Top AI Predictions for 2026 from 17 AI-First Creators
Discover 14 AI predictions for 2026 and how agentic AI, multi-modal workflows, and AI-native tools can boost your business automation.
I asked 17 creators who use AI every day one simple question:
What are you betting on for 2026?
Not what they hope will happen. Not what would make a good headline. What they’re putting their time, money, and businesses into.
The answers surprised me. I thought AI predictions for 2026 would focus on better models, smarter prompts, or cooler tools. Instead, I got something more interesting:
The creators most excited about AI are betting hardest on human parts.
More on that in a moment.
First, the practical stuff. Each creator answered two questions:
What’s your biggest bet for 2026 when it comes to AI?
What’s something most people are sleeping on that you think will be leverage in 2026?
I’ve grouped their predictions into themes so you can find what fits YOUR work. Skip to the section that matches your challenge. Bookmark the sleeper picks that make you think.
What Are the Biggest AI Predictions for 2026?
The biggest AI predictions for 2026 focus on five key themes:
human-AI teamwork where human judgment stands out,
AI agents becoming common in business,
vibe coding letting non-coders build real products,
AI literacy becoming a must-have skill, and
platform changes toward owned audiences.
Creators who build with AI daily say “winners will automate routine work while lifting human skills.”
Let’s deep dive into each of these themes one by one.
👋 Julley, I’m Dheeraj and I’m an AI systems builder.
I build production-grade AI systems at work by day and ship my own products by night (9+). This newsletter is the bridge between those two worlds. Every system, every build, documented step by step.
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Theme A: “Become Unpromptable” aka The Human Advantage
AI has made content creation very easy. Anyone can make a blog post, LinkedIn update, or newsletter draft in seconds. But that’s the problem. When everyone has the same power, it stops being special.
Four creators came to the same surprising conclusion. In a world full of AI-made content, human qualities become your edge. Not despite AI, but because of it.
The question isn’t “how do I use AI better?” It’s “what can I offer that AI can’t copy?”
This matters because we already see the effects. Feeds are full of content that’s correct but feels flat.
Readers sense AI-made content, not with tools, but by feeling it’s not truly created. The creators betting on human advantage aren’t being nostalgic. They’re being smart.
Nick Quick said something that stuck with me:
“The content treadmill breaks people. Quality over quantity will win. Creators who post memorable content beat those posting daily but forgettable posts.”
Nick goes further. He thinks when AI makes content instantly, “the post itself loses value.” What becomes valuable?
He calls it “human proof-of-work” which is showing messy creative processes instead of polished results. Content that’s “clearly human” and “obviously worked on.” The rough parts become the prize.
James Presbitero puts it simply:
“Becoming ‘Unpromptable’. Showing human qualities AI can’t copy while automating what can be automated.”
The word “unpromptable” is key. It’s not about avoiding AI. It’s about building qualities AI can’t copy. Your judgment, taste, relationships, and experience.
James says AI can automate routine tasks, freeing you to focus on “things that can’t” be automated.
Mia Kiraki 🎭 bets on teamwork:
“AI systems that boost a person’s unique story and point of view through human-AI teamwork instead of drowning uniqueness.”
She sees us moving past just “Generate content” to something better. Winners will show “human-AI teamwork that brings out your unique storytelling.”
Raghav Mehra frames it as economics:
“When AI handles execution, judgment and direction-setting become your actual scarce resource. The ability to ask the right questions becomes harder to commoditize than the ability to execute.”
Strategists, internal or customer-facing, become disproportionately valuable. Not because AI can’t execute, but because it can. Someone still needs to point it in the right direction.
Pinkie (AI Meets Girlboss) makes it practical:
“Early AI users with strong judgment will stand out. Using AI to make high-quality work without lowering standards moves careers forward.”
Pinkie points out timing matters. People using AI now get an edge while “rules are still loose.” But the edge isn’t tools.
It’s judgment: knowing how to guide AI, question its output, and “go beyond the first draft.”
She sees that “AI content blends together because people generate, skim, and publish.”
Winners? Those with “taste and standards” who spot flat work and add personal value.
The summary: Four creators see the same thing. As AI makes production cheap, judgment becomes valuable. Your taste, standards, and saying “this isn’t good enough”; that’s your edge.
Theme B: AI Agents Go Mainstream
If 2025 was when AI agents left labs, 2026 is when they enter daily work. But not how most expect. The breakthrough isn’t smarter agents. It’s simpler interfaces.
Right now, AI agents need technical skills. Setting up automations requires coding to an extent or comfort with complex tools. That will change.
Five creators bet 2026 is when non-technical people get access to agent workflows. Systems that don’t just answer prompts but do multi-step tasks on their own.
This changes how solopreneurs and small teams work. AI stops being a feature you click. It becomes a layer running daily operations. The productivity gains are big. In fact at GenAI Unplugged, most of my daily operations layer is powered by AI agents and Agentic AI workflows only.
Ilia Karelin says:
“AI agents, sub-agents, skills, tools, and plugins will be common topics in 2026.”
He notes they “started growing fast” before 2025 ended and will be huge in 2026.
Wyndo bets on what helps mass use:
“Better user experience for agents lets non-tech people move from chatbot prompts to agent workflows, plus personal branding no matter the format.”
Wyndo points out 2025 agents used platforms like n8n, Langchain, and Claude Code. All needing tech skills or comfort with tech skills. The 2026 shift lets “normal people jump in” and move “from chatbot prompts to agent workflows.”
He adds: “Everyone can build anything now, but not everyone has your story.”
Carrie Loranger describes:
“Solopreneurs speed up work with AI agents. Stacked automations handle monitoring, prioritizing, and doing tasks with human check on the last 20%.”
She describes a system where “one watches inbox, calls, analytics; one prioritizes and drafts; one sends, posts, schedules.” AI moves from a feature to a process layer.
The 80/20 split is key. Not full automation, but smart automation with human checks.
Daria Cupareanu adds a key point:
“AI agents get better than most expect; those who build clear processes and feedback loops will win.”
She says agents “can plan, act, and fix themselves” but success needs the “boring stuff”: clear steps, goals, checkpoints, context, and reviews.
This is like a management job. People who skip this aren’t ready for agents.
Elena Calvillo at Product looks at teams:
“Agentic Orchestrator helps manage people. AI moves from personal assistant to team manager, keeping everyone aligned and spotting problems.”
For teams, Elena says the hard part is the messy human side. Keeping leaders on track and avoiding strategy drift. She sees agent systems that “map influence and predict issues.”
The summary: Five creators see agents moving from “developer tools” to “standard work.” But all say clear processes come before good agents. You can’t automate chaos.
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Theme C: Vibe Coding Makes Building Normal
For years, there was a clear line between “idea people” and “builders.” You either coded or needed someone who did. That line is fading. Two creators bet 2026 is when it disappears.
This isn’t AI writing code for developers. It’s AI letting non-coders ship real products. As Jenny Ouyang experienced “A filmmaker building an iOS app. A marketer making a search tool. A designer launching a portfolio with AI features.”
These are real examples. 2026 is when “vibe coding” stops being new and becomes as normal as using spreadsheets.
This changes more than speed. When anyone with knowledge and clear thinking can build, the bottleneck moves from skill to action. “I have an idea but can’t build it” won’t fly.
Jenny Ouyang says:
“Vibe coding changes who can build. The barrier between idea and builder is falling.”
She gives examples: a filmmaker building an iOS app in 8 days, a marketer making a search tool, a designer launching a portfolio with AI features. People without coding skills shipping products “in days, not months” using AI helpers.
By 2026, vibe coding will be “as normal as spreadsheets.”
Elena Calvillo at Product adds:
“Vibe Coding will replace old specs with natural language intent-driven specs.”
While others see vibe coding as prototype tools, Elena sees it as a “communication layer for complex systems.” She predicts the old 20-page spec will be gone. Instead, the vibe, simple natural language, will be the live spec.
The summary: “I have an idea but can’t build it” stops being an excuse. The tech barrier drops so execution, not skill, is the limit.
Theme D: AI Literacy Becomes Essential
AI literacy is a must for 2026. But the creators betting on literacy aren’t talking about prompt tricks or tool skills. They’re talking about something harder.
The judgment.
The skill isn’t just how to use AI. It’s knowing when NOT to use it. When to override AI. When to slow down. When to refuse automation.
This is surprising in an AI bets roundup. But it’s the main idea from three creators who think about lasting AI use.
This matters because “AI slop” is already everywhere. Low-quality AI content floods platforms. The winners won’t be those who use AI most. They’ll be those who use it smartest.
Joel Salinas sees it as a leadership skill:
“AI literacy will be a must-have leadership skill.”
Sam Illingworth gives a surprising view:
“Deliberate friction. Systems that slow AI use with pauses, reflection, and human judgment.”
I know you may be thinking, Dheeraj, Slowing AI? In an AI bets list?
Sam’s point: “Prompts, workflows, and rules that add pauses and human judgment” become valuable because speed is common. “Trust, judgment, and realness are not.”
Knowing when NOT to use AI is as important as knowing how. Adding friction like forced pauses, judgment gates will play crucial role in stopping the “AI slop” flooding the web.
Matt Giaro combines both views:
“Learning how to steer AI well, not chasing every new tool.”
Matt is building Contentbase to “train AI on your past content”. It is what he calls “the easiest way to get more from your ideas and stop AI mistakes.”
His sleeper bet isn’t the tool, it’s the skill. Learning “the right way” to use AI means knowing how to guide it. He calls this “AI-steering skills”, knowing enough to direct AI without mastering everything.
The summary: AI literacy isn’t just prompt engineering. It’s judgment. Knowing when to use AI, when to override it, and when to ignore it. The skill is steering, not prompting.
Theme E: Platform & Business Model Changes
Three creators bet on big changes beyond AI tools. Think where we publish, how we organize work, and who gets hired.
These aren’t flashy predictions, but they matter. The platforms you pick, systems you build on, and credentials that count are all changing. Getting these right gives big advantages.
Karen Spinner sees a platform move:
“Users moving away from social media to Substack and gated communities as social media growth slows.”
Karen points to Pew Research data showing “social media growth has already slowed.” Moving to owned audiences and gated groups is a response to unstable algorithms and less free reach.
Well, I will add, if you are here reading it on Substack, you are already in the right platform :)
Anfernee bets on one system:
“Notion AI as the main workspace for solopreneurs, with AI-first offers giving faster results and higher value.”
He sees Notion as where “thinking, planning, and doing happen in one place.” With “AI-first offers”, think products with built-in AI like adaptive templates and guided tools, solopreneurs can deliver faster and better.
Anna Levitt predicts hiring changes:
“Companies hiring for learning speed over fixed roles; portfolios and proof-of-work replacing degrees.”
Anna sees careers becoming “skill networks,” with degrees losing power and portfolios becoming key. This changes how people build and show credibility.
The summary: The base is changing. Where we publish, how we work, who we hire. AI speeds all this up.
Those are the bets for 2026. Now for what most people might miss if they don’t pay attention...
Most people who read prediction roundups like this one close the tab, move on, and test nothing. The creators pulling ahead in 2026 are not the ones who read the most articles. They are the ones who ran the first small experiment within 48 hours.
PluggedIn has the actual prompts, workflow configs, and checklists I used to build my own AI layer, so you skip the trial-and-error and run your first test today.
What AI Trends Are Most People Still Missing?
Most people might miss these six underrated AI trends if they don’t pay attention in 2026:
Claude Code as a general automation tool (not just for developers),
domain expertise beating tool chasing,
knowing when NOT to use AI,
turning internal systems into products,
the boring foundation work that makes AI agents work well, and
compliance and governance becoming competitive advantage
Let’s deep dive into each of these sleepers one by one.
Sleeper #1: Claude Code (Mentioned Three Times)
Here’s something rare: three creators named the same tool as their sleeper pick. In a group of 17, that matters.
Claude Code is Anthropic’s command-line tool for their Claude AI. It looks like a developer tool running on your terminal. But it is just a way to talk to AI through terminal commands instead of chat window.
But something interesting is happening. Creators who use it see uses beyond coding.
Personally for me, working with Claude Code has become as normal as having my morning tea. I have not just built but solved many non-code problems in my day to day life for efficiency gains. But more on those in time to come. So, back to topic.
The “sleeper” label matters. Claude Code isn’t unknown. So many developers use it daily these days. But its power as a general automation tool for non-developers is underpriced. When three people point to the same thing, the market hasn’t caught on yet.
Ilia Karelin highlights:
“Claude Code’s power, especially combined with IDEs, web, Chrome, making strong tool combos solving varied problems”
He expects more attention in 2026 as people see what happens when Claude Code links with their tools.
Wyndo, a power user of Claude Code himself, suggest:
“Claude Code use and voice transcription tools; the future is talking to AI while agents work, not typing.”
Wyndo pairs Claude Code with voice tools like Wisperflow. His vision is multimodal talking to AI while agents work behind the scenes, removing typing limits.
I personally followed this advice from him a while back and literally things have multi-folded for me since then when working with Claude code.
Why it matters:
When three people name the same sleeper pick, listen. Claude Code is seen as “AI for developers,” but its power as a general automation tool is underused.
If you want to start, I wrote a beginner’s guide to building custom AI assistants that covers basics. Those who master Claude Code + MCP (Model Context Protocol) early in 2026 will have big advantages.
Just check this video on how Claude Code + Chrome can help not just developers but soloprenuers looking to solve productivity challenges.
Sleeper #2: Domain Expertise Beats Tool Chasing
There’s a hidden truth: Your existing expertise matters more than which AI tools you learn.
Everyone rushes to learn the latest AI tools or models. New models come monthly nowadays. Tutorials flood YouTube. The urge is to chase every new thing. But three creators bet the opposite that your domain knowledge, not tool skills, is your unfair edge.
This matters because tool knowledge becomes common fast. Everyone will know the same AI tools soon. But your years in a field? Your understanding of problems no AI was trained on? That’s a moat AI can’t cross.
Jenny Ouyang says:
“Using your domain expertise as an edge; standing out comes from solving unique problems, not from AI tools everyone learns.”
Jenny’s point: “Your years in finance. Your niche market knowledge... Your domain knowledge no AI has.” While tool-chasers make the same outputs, domain experts create unique value.
Matt Giaro points out:
“Knowing enough to guide AI well, not chasing every new tool.”
Matt calls this “AI-steering skills” foundational understanding that lets you guide AI without mastering every new release. The goal isn’t knowing all tool features. It’s knowing enough to direct AI toward useful results.
James Presbitero mentions:
“Non-technical AI skills viz. translating problems, knowing AI limits, maintaining network and meaningful relationships.”
James points to skills that boost AI results: “translating what AI can solve,” knowing where “AI stops,” and “keeping relationships.” Tools alone aren’t enough; these skills multiply results.
The summary: Everyone learns the same AI tools. Your unique domain knowledge is your edge. The accountant who knows AI beats the AI fan trying to learn accounting.
Sleeper #3: Knowing When NOT to Use AI
The race to adopt AI is loud. Tutorials, tools, new models every month. But what about knowing when to step back? When to say “not here”? That judgment is harder to teach and more valuable.
Sam Illingworth doubles down:
“Personal AI literacy as a civic skill. Knowing when to refuse AI, limit it, and explain your choices.”
This isn’t anti-tech. It’s smart. Sam says people who “explain when and why they didn’t use AI” get an edge. They can justify choices instead of just automating.
Joel Salinas adds:
“Prompt engineering will fade as interfaces go beyond text.”
As AI moves to voice, vision, and gesture, typing perfect prompts matters less. The skill shifts from crafting prompts to knowing when AI adds value and when it doesn’t.
The summary: The skill in 2026 isn’t better prompting—it’s better judgment on when AI helps and when it hurts. As interfaces improve, prompting matters less. Judgment always matters.
Sleeper #4: Turning Internal Systems Into Products
Every solopreneur builds systems. Notion databases, AI automations, content flows. Most keep these private. But what if your system is the product?
This is different from “selling what you know.” It’s “selling how you work.” It scales differently than consulting or courses.
Anfernee explains:
“Turning internal systems into sellable assets; packaging Notion systems into repeatable solutions others can use.”
Most use Notion and AI just for themselves. The chance is “packaging those systems into repeatable AI-assisted solutions” for others.
Carrie Loranger take on it:
“Niche AI middleware businesses where non-coders packaging tools into ready-to-use solutions for clinics, gyms, coaching, and more.”
Carrie sees small operators as “translators” between generic AI tools and specific workflows. The chance? Fill “the big gap between what tools do and what small businesses can use easily.”
The summary: The systems you build for yourself have value to others in your niche. 2026 is when more creators sell the system, not just the results.
Sleeper #5: The Boring Foundation Work
Most people want flashy AI demos but skip the basics. Process docs. Clear goals. Feedback loops. Checkpoints. This work isn’t exciting but separates useful AI from expensive toys.
Creators who do this boring work first will win when agents arrive because agents need the clarity most avoid.
Three creators bet on the ‘boring work’ that makes everything else work.
Daria Cupareanu recommends focusing on:
“The boring foundation work which includes mapping processes, inputs/outputs, setting goals, building feedback that most struggle with but agents need.”
She says that the people who pull ahead will be the ones who learn to manage well. To think in systems, to delegate clearly, to build feedback loops. In a way, preparing for the next wave of AI means getting better at the very human skills we already struggle with.
Anna Levitt says:
“Companies building internal ‘universities’ to retrain workers, making career changes normal at any age.”
She imagines “a 45-year-old intern becoming normal, not rare.” The foundation work is about human systems that adapt.
Nick Quick shares:
“Showing messy creative work instead of polished results will be valuable.”
This links back to human proof-of-work. The “messy process” is foundation work shown and it’s becoming a sign of value.
The summary: The flashy AI demo hides the boring work. Process docs. Clear goals. Feedback loops. Creators who do this groundwork first win with agents.
Sleeper #6: Compliance Becomes Competitive Advantage
Everyone chases what AI can do. Almost no one prepares for what AI can break.
More adoption means more surface area for problems. Think about ethical violations, governance failures, bad data flowing through systems. These aren’t edge cases anymore. They’re structural risks that companies will face real liability for.
Raghav Mehra sees this gap:
“Compliance, governance, guardrails, and law. This is the unsexy answer, but it’s where real value concentration happens in 2026.”
Raghav’s point is sharp: people obsess over capability and ignore liability. They treat compliance as overhead instead of advantage.
The ones who flip that script early such as compliance specialists, governance architects, AI ethics practitioners, etc. will become non-negotiable hires, not nice-to-haves.
The summary: While everyone races to build with AI, the winners also build guardrails around it. Compliance isn’t the boring cousin of innovation—it’s the moat.
Other Notable Sleepers Worth Mentioning
Some contributors named other sleepers:
Voice-first AI over text - Wyndo feels tools like Wisperflow point to a future where typing isn’t needed. I personally use this all the time.
Adaptability as a key skill - Mia Kiraki 🎭 says keeping your ground as tech changes will help in adapting faster.
Small, ethical desktop AI models - Karen Spinner thinks there’s a sizable untapped market for small, ethically built models that can run on the desktop. If they can be made user-friendly then it could be big.
Using AI to build unique voice, not just content - AI Meets Girlboss says moving from mass content generation to voice building will set creators apart.
Why Does More AI Mean More Human Value?
More AI means more human value because AI handles routine work while humans focus on premium work. When AI makes production cheap, judgment becomes valuable.
The creators most excited about AI bet hardest on human parts like taste, relationships, original thinking, and smart decisions. AI clears repetitive tasks so humans can focus on what only they can do: creative direction, quality, and real connections.
Here’s what surprised me most
The creators most excited about AI also bet hardest on human parts. Sam Illingworth wants “deliberate friction” to slow AI. Nick Quick bets on “human proof-of-work.” James Presbitero wants to be “unpromptable.”
At first, this seems odd. Isn’t AI meant to remove human friction?
But there’s no conflict once you see the pattern:
AI handles routine work. Research, drafts, formatting, distribution, repetitive tasks.
Humans handle premium work. Judgment, taste, relationships, original thinking, smart choices.The creators betting big on AI aren’t replacing themselves. They’re lifting themselves. They are using AI to clear routine work so they can focus on what only humans can do.
This is the main idea across all 17 predictions. Whether it’s agents running operations (Carrie), voice replacing typing (Wyndo), or vibe coding letting anyone build (Jenny), the goal is:
Free human attention for human-premium work.
Carrie’s “orchestrated AI agents” don’t replace her judgment. They free her to use it on harder problems.
Sam’s “deliberate friction” isn’t anti-AI. It’s pro-judgment. AI should speed work, not skip thinking.
The risk of missing this:
You automate the wrong things. You make more content instead of better content. You chase volume when the market wants taste.
The chance if you get it right:
You work at a scale never possible before while raising the human quality of your work. Not by working harder. By using AI on the right problems.
What’s My Take For 2026?
I believe that 2026 will reward builders over users. People who create AI systems, not just use AI tools. The human stays the creative director while AI handles assembly line work. People who can think, articulate, and map processes first before automation or AI enablement.
Why?
Because I have felt the burnout of content treadmill Nick Quick talks about. Over the last decade, I had scaled my travel blog and its YouTube channel (~60,000 subscribers). However, it was only passion that kept me going even though there was always burnout in it. I always played a catchup game with it.
Being a serious content creator or running a soloprenuer business while having full-time job doesn’t work unless something changes. AI tools help a bit. But, building scalable, repeatable systems is the key to not burnout.
Here’s what I learned and practice that fits these predictions:
The human is the creative director.
Every creator who mentioned AI agents said human checks matter. That matches me. My system makes many decisions alone. But I set limits, review results, and make key calls. AI handles 70% of the process. I handle 30% of taste.
Clear processes come before automation.
Daria called this “boring foundation work.” She’s right. Before automating my workflows with n8n and Claude Code, I would write down every step and map the process. I practice this since I was an intern software developer, so this comes easy to me. But for many, it does not.
I always appreciate that AI can’t judge what I haven’t explained. Most want to skip to the building automation. However, the real power is in the clarity and the foundation.
Claude Code, Claude Skills, n8n, and MCP forms my system’s base layer.
Three creators named Claude Code as a sleeper. I’m the right there with them, and I’ll go further:
When you mix Claude Code, Claude Skills, n8n, and MCP servers linking your tools like Notion, Firecrawl, Google Drive, etc.. you don’t just prompt. You orchestrate.
Most see Claude Code as “AI for developers.” I see it as the system for anyone who wants to build anything without coding including AI workflows. The interface is a terminal, but the power is orchestration.
Recently, I vibe-coded a native macOS Backup App in 2 Hours with Claude Code and I'm not a Swift Developer. Karen Spinner and Raghav Mehra have so many other use cases documented in their Substacks beyond AI tools. Go explore them
My holistic Content OS system uses Claude Code to connect research tools, writing helpers, SEO / AEO analyzers, and publishing platforms. Each part is simple. The magic is in the orchestration.
The chance is closing.
Pinkie said early users get an edge while “rules are loose.” That window is closing.
Creators who build these systems early in 2026 won’t just save time, they’ll work at a new scale. By late 2026, I believe many of these patterns will be set. Early movers will have growing advantages.
What does this mean?
2026 will reward system builders over general users. The difference matters. Using ChatGPT or Claude in silos makes you a faster, surely. Building layered AI-powered systems that runs your whole business as a soloprenuer makes you different.
I’m betting my business on this. Not because I’m sure but I am open and vulnerable. Predictions are hard, they may vanish. But the downside for me is learning skills that will help me anywhere. The upside is working leverage that grows.
The creators in this list each see part of the puzzle. The human advantage. Agent use. Vibe coding. AI literacy. Platform changes. Foundation but boring work.
What Should You Do Next With These AI Predictions?
Start by finding your biggest pain point, then match it to one prediction here.
Try one small test this week:
automate one task,
create content by voice,
build a simple workflow, or
find what you do that AI can’t copy.
Focus on action, not just reading. The winners in 2026 aren’t those who read most, they’re those who try and improve fastest.
Don’t try all predictions here. Answer these three questions:
Question 1: What’s your biggest pain right now?
Be clear. Not “content creation.” But:
“reformatting my YouTube video into blog posts, social clips, and newsletter parts takes 4 hours per video.”Not “client work.” But:
“sending project updates to 15 clients every Friday takes 90 minutes.”Question 2: Which prediction helps with that pain?
Content reformatting → Multi-modal AI, voice-first workflows
Repetitive tasks → Agentic AI systems
Building without coding → Vibe coding, Claude Code, n8n masterclass
Standing out → Human advantage, unpromptable skills
Too many tools → Domain expertise over tool chasing
Question 3: What’s your first test?
Not full setup. One small try:
If betting on AI agents: Automate ONE repetitive task this week
If betting on voice: Make ONE piece of content by speaking
If betting on Claude Code: Build ONE tiny app that solves your day to day problem or ask it to organize and clean up your downloads folder (I just did that)
If betting on n8n: Build ONE small automation workflow connecting two tools
If betting on human advantage: Find ONE thing you do that AI can’t copy
What’s Your Bet?
Now I want to hear yours.
Drop a comment: What’s your biggest bet for 2026? What do you think most people miss?
The best answers might be in a follow-up.
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Amazing list of creators. Thank you for the opportunity Dheeraj. Love these kind of posts with multiple amazing creators in the same list!
I love this collaboration that highlights everyone’s predictions and shows how they converge on the same themes. It's true that 2026 will be quite different.
Thanks for inviting me!