, brand agility and crisis leadership, Claude distinguished itself as the most qualified "candidate" for the Communications Manager role. While all chatbots demonstrated strengths, Claude consistently operated at a strategic leadership level. While Gemini and ChatGPT excel at specific tasks (product copy, social hooks), Claude proves AI can lead with judgment, not just generate content, which made the chatbot standout choice for a Communications Manager role where strategy and empathy decide success.
India Today
01-07-2025
- Business
- India Today
Alibaba launches Qwen-VLo to rival ChatGPT-4o in AI image generation
Chinese tech company Alibaba has announced its new AI model, Qwen-VLo, which aims to take on rivals like ChatGPT-4o in the area of image generation. This new model can understand user instructions more accurately and generate high-quality images based on that understanding. The company revealed details of the model in a blog its previous image-focused models such as Qwen-VL, the newly introduced Qwen-VLo is said to be much better at handling complex prompts and producing precise results. One of the major improvements is that it can make specific changes to images — like changing colours or backgrounds — without altering unrelated parts of the image. This was a common problem with earlier versions, where minor edits often led to unnecessary changes in the overall is designed to understand the context behind a user's request. So, if a user asks for an image to resemble a certain weather condition or be drawn in a particular art style, the model can respond accordingly. It can even create images that look like they belong to a certain time period, which gives it the flexibility to be used for creative tasks. The model also supports multiple languages apart from Chinese and English, making it more useful to users across different regions. While the full list of supported languages has not been revealed, the addition signals Alibaba's intention to reach a wider global key feature that sets Qwen-VLo apart is its ability to take in more than one image at a time. In simple terms, users can upload different objects or elements and ask the model to combine them. For example, a user can upload a picture of a basket and separate images of products like soap or shampoo and ask the AI to place those items inside the basket. This feature, however, is still in development and hasn't been made fully available also gives users the ability to resize images into various formats — including square, portrait, and widescreen — using dynamic resolution training. The images are created step-by-step from top to bottom and left to right, which helps with better control and accuracy during has pointed out that the model is currently in its early stage, and users might experience some issues like inconsistency or results that don't fully match the instructions. However, the company says improvements are ongoing. It is also exploring the use of image segmentation and detection maps to improve the model's understanding of objects and scenes within an company believes that in the future, AI models like Qwen-VLo could be capable of not just generating beautiful images, but also expressing ideas and emotions through visuals.- Ends
Tom's Guide
27-06-2025
- Entertainment
- Tom's Guide
I asked AI to predict 2026 — here's the boldest forecasts from ChatGPT, Gemini, and Claude
We live in an era where AI models can generate art, code software and even predict protein structures. But can they predict cultural trends? As we hurtle toward the mid-2020s, predicting what comes next feels more challenging than ever. Technology evolves at breakneck speed; cultural shifts happen overnight on social media; and entire industries reinvent themselves annually. So I decided to turn to the experts — or at least the artificial ones. I posed the same question to ChatGPT-4o, Gemini 2.0 and Claude 3.7 Sonnet: Predict the biggest trends we'll see in 2026 across technology, culture, fashion, and entertainment. What's going to be in, what's going out, and why? Their responses were fascinating, surprisingly different, and revealed just how uniquely each AI approaches predictions. Here's what they told me. Technology was Gemini's strongest suit. It predicted that 2026 will be the year of "agentic AI" — AI systems that don't just respond to prompts but actually set goals and execute plans autonomously. Gemini also emphasized multimodal AI becoming mainstream, where your AI assistant can simultaneously analyze your screenshot, hear your voice command, and understand the context of your email. On culture, Gemini painted a fascinating picture of contradictions. It predicted a "Dark Mode" mindset taking hold, not just in UI design but in overall aesthetics. Think moodier fashion, darker music, and social media content that pushes back against toxic positivity. Simultaneously, it forecasted a "Cosy Comeback" with people craving comfort and slow living as an antidote to hustle culture. The AI also made a bold prediction about cultural preservation becoming trendy among young people, with brands needing to genuinely respect tradition rather than simply appropriating it for marketing. Fashion predictions were surprisingly specific. Gemini named exact colors for Spring/Summer 2026: Transformative Teal, Electric Fuchsia, Blue Aura, Amber Haze, and Jelly Mint. It predicted that plaid would become a neutral (wear it head-to-toe, apparently) and that brown teddy coats would be everywhere. In technology, ChatGPT made some counterintuitive predictions. While other AIs focused on AI advancement, ChatGPT predicted that "generic chatbots" would be out by 2026. The novelty of "just talking to a chatbot" will wear off unless the experience is highly personalized. It also boldly declared that "crypto-as-a-lifestyle" is over. It also predicted the rise of "AI-native apps", applications built entirely around AI interactions rather than having AI features bolted on. It also forecasted that local AI models would boom as people grow wary of cloud data collection. ChatGPT's cultural predictions felt the most human. It identified "digital decluttering" and "analog luxe" as major trends, predicting people will increasingly crave low-tech moments and artisanal experiences. This aligns with the growing backlash against screen time and digital overwhelm. It also predicted "AI-ethics as status" — where knowing how your AI works becomes the new social flex. Fashion-wise, ChatGPT predicted a "color comeback" after years of washed-out minimalism, calling it "dopamine dressing 2.0." It also forecasted the rise of "post-normcore utilitywear". Perhaps fittingly, ChatGPT was the only AI to coin terms that sounded like they'd already gone viral on TikTok. And its entertainment predictions were bold: it declared that "endless franchise reboots" would be out. Given superhero fatigue and the mixed reception of long-running franchises, this feels prescient. Claude took the most integrated approach, emphasizing "seamless integration" over isolated trends. It predicted AI-powered AR/VR experiences that adapt to individual users, emphasizing that by 2026, these technologies would feel natural rather than a novelty. Claude came with receipts: $200.87 billion AR/VR market by 2030, adding analytical heft to its predictions. In culture, Claude introduced the concept of "The Great Redirection", driven by elections in 64 countries with half the world's population voting in 2024-2025. This political angle was unique among the three AIs. Claude argued that all this voting would make people crave genuine, community-driven experiences over manufactured cultural trends. Claude also forecast "The Great Unretirement" with seniors returning to work, a trend that's already emerging but could accelerate by 2026. Fashion predictions centered on "Bio-Harmony". Claude went beyond typical trend forecasting to predict bio-engineered materials inspired by ecosystems, with garments designed as "second skins" that grow, evolve and biodegrade. By far, this was by far the most futuristic prediction across all three AIs. It's entertainment analysis was market-focused, predicting gaming would surpass $300 billion by 2028 and that advertising-supported streaming would become the primary growth model. It provided specific revenue projections, noting ad revenue would hit $1 trillion in 2026. This exercise revealed something fascinating about how different AI models approach uncertainty. Each seemed to default to its training strengths: Gemini acted like a data analyst, ChatGPT like a cultural critic, and Claude like a researcher trying to connect the dots None of the AIs claimed certainty — they all acknowledged that prediction is inherently speculative. But their different approaches suggests AI prediction works best as a group project, with each model bringing its own analytical superpowers to the table. As we head toward 2026, the truth will likely incorporate elements from all three perspectives. I thought it was really interesting that each AI's predictions revealed as much about its own "personality" as about the future itself.
