They read about new models. They save prompt threads. They watch demos. They say things like, "I should really use AI more for SEO or any other work they do."

Then, when real work shows up, they go back to the old way. Here, I'm going to consider SEO as an example.

They write the meta descriptions from scratch. They build the keyword cluster in a spreadsheet, one row at a time. They open 10 tabs to research a competitor's backlink profile.

They rewrite the H2s line by line. They stare at a 4,000-word page that won't rank and think, "I'll audit this later."

That is the actual AI adoption problem. It is not that SEOs need another 47-page prompt guide.

It’s not that they lack curiosity-it’s that their work habits move faster than it.

So here is the simplest rule I know:

Before you do a task by hand, let AI take the first cut

Yes, you heard right, its Not the final cut. The first cut.

AI does not need to finish the job perfectly to be useful. A power saw does not build the cabinet for you either. But it can rough out the keyword map, expose the thin sections of a page, or draft the schema in terms of AI in SEO.  Ultimately, it saves your best energy for the parts that need judgment, like deciding which intent actually matches the SERP.

You don't become AI-fluent by reading about the workshop. You become AI-fluent by working in it.

The Workshop Rule: Give AI the First Cut

Imagine your daily SEO activities as a workshop.

Every task is a piece of raw material.

• Simple pieces: title tags, meta descriptions, alt text, FAQ schema.
• Awkward pieces: topic clusters, internal linking maps, content briefs for a niche you don't know well.
• Expensive-to-ruin pieces: a homepage rewrite, a migration redirect map, a YMYL article, a pillar page that drives 30% of your organic revenue.

AI is one tool on the bench. Not the whole workshop. In SEO terms, AI is not the strategist. Not the owner of the final ranking decision.

Before you grab the hand saw, ask:

• Could AI draft the first content brief from the top 10 SERP results?
• Could AI cluster 800 keywords into topic groups?
• Could AI find the thin sections in this 3,000-word post?
• Could AI compare my page against three competitors?
• Could AI turn this technical audit into a prioritized checklist?
• Could AI pressure-test whether my target keyword actually matches search intent?

Sometimes the answer will be no. Fine. But if you never try, you never build the judgment to know where AI helps and where it does not.

That judgment is the real skill.

Stage 1: Set Up the Bench Before You Start Cutting

A lot of SEOs blame AI when the output is bad. Sometimes that is fair. But often, the problem is the bench.

If you ask, "Write a blog post about email marketing," you will get generic, AI-flavored slope content that ranks for nothing.

i. Create Your Default Workshop Settings

Use custom instructions. Tell the AI once:

I work on B2B SaaS SEO. Target audience is marketing managers at 50–500 person companies. We optimize for bottom-funnel commercial intent keywords. Avoid generic "in today's digital landscape" intros. Never invent statistics. If a claim needs a citation, flag it. Match the tone of First Round Review, not HubSpot.

That is the equivalent of aligning the blade before the cut. It does not guarantee perfect work, but it makes bad work less likely.

ii. Build a Job Ticket for Repeated Tasks [Skills]

For any recurring SEO task - content briefs, on-page audits, meta tag rewrites - create a reusable context packet & save it as skills:

• Goal: Rank in top 3 for "[keyword]" within 90 days.
• Audience: Mid-market IT directors evaluating vendors.
• Context: Top 3 competitors are [URLs]. Our unique angle is [X].
• Inputs: Existing page, target keyword, SERP screenshot, our internal product docs.
• Constraints: No fluff intros. No unsupported stats. Must include a comparison table.
• Output: H1, H2 outline, word count target, internal link suggestions, FAQ section.
• Success standard: A junior writer could execute this brief without asking questions.

Then add this line:

Before drafting, restate the assignment and flag any missing information or risky assumptions.

That single instruction catches the crooked measurement before the cut.

iii. Improve the Jig, Not Just the Output

When your "write a meta description" prompt keeps producing 175-character descriptions that ignore the modifier, don't fix the output and move on.

Fix the jig:

Here's a prompt I use for meta descriptions. The output keeps exceeding 155 characters and burying the primary keyword. Analyze what's missing, ask clarifying questions, then rewrite this into a stronger reusable prompt with stricter constraints and a self-check step.

A reusable prompt is a shop-made jig. You refine it because you'll use it again next week.

Stage 2: Use AI as the Shop Foreman, Not Just the Saw

i. Use AI for a Site Survey Before a Pitch

Don't ask: "Summarize this company." That produces slope.

Ask for a site survey:

I'm pitching SEO services to this company. Review their website, blog, and visible SEO setup. What do they sell? Who do they target? What's their current content strategy look like? What technical SEO issues are visible from the homepage? What keywords are they likely targeting but missing? What should I ask that's not obvious?

A summary gives you facts. A site survey prepares you to walk in with better questions.

ii. Let AI Run the Punch List on Your Page

Before publishing a pillar page:

Here's a draft article targeting "[keyword]." Create a punch list from five perspectives: a skeptical buyer, a Google quality rater, a competitor's SEO, a subject-matter expert, and a first-time visitor. What would each person question, dislike, or need more proof for?

You hear the objections privately first. That gives you time to fix the page before the SERP does it for you.

iii. Inspect the Page, Don't "Make It Better"

"Make this better" is vague. Try this instead:

Review this landing page like a skeptical buyer comparing three vendors. Create a punch list of fuzzy claims, unsupported statements, weak calls to action, and sections that could easily be mistaken for a competitor’s. Then rewrite the three weakest sections with stronger, more specific copy.

That's a real assignment, not a wish.

Stage 3: Make Test Cuts Before Choosing Your Default Tool

You wouldn't run your most important client's content brief through a brand-new prompt without testing it first.

Take one real task - say, generating content briefs from a SERP - and run it through 3 setups:

1. ChatGPT with your standard prompt
2. Claude with the same prompt
3. A specialized SEO tool (Frase, SurferSEO, etc.)

Score each output on:

• Accuracy of SERP analysis
• Specificity of subheadings
• Quality of suggested entities and related terms
• Editing time required
• Hallucinated facts or fake sources

Then ask:

Compare these three briefs against the scorecard. Which is strongest? Where is each weak? Which setup should become the default for content briefs in our workflow?

You stop guessing. You turn preference into evidence.

Stage 4: Keep a Maintenance Log

After a useful AI run, don't just paste the output and close the tab.

Before we finish, review this content brief workflow. What information was missing? Where was the output likely weak? What should I add to the prompt or context packet before I run this again next week?

Every use becomes a small improvement. The workflow compounds.

Stage 5: Draw the Blueprint for Messy Work

Take 1 messy SEO process and ask AI to map it:

Here's how we currently produce a piece of SEO content from keyword to publish. Turn this into an operating map. Identify inputs, steps, tools, decisions, handoffs, failure points, and quality checks. Then label each step as human-owned, AI-assisted, automatable, or deterministic.

You'll see something like:

• Keyword research → AI-assisted (clustering) + human-owned (priority call)
• SERP analysis → AI-assisted (extraction) + human-owned (intent judgment)
• Content brief → AI-assisted (draft) + human-owned (angle and proof)
• First draft → AI-assisted (sections) + human-owned (expertise and examples)
• Internal linking → automatable (with rules)
• Schema markup → deterministic (template)
• Editorial review → human-owned
• Indexing check → deterministic (Search Console API)

This prevents two bad extremes: trying to automate everything, or assuming AI can help with nothing. Most real SEO workflows sit in the middle. We are doing it to keep human in loop, which is very important.

The 60/30/10 Workshop Rule

• 60% production: Run the workflows that already work - meta descriptions, FAQ schema, content briefs, alt text at scale.
• 30% improvement: Sharpen the prompts, add examples, build better scorecards, refine the jigs.
• 10% experimentation: Try the weird stuff - AI-generated programmatic SEO pages, automated internal link suggestions, agentic SERP monitoring.

Make sure you are not violating Google Search Ranking Policies by creating scaled content abuse.

Too much experimentation creates chaos. Too much production creates complacency. The mix matters.

Keep a "Not Yet" Shelf

When AI fails at something important - say, generating a technical SEO audit that actually catches JavaScript rendering issues - don't conclude "AI can't do this."

Write down:

• What you tried
• What failed
• What context was missing
• What would need to improve
• When to retry

Revisit in 3 to 6 months. The phrase "AI couldn't do it" should almost always end with "yet." I personally observed that all next frontier SOTA  AI models will solve most of your earlier issues.

The Real Skill Is Knowing Which Tool to Pick Up

The most AI-fluent SEOs are not the ones with the fanciest prompts. They are the ones who can look at a task and quickly decide:

• AI should draft this meta description.
• AI should critique this H1.
• AI should summarize these 12 competitor pages.
• AI should not write the original case study.
• AI can map this content cluster, but a human picks priorities.
• AI can suggest internal links, but we verify relevance.
• AI can make the first cut on the brief, but I finish the angle.

That is a work judgment. It only comes from action and repetition.

I’m personally a big fan of technology. We should all start using it like a baby you watch grow, learning, improving, and gaining new abilities over time.

AI agents are going to amplify what people in the SEO industry can do, and WebMCP could be the next opportunity to catch.

A Simple Exercise for Today

If you really want to cross-check your understanding, explain what you understood from all of my analogies and suggestions.

Pick one SEO task you were going to do manually:

• A meta description batch
• A content brief
• A competitor analysis
• A thin-content audit
• A keyword cluster
• An FAQ section

Pick any of your favourite AI tool. Give it the job ticket. Ask for the first cut. Then ask:

• What's useful?
• What's wrong?
• What did I forget to tell it?
• Should I save this prompt as a reusable jig?

That's one rep. Do it enough times and AI stops feeling like a novelty. It becomes part of the bench.

Repetition is the mother of invention. That mindset is what Thomas Edison believed in- and it’s how we ended up with the light bulb.

Final Takeaway

I think the SEOs who win with AI are not waiting for perfect training. They are not memorizing giant prompt libraries. They are not treating AI like a magic button.

They are treating it like a power tool.

They set up the bench. They create job tickets. They make test cuts. They inspect the output. They improve the jig. They keep a maintenance log. They build a better workshop one workflow at a time.

So next time you sit down to write a meta description, build a content brief, or audit a page the old way, pause.

Before you pick up the hand saw, ask: Can AI make the first cut?

If yes, let it. Then use your judgment to finish the work. I'm happy to know your case studies & experiences of using AI and agents.

I didn’t recognize the name. We were three weeks into automating a mid-sized D2C brand’s online store - working on the Shopify backend, a custom checkout, and code connecting their product system with order fulfillment.

The AI had been writing most of those integrations all morning. That’s pretty much how our teams work now.

Prompt->generate-> install->ship.

The package looked legitimate. It was published under @tallyui, the versioning looked normal, and the npm README was polished and convincing. My developer was just about to merge it.

I asked him to wait 2 min while I checked something.

That 2 min is the only reason I'm writing this post instead of rotating every credential my team has ever touched.

What Crawled Into Our Codebase

On May 11, 2026, a hacking group brought back a malware campaign known as Mini Shai-Hulud. Between 7 and 8 PM UTC, they uploaded 373 malicious versions across 169 npm packages. The security team at Aikido Security found that several popular package scopes were affected, including @tanstack/, @uipath/, @mistralai/, @squawk/, @draftlab/, @beproduct/, and - the one that immediately caught my attention - @tallyui/, including these four packages

• @tallyui/connector-shopify
• @tallyui/connector-woocommerce
• @tallyui/connector-medusa
• @tallyui/pos

Yes - Shopify, WooCommerce, Medusa, and even a point-of-sale connector were affected. So if you’re building e-commerce automation tools right now, you need to be careful.  This attack was targeting exactly the kind of tech stack you’re likely using.

The malware itself is aggressive. Once installed, it searches for sensitive credentials like GitHub tokens, npm tokens, AWS keys, Kubernetes files, Vault tokens, .env files, and other secrets stored in environment variables. The most alarming part is that it can also use a stolen npm publish token to infect other packages you own, publish compromised updates under your account, and spread the malware further.

I looked at it carefully and found that it's not just an infostealer. It's a worm that turns any release pipeline into the next attack.

Why This Attack Broke Everything I Thought I Knew

I had read about npm supply chain attacks before. I thought I understood the threat model in which a bad actor compromises a maintainer's token, then publishes a poisoned version later malicious postinstall script runs. Which leads to hijacking your code.

I'm well aware about above workflow, but the TanStack wave broke that model. Here's what the compromised packages actually did, according to Aikido's analysis.

The malicious version added a file at the package root called router_init.js. Which seems harmless at first glance

Then it added what looks like a perfectly reasonable optional dependency:

"optionalDependencies": {
  "@tanstack/setup": "github:tanstack/router#79ac49eedf774dd4b0cfa308722bc463cfe5885c"
}

That dependency points to a GitHub commit on the real TanStack repo.

The trick: npm runs lifecycle scripts for Git dependencies during install, and the referenced commit contains a prepare script that does this:

"scripts": {
  "prepare": "bun run tanstack_runner.js && exit 1"
}

The && exit 1 part is the clever trick. Since it’s an optional dependency, npm ignores the failed install. But by then, the malicious code has already run and the credentials have already been stolen.

And this is the part that really shocked me, the malicious packages were actually published through TanStack’s real GitHub Actions release system using trusted OIDC publishing.

So the packages looked fully legitimate. They even had valid SLSA (Supply Chain Levels for Software Artifacts) security verification showing they came from a trusted source.

Technically, that verification was correct. The attacker didn’t steal a publish token - they took control of the release workflow while it was running.

So if you were told to “only install verified packages”  like I told my own team, you would’ve checked the package, seen the green verification badge, and trusted it enough to merge.

Where Vibe Coding Let Me Down (And I Let It)

I went back through Cursor's tool-call log. Between 9 AM and 11 AM, our agent had run npm install 11 times. Eleven. For a feature branch.

Each time, it was responding to a small prompt - "add SKU validation against the Shopify product catalog," "wire the inventory sync to the Medusa endpoint," "set up a webhook handler" - and each time it would propose a couple of packages, add them, and run install. I'd been approving the diffs without reading them carefully, because the code looked right and the tests passed.

This is the vibe coding failure mode, and I had walked straight into it.

When an AI agent installs a package because your prompt said "add a connector," nobody in the loop has reviewed the package.

The AI agent has no idea that the package maintainer’s account may have been hacked just a few hours earlier.

It also can’t tell that the README might have been carefully written to attract AI tools - something security researchers at ReversingLabs say is happening in a campaign called PromptMink. And sometimes the package name itself may not even be real.

A study from Stanford Human-Centered Artificial Intelligence found that AI-generated JavaScript code occasionally invents fake package names. Attackers take advantage of this by registering those fake names on npm and waiting for developers or AI agents to install them - a tactic researchers call “slopsquatting.

I had built a workflow that did the choosing-a-dependency part on autopilot, then told myself I was reviewing.

How We Prevented npm Supply Chain Attacks in Our Vibe Coding Project

After my dev paused and I checked the Aikido feed, here's what we did in this exact sequence.

I'm sharing it because most "incident response" posts skip the order, and the order matters.

1. Did NOT run rm -rf node_modules

This was the first thing I had to actively stop my team from doing. The Mini Shai-Hulud worm persists outside node_modules  it drops a launch agent at ~/Library/LaunchAgents/com.user.gh-token-monitor.plist on macOS and a systemd user service at ~/.config/systemd/user/gh-token-monitor.service on Linux.

It also writes payloads into .claude/ and .vscode/ that survive uninstall. Nuking node_modules would have made me feel productive while leaving the actual persistence mechanism alive. We searched for those files first.

2. Blocked the C2s at our network layer, not just on the dev machine

For this wave: *.getsession.org, the AWS metadata IP 169.254.169.254 (the payload tries to scrape EC2 instance credentials), and the npm tokens endpoint at registry.npmjs.org/-/npm/v1/tokens.

Blocking it at the firewall meant that any other machine on our network that had installed the package could no longer communicate with the attacker’s server while we investigated the issue.

3. Rotated tokens in priority order - not alphabetically

We decided to reset the npm publish tokens first because the malware uses them to spread itself to other packages.

As long as those tokens stay active, the attacker could publish infected updates using our account. After that, we rotated GitHub personal access tokens, then AWS credentials, then Vault tokens, and finally everything else.

The safest approach is to reset credentials in the same order the malware is most likely to abuse them.

4. Hunted for the worm's calling card

We searched our entire GitHub org for repositories with the description "A Mini Shai-Hulud has Appeared". The worm exfiltrates stolen data by creating public GitHub repos on the victim's own account with that exact phrase. We didn't have any. If we had, deleting them would have been step zero of damage control.

5. Audited every package version installed in the last 72 hours

We cross-referenced all our active e-commerce projects against the affected list. 3 repos had pulled @tanstack/react-router versions that weren't compromised - the bad versions were 1.169.5 and 1.169.8 specifically; we'd pulled 1.169.4.

One repo had @mistralai/mistralai in its lockfile, and I had to check three times that we were on 2.2.1 and not 2.2.2, 2.2.3, or 2.2.4.

By 2 PM I knew we'd dodged it. By 3 PM I knew exactly why, and I was angry at myself for it being luck.

What We Actually Fixed, Starting the Next Morning

Luck is not a control. Here's what we shipped that week, in order of how much it would have helped if we hadn't been lucky.

The One-Liner That Would Have Stopped Everything

npm config set min-release-age 3

This is a native npm config that refuses to install any package version published less than 72 hours ago. The Mini Shai-Hulud TanStack versions were detected within hours. The earlier Axios compromise in March was detected within 24 hours. SAP at the end of April: detected fast. PyTorch Lightning April 30: detected fast. That's why we decided to delay it till 3 days have elapsed.

I realised this single setting would have blocked all of these packages during installation. No extra scripts, CI checks, or security scanners needed. Just one line of configuration. Unfortunalty I hadn’t enabled it in any of our projects, and that was my mistake.

We’ve now added it to our default project template, with a 7-day delay for e-commerce projects since we usually don’t need the newest package versions immediately.

Lock Down npm Before It Locks You Out

Here's what lives in our .npmrc now, committed to every project root:

# Project root .npmrc — committed
min-release-age=7
ignore-scripts=true
save-exact=true
audit-level=moderate
fund=false

ignore-scripts=true is the one most teams resist because a handful of legitimate packages (sharp, bcrypt, better-sqlite3) need to compile native bindings on install.

After going through this myself, I think that mindset is completely backwards. Automatically allowing the npm packages code to run during installation is exactly what made attacks like Mini Shai-Hulud.

The axios incident, the SAP attack, and the PyTorch Lightning attack are possible during such settings. Most packages don’t actually need that level of access. And for the few that do - maybe 5 packages a year - we can manually run commands like npm rebuild sharp ourselves, so we know exactly what code is being executed and when.

save-exact=true writes "axios": "1.14.0" instead of "axios": "^1.14.0".

The caret (^) in package versions is what allows a new npm install to automatically pull in a newer patch or minor release - including a malicious one if a package gets compromised.

Drop npm for New Projects. Use pnpm.

pnpm and bun provide better dependency controls, but lifecycle scripts still need explicit hardening. They significantly reduce exposure to common postinstall -based attacks when properly configured. I wish I'd known this two years ago.

We're not rewriting existing projects, that's not free, but every new e-commerce automation we start now is pnpm, full stop.

Tell Your AI Agent What It's Not Allowed to Do

The single most important file in our repo is now .cursorrules (and CLAUDE.md for the projects on Claude Code).

Here's roughly what's in it now:

# Dependency rules — enforced

1. Before adding ANY package to package.json:
   - Run `npm view <package> version time --json`
   - Report the package's latest version, weekly downloads, and publish date
   - If the package has <1,000 weekly downloads OR was last published <7 days ago,
     STOP and ask before adding
   - If the package does not resolve on npm at all, STOP. Do not guess at a similar name.

2. Never use `npx <package>` for a package not already in package-lock.json.

3. Use `pnpm` for all installs. Never `npm install` in CI —
   use `npm ci` or `pnpm install --frozen-lockfile`.

4. When adding e-commerce connectors (Shopify, WooCommerce, Medusa, BigCommerce):
   - Only use the official packages from those vendors' verified npm scopes
   - Flag any @tallyui/*, @squawk/*, @uipath/* package as requiring manual review

5. If you are uncertain whether a package name is real, SAY SO. Do not invent.

Rule 1 catches the cooldown-window attacks.

Rule 5 prevents slopsquatting.

Rule 4 is specifically what would have caught the package my dev almost merged on Monday.

Rememeber It's not a magic shield. Agents drift from their rules under pressure. But it changed the failure mode from "agent silently installs poisoned package" to "agent visibly defies its rules," which AI will notice.

A CI ( continuous integration) Gate That Actually Catches This in Pull Requests

# .github/workflows/dependency-cooldown.yml
name: Dependency Cooldown
on:
  pull_request:
    paths: ['package.json', 'package-lock.json', 'pnpm-lock.yaml']

jobs:
  cooldown:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
        with: { fetch-depth: 0 }
      - uses: actions/setup-node@v4
        with: { node-version: 22 }

      - name: Block fresh dependencies
        run: |
          git fetch origin "${{ github.base_ref }}" --depth=1
          added=$(git diff "origin/${{ github.base_ref }}"...HEAD -- package.json \
                  | grep -E '^\+\s+"[^"]+":' \
                  | sed -E 's/^\+\s*"([^"]+)".*/\1/' \
                  | grep -v '^[a-z]*Dependencies$')

          [ -z "$added" ] && { echo "No new deps."; exit 0; }

          fail=0
          for pkg in $added; do
            data=$(npm view "$pkg" version time --json 2>/dev/null) || continue
            v=$(echo "$data" | jq -r '.version')
            t=$(echo "$data" | jq -r ".time[\"$v\"]")
            age=$(( ($(date +%s) - $(date -d "$t" +%s)) / 86400 ))

            if [ "$age" -lt 7 ]; then
              echo "::error file=package.json::$pkg@$v is $age day(s) old. Cooldown is 7 days."
              fail=1
            else
              echo "✓ $pkg@$v ($age days old)"
            fi
          done
          exit $fail

Two things this workflow gets right that most tutorial versions don't: fetch-depth: 0 so the diff actually resolves against the base branch, and it gates only on real new dependencies, not every keystroke in package.json. If you skip either, your team will disable the workflow within a sprint.

Aikido Safe Chain as a Safety Net

I'm not pitching anyone, but I'll be honest, after this incident, I installed Aikido's Safe Chain on every dev machine on my team.

It's open source. It sits in front of npm, npx, yarn, and pnpm and checks packages against their malware intel feed before the install actually happens.

If they had flagged @tallyui/connector-shopify by Monday morning, my dev would have seen the warning before he typed install. Worth setting up.

Perplexity recently open-sourced Bumblebee, a lightweight supply-chain vulnerability scanner that helps developers detect risky packages, IDE/browser extensions, & AI tool configs on developer machines . This tool is especially useful for every vibe coder to quickly audit their macOS or Windows setup for hidden supply-chain risks before they become problems.

The Uncomfortable Truth About Letting AI Pick Your Dependencies

Here's the thing I had to sit with on Monday afternoon.

The whole pitch of vibe coding and the reason my agency can take on the volume of e-commerce work that we do - is that I don't have to read every line of code or evaluate every dependency.

The agent does it. That's the productivity multiplier.

But there is no agent in the world right now that can reliably distinguish a legitimate @tallyui/connector-shopify from a worm wearing its skin.

The AI agent will install both packages with the same level of confidence, and it’ll give the same cheerful “everything worked” summary either way.

AI really does make development faster. But it’s a mistake to assume the AI understands risks, context, or trust the same way a human developer does.

The settings we added like cooldown periods, disabled install scripts, pnpm, agent rules, and CI checks - don’t really slow down AI-assisted coding.

They mainly slow down fully unattended AI coding. And that difference matters a lot now.

The real issue isn’t “how do we make the AI smarter.” The better question is: “how do we limit the damage when the AI makes a bad decision or gets tricked".

If You Read Nothing Else, Do This Today

The most expensive thing in a vibe coding workflow isn't the time you spend reviewing a package. It's the assumption that the agent already did.

npm config set min-release-age 3

Set it. Today. Even if you do nothing else this week.

I personally recommend that you consider vibe coded code as your review draft, not the final code to be shipped.  Please do share your experience if you & your team also get scared with such attacks & questioning the coding safety.

I learned a lot of happenings in entrprise AI space. Some are good, some are challenging.  After coming back, I stumbled across something last week that's been keeping me up at night, and I need to share it with you all. We have a problem. And the scary part? We don't even know it's there.

What Happened Last Thursday

Last Thursday at 3:47 PM, I watched Somesh from my team use Copilot to generate our new discount calculation module. The code was beautiful - clean, well-structured, perfectly formatted. Tests passed.

I reviewed it myself, saw nothing obviously wrong, and clicked approve. This Monday, my client's finance called. We've overspent on discounts by 22% for the entire quarter. That's $xxx,00. Every single purchase for three months triggered the wrong discount logic. Here's what bothers me most: nobody screwed up.

The pipeline worked exactly as designed. Somesh didn't make a mistake. I didn't miss anything obvious in the review. The AI-generated working code was behaving as expected during tests. But none of us actually understood what it did.

I'm Calling It "Epistemic Debt" (Bear With Me)

Well, we all know about technical debt, right? That's when we(dev) consciously cut corners to ship code faster. In our case we know we're doing it, we understand and we plan to fix it later. This is different. This is unconscious. It's the growing gap between the code our AI tools generate and what we actually comprehend.

Technical debt makes code hard to change. Epistemic debt makes code dangerous to change.

PS: I'm borrowing the term "epistemic debt" from a paper I read, and it fits perfectly. Epistemic means "relating to knowledge." So epistemic debt is knowledge debt - the debt of not knowing what's in our own codebase.

This leads me to understand the importance of AI-generated code review in our development cycle.  Avoiding it may break the entire system. This is what happened in our case, which resulted in my client's budget being overspent.

Then I realize the way we are hearing about the next. The SOTA model is becoming increasingly powerful, but it is also introducing some new blunders due to the current design & architecture faults of LLM systems. It looks fancy at the frontend, but in reality, it may go wrong. Let me explain the real flaw in the current LLM system.

The Highway at Rush Hour

Think about our codebase like a highway system. Every day, AI tools add hundreds of cars (lines of code) to our highway. Meanwhile, we've got maybe two inspection stations where we actually check these vehicles thoroughly. On top of that, it's raining - people leave the team, context gets lost, documentation rots.

Before we started using AI heavily, maybe 100 cars entered per day, and we could inspect 100 cars per day. Balanced. Now? We're getting 1,000 cars per day. We still inspect maybe 150 if we're lucky. The rest just... drive onto the highway uninspected. Everything looks fine until there's an accident. Then we realize half the cars on the road have faulty brakes that nobody checked. That's where we are right now. We need to reinvent our SDLC (Software Development Life Cycle) for AI.

The Four Ways We Lose Understanding

I've noticed four distinct patterns in how we lose track of our own code:

1. The Black Box Problem

I've observed that AI sometimes generates code that works, but we genuinely can't reverse-engineer why it made certain choices. I saw this in Priya’s (our Sr. Developer) authentication module last sprint. It works well, but she would have trouble explaining how the session timeout is calculated-not because she isn’t smart, but because the AI’s logic isn’t clear.

2. The "Tests Pass" Trap

In another scenario, I asked the AI to add “no refunds on sale items.” It wrote code that blocks refunds for items under $20 and even added tests to prove it worked. The tests passed, but the logic was wrong—sale items are based on a “seasonal clearance” tag, not price. The AI misunderstood the rule, tested its own mistake, and we shipped it anyway.

3. The Missing Context Problem

Our team has unwritten rules, like never emailing customers on weekends or always logging before external API calls. We learn these from working together and from past mistakes. The AI doesn’t know these shared habits, so it can write code that works but breaks rules we never wrote down.

4. The Memory Fade Problem

In another case, Robert (Test lead on our team) reviewed the payment processing code six months ago. He understood it. He left the company last month. Now there's a bug, and literally nobody on the team can explain why certain decisions were made. The understanding walked out the door with Robert. This is an alarming concern I've found within my team, as it's difficult for a replacement to understand it. The same is happening with AI-generated code.

Real Stories That Should Scare Us

Let me share a few more incidents from other teams that mirror risks I see in our own codebase:

1. The Package Name issue

In another incident, a developer asked the AI for colored terminal output. The AI suggested installing colourama. The code worked and tests passed. Three weeks later, a security audit found a problem. The real package is colorama. Someone had made colourama as a fake, malicious package to fool AI tools.

It added colors-but also secretly stole environment variables like database passwords and API keys. It sounds extreme, but it happened with one of our modules. Thats why you must take care of your dependencies. How many did you actually check yourself instead of trusting the AI?

2. The Missing Size Limit

In another case, AI generated a file upload feature with tests that confirmed files could be uploaded and downloaded correctly. Everything passed. Later, someone uploaded a huge file again and again, filling up disk space and taking the service down. The tests proved the feature worked, but never checked size limits or abuse prevention.

3. The Architecture Nobody Noticed

In another case clients company had a rule- microservices must use a message queue, not direct HTTP. Then the team used AI to build services over four sprints. Each service worked on its own, and all tests passed.

Then one service crashed, and six others crashed with it. The investigation found 14 rule breaks because services were calling each other directly over HTTP. No one noticed because we tested each service alone, not the whole system together. That's why the orchestration part is missing with AI-generated code.

Why Our Current SDLC (Software Development Lifecycle) Process Isn't Enough for AI-generated code?

I can already hear some of you thinking that all of the above issues I highlighted are "just bad engineering" or "better code reviews would catch this,"  but let me push back on that.

• We trust that good engineers always understand their code

Yes - this is only applicable for the code they wrote. Writing forces comprehension. You can't write something without understanding it at some level. But reviewing AI-generated code? I think that's reading, not writing. Even if you are an excellent engineer & still do not fully understand code you're reading in a 20-minute PR review, especially when you're looking at 400 lines generated in 10 minutes.

• We think Better Reviews Would Fix This

Maybe. But here's the problem: we're generating code 10x faster than before because of Gen AI. If we do proportionally thorough reviews, we become the bottleneck. Nowadays, leadership wants velocity. Product wants features.

We feel pressure to keep the pipeline moving."Thoroughly review all AI code" loses to "ship the feature" every single time, even when we have good intentions. This is another difficult scenario I observe in our new way of AI-assisted coding.

• We trust that We Already Do Code Reviews

I realized now that our code review process was designed for human-written code. Humans make predictable mistakes like typos, off-by-one errors, and null pointer exceptions.

AI makes different mistakes like plausible-but-wrong implementations, hallucinated dependencies, and subtle business logic misalignments. Our review checklist doesn't catch AI-specific failure modes because we built it for human failure modes. That's why we required the restructuring of our existing process to comply with AI code.

The Speed Mismatch That's Killing Us

Well we all know that as AI systems improve with newer, more advanced models, they can generate more code faster and with greater capability.

Let me put some numbers to this.

Before heavy AI usage:

• Average developer: 150 lines of production code per day
• Average review capacity: 150 lines per day
• System balanced

Current state:

• Developer with AI Copilot: 800+ lines per day
• Review capacity: still ~150 lines per day
• System drowning

We've made the big highway 5x wider but kept the checkpoint the same size. And we wonder why the cars are flooding on the way.

I also want to distinguish between two completely different things we keep confusing while using AI-assisted development :

AI Output Trust = "How often does the AI write correct code?"
This is what the AI companies advertise. "Our model is 94% accurate on benchmarks!"

System Trust = "Can our team explain, predict, and safely modify this system?"
This is what actually matters for our jobs. You can have high AI accuracy and low system trust simultaneously. In fact, that's the danger zone.

What I Think We Should Actually Do

I don't have all the answers, but here are some concrete things I want you to try:

1. Start Tagging AI-Generated Code

Simple rule: every AI-generated block gets tagged with metadata.

python

# AI-GENERATED: Copilot, 2026-01-15, Miguel
# REVIEW-TIME: 25 minutes
# HUMAN-VERIFIED: Yes, tested edge cases manually
def calculate_shipping_cost(weight, destination):
# ... code here

Why? When there's a bug six months from now, we'll know this section needs extra scrutiny. It's like putting allergen warnings on food - just basic safety labeling.

2. Create a "Critical Path" Review Process

Not all code is equally important. Let's be honest about that. Critical code (payment, auth, user data, financial calculations):

• Requires human-written tests
• Requires architectural review
• Requires documentation explaining WHY not just WHAT
• Gets 2x the normal review time

Standard code (UI components, utility functions):

• Normal review process

This means some things ship slower. I think that's okay if it means we avoid another $xxxK mistake.

3. Set Understanding Budgets

Here's a radical idea like what if we treated comprehension like a finite resource?

Proposed policy:

• Maximum 40% of any feature can be AI-generated without deep review
• If we hit that limit, we pause feature work for a "comprehension sprint"
• One week per quarter dedicated to understanding existing code, not writing new code

I know this will slow us down. But so will the next production incident caused by code nobody understands.

4. Change Our Review Questions

During PR reviews, I want us to stop asking:

• "Does this code work?"
• "Do the tests pass?"

Start asking:

• "Can I explain this to a new team member?"
• "Do I know why it's implemented this way?"
• "Could I debug this at 2 AM if it breaks?"
• "What would happen if I changed line 47?"

If you can't confidently answer yes to these, the PR isn't ready regardless of whether tests are green.

5. Use AI to Check AI

We should use AI tools not just to generate code but to verify it.

Workflow I want to try:

1. Copilot generates authentication code
2. We ask ChatGPT: "What security vulnerabilities might this have?"
3. We ask Claude: "Generate attack scenarios"
4. We compare outputs and see where different models disagree

Different AI models have different blind spots. Let them check each other's work.

6. Build a Comprehension Dashboard

I want us to track metrics we currently ignore:

• Percentage of codebase that's AI-generated
• Average review depth (time per line)
• Number of people who understand each critical module
• Time since last deep review of each component
• Documentation coverage

Then set thresholds:

•  "Payment module: only 1 person understands this"
•  "Auth service: AI-generated 8 months ago, zero reviews since"
•  "Discount logic: 0 people can explain the algorithm"

Yeah, this feels like surveillance. But think of it as safety infrastructure, like monitoring disk space or memory usage.

Why This Conversation Is Uncomfortable

You may be thinking, what the hell is this guy trying to tell us. I get it. Talking about epistemic debt feels different than talking about technical debt.

Technical debt = "The code has problems" (external, blameless)

Epistemic debt = "I don't understand my own code" (internal, feels like failure)

Right now, nobody wants to admit they don't understand something. It feels like admitting incompetence. But here's what I need everyone to hear: This isn't about individual skill. Our AI tools got 10x faster. Our brains didn't. That's a systems problem, not a your problem.

I personally don't understand large portions of our codebase anymore. I'm admitting that openly. Not because I'm bad at my job, but because the tools are producing code faster than any human can thoroughly comprehend it. If we can't talk about this without feeling defensive, we can't solve it, guys.

The Part Where I Get Real With You

Look, I love these AI tools. I use them every day. My productivity has skyrocketed. I'm not suggesting we throw them away. But I watched my team ship code that cost nearly a million dollars because nobody actually understood what the AI generated.

I've seen my team introduce security vulnerabilities because they trusted passing tests instead of understanding the implementation. I've watched team members leave and take critical knowledge with them because we never forced ourselves to document AI-generated code.

We're building faster than ever. That's great. But we're also accumulating a debt that doesn't show up in any dashboard, doesn't trigger any alerts, and won't become obvious until something breaks badly.

Three months from now, when you can't figure out why the new feature is causing memory leaks, or why changing one config breaks three unrelated services, or why our security audit finds vulnerabilities in code that passed all our tests - you are going to wish we had this conversation sooner.

What I'm Asking From Each of You

Starting next sprint, I want you to try this:

For You as Individual Contributors:

• Tag your AI-generated code honestly
• Spend an extra 10 minutes really understanding what the AI wrote before submitting PR
• Ask "do I understand this?" not just "does this work?"
• Speak up in reviews when you don't understand something

For Our Team Leads:

• Create the "critical path" review tier
• Protect time for comprehension sprints
• Make it safe to say "I don't understand this yet"
• Track comprehension metrics alongside velocity metrics

For All of Us:

• Stop treating passing tests as proof of understanding
• Document WHY decisions were made, not just WHAT was implemented
• Share knowledge aggressively - don't let it stay in one person's head
• Accept that some things need to ship slower to ship safer

The Bottom Line

AI coding assistants are incredible. They're also creating a new kind of risk that our current processes weren't designed to handle.

The problem: Code gets written 10x faster, but understanding doesn't keep pace.

The danger: We're building systems nobody fully understands, and calling it productivity.

The solution: Admit when we don't understand something, slow down for critical code, and treat comprehension as a first-class metric. We can't keep asking "does this code work?" and thinking that's enough. We need to start asking: "Will we still understand this code in six months? Can we safely modify it?

Do we have the knowledge to own it long-term?" If the answer is no, we shouldn't ship it. Not because it doesn't work - but because code we don't understand isn't an asset. It's debt. And unlike technical debt that shows up in our backlog, epistemic debt stays invisible until it's too late.

I want to hear your thoughts, concerns, and ideas. This affects all of us, and we need to solve it together.

I think most teachers are freaking out because most of the homework assignments could've been written by AI, and there's no way to know for sure. It's their fear that the entire grading system just became obsolete overnight.

But I think we need to stop pretending to play this cat-and-mouse game and start actually dealing with reality. Recently, during my Microsoft AI tour meetup where someone much smarter than me laid out what schools actually need to do about AI. And honestly? It made so much sense that I'm shocked more schools aren't doing it already. I am amazed by the discussion with subject matter experts who have been in edtech for a couple of decades. Let me break down what's actually going to work, because what we're doing now definitely isn't.

1. You cannot detect AI Use in Homework. Period.

I know this sucks to hear, but those AI detection tools everyone's buying? They don't work. Because new frontier models are becoming more human-centric, and their tools are trying to do reverse engineering, which is technically incorrect and biased.

In my personal opinion, they KIND OF work, sometimes, if the student just copies and pastes directly from ChatGPT without any changes. But even then, they're wrong a LOT.

Here's what I've seen happen:

In my nephew's case, his essay was flagged for using AI. But we know he definitely wrote it himself as he was engaging with our family members to write on his essay topic. Later, the teacher was convinced the detector was right. The kid was in tears. Turns out, he's just a really good writer with a formal style doesn't mean it's AI. The "AI detector" couldn't tell the difference between a well-written essay and AI output. It's not me; many AI experts have already talked about this.

Now you will ask me then how search giant Google competes with this scaled synthetic content, well, that's a different story, they have a lot of parameters to check out than these AI detectors, which helps them to partially figure out within their algorithms. This is an industry-level challenge.  Here we are talking about something else, which is the thinking capabilities of children in the age of AI.

The problem is simple: AI detectors are trying to spot patterns in writing. But good students write in patterns too. And smart students who are using AI,  know how to break those patterns with like five minutes of editing using some paraphrasing tools.

You can ask ChatGPT to "write this in a more casual style with some grammar mistakes." You can run it through a paraphraser. You can take the AI's ideas and rewrite them in your own words. You can have ChatGPT write it, then rewrite it yourself using the same structure.

There are a dozen ways around detection, and kids share these tricks with each other faster than teachers can keep up. Another way is if you feed your existing writing style [doc or pdf ] to ChatGPT or any LLM tools, it will consider a similar pattern while composing things.

Plus, the whole concept is doomed from the start.

As AI gets better at writing like humans, and humans get better at editing AI output, the line between "AI-written" and "human-written" completely disappears.

In this case, Schools are trying to hit a moving target that's actively trying to look exactly like what you're comparing it to.

So what does this mean?

We have to assume that any work done at home has used AI in some way.  Not because every kid is cheating. But because you literally cannot tell the difference anymore, and pretending you can just creates false accusations and loopholes.

2. If Homework Can't Be Trusted, Where DO You Test Kids?

Okay now you will say, so if we can't grade homework anymore, what the hell do we do?

The answer is actually pretty straightforward: Move most of the grading to in-class work.

I know, I know. Teachers are already overwhelmed. My entire family is in the teaching field.  This sounds like more work. But hear me out. It's very much essential for the future of classroom education.

Here is the idea :

• Students can use AI for homework. That's fine. Homework becomes practice and learning, not evaluation.
• But the actual grading? That happens in class, where the teacher can physically see what the student is doing.
• In-class essays. In-class problem sets. In-class presentations where they have to answer questions on the spot. They can conduct tests at desks without phones.

Why this works:

• Students are still motivated to actually learn the material because they know they'll be tested on it later without AI help.
• It's actually fairer. Right now, rich kids can pay tutors or have parents who help them. Poor kids are on their own. At least with in-class testing, everyone's on equal footing.
• It catches learning gaps early. If a kid has been relying on AI for everything and doesn't actually understand the material, you'll know immediately when they can't do it in class.

Real example of this working:

I talked to a smart math teacher in our locality who made this switch last year. Homework is now "practice". Students can use calculators, AI, YouTube tutorials, whatever. But tests are all in class, no tech allowed.

You know what happened? Kids actually started USING the AI to learn instead of just copying answers. Because they knew if they didn't understand it, they would fail the test. This way for the AI became a tutor instead of a crutch.

He also said that his class's average test scores didn't drop. Rather, it went up slightly because kids were practicing more at home without the pressure of "perfect" homework.

3. We Want Kids to Use AI... But Also Not Need It

Here's where it gets tricky. We don't want to ban AI completely. That would be stupid. It's like banning calculators-the real world uses them, so teaching kids to work without them entirely doesn't make sense.

But we also don't want kids to be completely helpless without AI.

The calculator comparison is actually a perfect example to think about it.

Please understand our Schools still teach us basic math and arithmetic. We learn to add, subtract, multiply, divide-all by hand. Even though calculators exist and are faster.

Why? Because you need to understand WHAT the calculator is doing. You need to be able to gut-check its answers. If you type something wrong and get a crazy result, then you definitely consider "wait, that doesn't make sense."

AI should work the same way.

Students should learn to write essays without AI so they understand how essays work. They should learn to solve math problems by hand so they understand the logic.

THEN, once they've got that foundation, they can use AI to speed things up, to help with brainstorming, to check their work.

But if the AI spits out something wrong (and it does this ALL THE TIME), they should be able to recognize it. This only happens by practicing more.

Why does this matter more with AI than calculators?

Calculators are pretty reliable. 2+2 always equals 4. The calculator isn't going to randomly decide 2+2=5 because it read some bad data somewhere. AI, on the other hand? AI makes up facts. Hallucinates sources. Confidently states completely wrong things. Gets confused by complex questions.

I asked ChatGPT for historical facts about a specific battle last month, and it gave me a date that was off by three years and mentioned a general who wasn't even there. Sounded completely confident about it too. If I didn't already know the actual facts, I would've believed it and put wrong information in whatever I was writing. This is another disadvantage of blindly trusting AI.

Students need to be able to catch these mistakes. And the only way to do that is to actually understand the subject well enough to spot when AI is wrong or hallucinating.

4. There's No One-Size-Fits-All Solution Here, AI Homework Helper Going to Stay

I think different subjects, different grade levels, different assignments-they all need different approaches to AI. This needs to be designed properly, like a curriculum.

Some teachers might do:

• No tools allowed (traditional test, in-class essay)
• Cheat sheets only (one page of notes you made yourself)
• Open book (use your textbook but not the internet)
• Here's an AI response, critique it (analyzing AI output for errors)
• Full access (use any resources including AI, but explain your process)

The point is, there's a lot of creative space here for teachers to design assessments that make sense for what they're trying to teach.

Example approaches I've seen work:

1.  English class: Students use AI to generate a first draft at home, then revise it in class and explain their revision choices. Test their editing skills and understanding, not just writing from scratch.
2.  Math class: Homework can use AI to check answers. Tests are in-class, no calculators for the first section (basic problems), calculators allowed for the second section (complex problems).
3.  History class: Use AI to gather information about a topic, but the in-class essay tests whether you actually understood and synthesized that information or just memorized what the AI said.
4.  Science class: AI can help with research and hypothesis generation at home. Lab work and analysis happen in class, where you have to apply what you learned.

The key is that teachers have the flexibility to design assessments that test actual understanding, not just "did you complete this task."

Smart Teachers are only going to create Smart Students

What Does This Actually Look Like in Practice?

Let me explain to you a picture of how this could work:

At home:

• Student has a writing assignment
• They can use ChatGPT to brainstorm ideas, outline, even draft
• They can use it as a tutor: "explain this concept to me"
• They can ask it to review their work and suggest improvements

In class:

• Timed essay on a related topic
• No AI, no notes (or maybe one cheat sheet they made)
• Teacher can see them writing
• This is what gets graded

What the student learns:

• How to use AI as a tool (like using calculators or spell-check)
• But also how to write/solve/think without AI
• How to verify AI output and catch mistakes
• Time management and working under pressure

What the teacher learns:

• Whether the student actually understands the material
• Where the student's genuine skill level is
• What gaps need to be addressed

The Pushback I Keep Hearing  when I discuss with other teachers,

• "But this means more work for teachers!"

Kind of, but also not really. You're already grading homework. Instead of grading homework that might be AI-generated (wasting your time), you're grading in-class work that you KNOW reflects the student's actual ability.

Plus, you can make homework less formal. Quick checks for completion rather than detailed grading. Save your energy for the in-class stuff that matters.

• "What about kids who test badly?"

This is a real concern. Some kids genuinely do better with take-home work because they have test anxiety or need more time to process.

Solutions: Give more time for in-class work. Allow some accommodations (notes, extra time). Have a mix of evaluation types. The point isn't to torture anxious kids, it's to verify actual learning.

• "Parents are going to complain."

Yep, probably. Especially parents who are used to "helping" (doing) their kid's homework. But honestly? This is fairer to kids whose parents CAN'T help. We would rather guide our nephew rather than do everything on behalf of him.

And if you explain the reasoning-"we want to make sure your child actually learns this, not just turns in AI work", most parents will get it.

• "This won't work for all subjects."

True! Some things are harder to test in class. Long-term projects, research papers, and creative work need time to develop.

For those, you might need different strategies. Maybe students keep a process journal showing their work. Maybe they present and defend their work in class. Maybe you do regular check-ins throughout the project.

The point isn't that EVERYTHING has to be in-class. It's that evaluation needs to happen in settings where you can actually verify learning.

• What About the Kids Who Are Honest?

This is the part that keeps me up at night. Right now, you've got some kids using AI for everything, and some kids doing the work honestly. And the honest kids are disadvantaged because they're spending way more time on homework.

Moving to in-class grading ensures a fair assessment environment for all students. The honest kids aren't penalized anymore. And the kids who were over-relying on AI? They'll figure out pretty quick that they need to actually learn this stuff.

A teacher told me this story:

She had two students, both getting A's on homework. One was doing it herself, spending hours. The other was using ChatGPT, spending 10 minutes.

First in-class essay? The honest kid got an A. The AI-dependent kid got a C-.The AI kid came to her after class, freaking out. "I don't understand why I did badly!"

Teacher's response: "Because you haven't actually been learning. You've been letting AI do your homework. Now when you actually have to write, you don't know how."

That kid started actually doing the work after that. Still use AI sometimes, but as a tool to help learning, not a replacement for it. This was we need to configure our school ecosystem for the betterment of education.

That's the goal. Not to punish kids for using AI, but to make sure they're using it in a way that actually builds skills instead of avoiding learning.

The Bigger Picture Here

This isn't really about AI. It's about the fact that our education system was already kind of broken, and AI just exposed the cracks. Tell me how many toppers during your school time are doing great in life? I think none or very few.

We are already grading busy work instead of actual learning. We are already letting kids slide by without really understanding things. We are already creating incentives to just "get it done" instead of "actually learn.

"AI didn't create these problems. It just made them impossible to ignore".

The opportunity here:

• We can redesign education to focus on what actually matters-critical thinking, problem-solving, understanding concepts, being able to apply knowledge in new situations.
• We can stop pretending that homework completion equals learning.
• We can give students powerful tools (like AI) while also making sure they develop real skills.

But only if we stop trying to fight AI and start figuring out how to teach effectively in a world where AI exists.

What You Can Actually Do About This

If you're a teacher:

• Stop relying on AI detectors. They're not helping.
• Start thinking about how to shift evaluation to in-class work
• Experiment with different assessment types
• Talk to other teachers about what's working
• Give students opportunities to use AI in ways that support learning

If you're a parent:

• Stop doing your kid's homework (yes, even "helping")
• Support teachers who are trying new approaches
• Talk to your kids about using AI ethically
• Make sure your kid actually understands their homework, not just completes it

If you're a student:

• Use AI to help you learn, not to avoid learning
• Remember that you'll be tested without AI eventually
• Don't screw yourself over by never developing real skills
• Be honest about what you know and what you don't

If you're an administrator:

• Give teachers the flexibility to redesign assessments
• Stop buying expensive AI detection software that doesn't work
• Support professional development around AI in education
• Focus on learning outcomes, not homework completion rates

Conclusion :

AI in homework is here to stay. You can't detect it. You can't stop it. Trying to do either is just wasting time and creating false accusations.

What you CAN do is restructure how you evaluate learning so that students:

1. Learn to use AI effectively
2. Also develop real skills they can use without AI
3. Understand material well enough to catch AI mistakes

The way to do that is to shift evaluation to in-class settings where you can actually verify what students know. It's not a perfect solution. It'll take work to implement. Some things will need to be figured out along the way.

But it's better than the current situation, which is "pretend AI doesn't exist and hope for the best." Look, I know this is a massive shift. I know it's scary. I know teachers are already exhausted and don't want another thing to figure out.

But we're at one of those moments where we can either evolve or keep doing something that doesn't work anymore. And honestly? I think if we do this right, it could make education better. More focused on actual learning. Less focused on busy work.

Teaching kids to use powerful tools while also building real skills.That doesn't sound so bad, does it? Please do share your opinion in comments as it's a topic to debate and learn from each other.

I also read that their pay sounded pretty good. Like, $50-100+ an hour for some tasks with Remote work and Flexible hours. It sounded almost too good to be true for anyone trying to make money online. And honestly, it kind of is. But you know, there’s now a whole black market built around getting into these jobs. People are actually buying & selling Facebook & WhatsApp accounts just so they can work for companies like Scale AI and Prolific. The whole thing is way more organized and way sketchier than I ever expected.

What Are AI Training Jobs?

Let me back up & explain what we're talking about here.You know how ChatGPT can write essays and answer your questions? Or how image AI can recognize what's in a photo? That doesn't happen by magic. Behind every AI model are thousands-sometimes tens of thousands-of real humans doing repetitive tasks. Yes, you heard it right !!!

What these jobs actually look like:

1. Rating chatbot responses: "Which answer is better, A or B?"
2. Labeling images: "Circle all the stop signs in this photo"
3. Reviewing content: "Is this response accurate/helpful/appropriate?"
4. Writing sample text: "Write a paragraph about cooking pasta"
5. Checking facts: "Is this statement true or false?"It's not glamorous work. It's honestly pretty boring. But it pays way better than most remote gigs, especially if you live in a country where $50/hour is like a month's salary in a local job.

The companies hiring for this:

• • Scale AI (through their Outlier platform)
  • Surge AI
  • Dataloop
  • Encord
  • Prolific
  • Appen
  • Lionbridg
  • A bunch of others

Some of this work is open to anyone, anywhere. But a lot of the highest-paying tasks are restricted to specific countries-usually the US, UK, Canada, parts of Europe. And that's where the problems started, which gave rise to this black market.

The Gold Rush That Created a Black Market

Imagine you live in Nigeria or the Philippines or Pakistan. You hear about this remote work that pays $100/hour. That's life-changing money. That's more than most local professionals make.But when you try to sign up? "Sorry, this work is only available in the United States."What do you do? For a lot of people, the answer became: buy someone else's account. This is the same as how people buy reputable Facebook/Google invoicing accounts for their ad campaigns.

Here's what I found when I started digging:

There are over 100 Facebook/Telegram groups dedicated to buying & selling verified AI training accounts. I'm not overestimating this. Business Insider found them, and when I went looking myself, I found even more. The groups have names like "Scale AI Account for Sale" or "Outlier Verified Account" or just harmless names that don't mention accounts at all. But inside? It's a marketplace. I personally searched using my Facebook and got what I was looking for.

What people are selling inside these groups :

• "Fully verified Scale AI account, US-based, $200"
• "Rent my Prolific account, 30% of earnings"
• "VPN setup + account access, complete package"
• "Will take your screening test for you, $50"

I saw posts from people offering to sell their accounts because "I don't have time for this work anymore" or "I need quick cash."  I also  saw buyers posting "URGENT: Need US account, will pay $300."And I saw scammers. So many scammers. Look like another level of scamming like crypto coins.

Why This Market Exists (It's Not Just About Cheating)

Before I go further, I want to be clear: I'm not defending account fraud. But I think it's important to understand WHY this is happening, because it's not as simple as "people are lazy cheaters."

Reason 1: The Pay Gap is Massive

In the US, $50/hour is decent money. Nice side income. Maybe you do it while watching TV. In countries like Kenya, India, or Venezuela? $50/hour is more than most doctors make. It's a transformative income that could support an entire family. When that kind of money is coming while sitting anywhere, irrespective of geography, people get desperate. Also it doesn't require some super special skills as well.

Reason 2: Work Availability is Wildly Inconsistent

I talked to someone who does legitimate AI training work. She told me some months she makes $3,000. Other months? $200. The work just... disappears. Projects end.  Clients Demand shifts. When you're in a country where work is available, this is annoying. When you're in a restricted country watching people talk about these jobs online, it's an opportunity.

Reason 3: The Tests Are Really Hard

To get accepted for higher-paying work, you often have to pass screening tests. These aren't simple multiple-choice. They're hours-long evaluations of your writing, reasoning, and subject expertise.A lot of people fail these tests. And when they do, they see posts saying "I'll take your test for you" and think... why not?

Reason 4: It's Technically Easy to Fake

VPNs exist. You can make your computer look like it's in New York when you're actually in Vietnam. You can rent a "shadow proxy" that mimics a US internet connection so well that most systems can't tell. If the only thing stopping you from a life-changing income is a VPN and a borrowed account. Now you can imagine this temptation is huge.

How the AI Training Black Market Actually Works

Okay, so I went deep into researching this.  Joined some groups (with a burner account, I'm not dumb). Read through hundreds of posts. Watched some YouTube videos of people explaining how it's done. I can only say one thing: It's organized. Like, surprisingly organized.

Method 1: Buying or Renting Accounts

Buying outright: In this case someone pays $200-500 for full account access. The original owner transfers the login info, sometimes the payment details, sometimes even helps set up a VPN.

Renting: The owner keeps control but lets someone else do the work. The renter pays a percentage of earnings-usually 30-50%. Sometimes they pay upfront rent like $100/month. Some sellers provide full "packages":

• Account login
• VPN setup instructions
• Tips for passing quality checks
• Contact info "in case you get caught"

Method 2: Stealing Real Accounts

This one's nastier. Scammers send fake emails that look like they're from Scale AI or Prolific:"Congratulations! You've been selected for a premium project. Click here to verify your account."Or:"Your payment is on hold. Update your information to receive funds."People click the link, enter their login info on a fake phishing website, and boom-account stolen.I saw posts in these groups from people saying "Someone logged into my account and changed my password! How do I get it back?" And the responses were basically "tough luck, should've been more careful."

Method 3: The Test-Taking Services

In this case, some people offer to take your screening test for you, like a remote proxy. You pay them $50-100, then you give them temporary access, and then they pass the test. You get verified. There are even group "study guides" where people share test questions and answers. The platforms try to randomize tests to prevent this, but people screenshot everything and share it. So the system is rigged

Method 4: The Complete Fraud Setup

Here, I also found that the most sophisticated operations aren't just selling accounts. They're selling complete fraud systems:

• Pre-verified accounts with work history
• "Clean" IP addresses that won't trigger fraud detection
• Software to mimic typing patterns (so your behavior looks like the original owner)
• Scripts for automatically selecting answers on repetitive tasks
• Coaching on how to communicate with platform support

One post I saw was literally titled "Full AI Training Work Setup - Everything You Need - $1000." And people were buying it.

The Scams Within the Scams (It Gets Worse)

Here's the dark, funny part: the black market is full of people scamming each other. It's more similar to how government scams common people with the illusion of progress.

1. Buyers getting scammed:

In this scenario, Someone pays $200 for an account. receives login info. Tries to log in. The password doesn't work. The seller has disappeared. Money gone. Or they get an account that works for two days, then gets banned because it was flagged for suspicious activity before they even bought it.Or they get a legitimate account, but the original owner reports it stolen and gets it back, leaving the buyer with nothing. I observed that in many Facebook groups, Many people who are looking for easy money fall for this trap.

2. Sellers getting scammed:

In this scenario, Someone "rents" their account. The renter does a bunch of work, earns $2,000. Then disappears without paying the owner their cut.Or the renter does terrible quality work, gets the account banned, and the original owner loses their income source permanently.

3. The middlemen scamming everyone:

In this scenario, People posing as "verified sellers" taking money from multiple buyers, providing nothing, then delete their accounts and start over under new names. I saw one thread where someone had compiled a list of known scammers with screenshots of their scams. The list had over 50 names. And people were still falling for it because new scammers kept popping up.

 People trying to commit fraud are getting defrauded in the process. It would be funny if it wasn't also kind of sad.

What Happens When You Get Caught

The platforms are not stupid. They know this is happening. And when they catch you, the consequences are real.

- For the account buyer:

Your account gets permanently banned. The money you paid for it? Gone. Any work you did? You don't get paid for it. Sometimes they ban your IP address or device fingerprint, so you can't just create a new account. If you committed identity fraud (using someone else's personal info), you could technically face legal action, though I haven't heard of this actually happening much.

- For the account seller:

Your account gets banned. You lose all access to the platform, including any money you had earned but not withdrawn yet. You might face tax issues if someone else earned money under your name and you have to explain to the IRS why your income doesn't match what was reported. If you rented your account and the renter did something illegal with it (shared harmful content, etc.), you're technically responsible since it's your account.

- For both:

Once you're banned from one platform, word can spread. These companies talk to each other. Get caught cheating on Scale AI? Good luck getting verified on Surge or Prolific.

Impact of these fraudulent activities on the AI Industry

You might be thinking, "so what, it's just people trying to make money." And I get that perspective. But this black market has real consequences that go beyond just breaking terms of service.

1: The AI Models Get Worse

If contractors are cheating, cutting corners, or using AI tools to complete the tasks they're supposed to be training AI with (yes, that's happening), the data becomes garbage.

I also learn that a model trained on synthetic garbage data makes worse predictions, gives worse answers, and makes more mistakes, along with hallucination.

For example, your annoying interactions with ChatGPT where it confidently states something completely wrong? It happen because the training data came from people who didn't actually evaluate the responses carefully.

Also, as per my understanding, such data is vulnerable to data exfiltration attacks.

2: Legitimate Workers Suffer

Imagine you're someone who legitimately qualifies for this work. You passed the tests honestly. You're in an approved country. You do quality work.

But now you're competing with people who bought accounts, who are willing to work for less (because they didn't earn the account legitimately), who spam low-quality responses to maximize volume.

Task quality goes down. Pay rates drop. Work becomes harder to get. The honest workers get pushed out by fraudsters. Such acts lower the confidence among legitimate workers.

3: Worker Safety Goes Out the Window

Here, people buying accounts often have zero protection. No support if something goes wrong. No recourse if they don't get paid.

It also leads to the risk of identity theft if they use personal info to verify the account. And they can't complain to the platform because, well, they're not supposed to be there in the first place.

4: It Makes Companies More Restrictive

When platforms detect high fraud rates from certain countries, what do they do? They ban or restrict access to those entire regions.

This punishes the legitimate workers in those countries who weren't doing anything wrong. It creates a cycle where restrictions drive fraud, fraud drives more restrictions, and honest people suffer most.

What the Companies Are Actually Doing About It

So I dug into some internal documents and reports about how these platforms are fighting back. And honestly? They're trying, but they're also kind of losing.

I even heard that Scale AI has been dealing with this for years, Thousands of accounts flagged as spammers or duplicate users. Due to this, entire countries have been temporarily banned from certain projects due to suspected mass fraud.

They also use VPN detection systems that catch obvious fakers. They also use Quality control systems that flag suspiciously fast or low-quality work

Prolific said something interesting: They compared this fraud to banking fraud or ticket scalping. It's not just individual cheaters anymore. It's organized rings with sophisticated tools.  I think more such orchestrated scams will rise after AI in the upcoming years.

Meta (Facebook) removed some groups after being notified, but for every group they shut down, three more pop up. Plus, the fraud has moved to WhatsApp and Telegram where it's even harder to track.

The problem: This is an arms race. Companies add security measures. Fraudsters find workarounds. Companies add more measures. Fraudsters get more sophisticated.

How to Actually Fix These AI  Frauds

Okay, after diving into all this, here's what I think needs to happen. Not just cracking down on AI scammers, but addressing why it exists in the first place.

Fix 1: Make Accounts Harder to Share (Technical Solutions)

1. Better identity verification:

• They can do Random selfie checks with liveness detection (so you can't just use a photo)
• Device fingerprinting that's harder to fake
• Behavioral biometrics (your typing rhythm, mouse movements-everyone's slightly different)

2. Smarter fraud detection:

• They can flag accounts where work patterns suddenly change drastically
• Detect VPNs and virtual machines better
• Send an Alert when someone logs in from wildly different locations within short timeframes

3. Better test security:

• They can make tests adaptive so they can't be easily shared
• Change test questions frequently
• Use different tests for different regions and time periods

I talked to someone who works in fraud detection, and he said the technology exists to make account sharing way harder. It's just a matter of platforms investing in it. So in short, it's a game of economy.

Fix 2: Stop Enabling the Black Market (Platform Enforcement)

Platforms need to:

• Permanently ban anyone caught selling accounts (not just suspend-BAN)
• They need to work with Facebook, WhatsApp, and Telegram to shut down fraud groups faster
• Prosecute obvious fraud cases to create actual legal discussions.
• They can blacklist people across multiple platforms (if you cheat on Scale, you shouldn't get to work on Prolific)

Social media companies need to:

• Create easy reporting tools for fraud groups
• Proactively scan for keywords related to account sales
• Actually enforce their own policies (looking at you, Facebook)

Fix 3: Be Transparent About How This Work Actually Works (Policy Changes)

I also observed in many legit Facebook groups that A lot of the frustration comes from confusion. People don't understand why certain countries are restricted, why work dried up, and why they failed the test.

Platforms should publish:

• Clear reasons for regional restrictions
• Expected work availability by region
• What the quality standards actually are
• Regular updates on project status

When people understand the system, they're less likely to fall for scams claiming to have "insider access" or "secret methods."

Fix 4: Create More Legitimate Opportunities (Economic Solutions)

Here's the uncomfortable truth: as long as you have desperate people in low-income countries watching others make $100/hour for remote work they're locked out of, you'll have a black market.

Possible solutions:

• Expand regional projects: Even lower-paying work in restricted countries is better than no work. If someone in Kenya could make $20/hour (still good money locally) doing legitimate work, they'd have less reason to buy a US account.
• Offer training programs: Instead of just failing people on tests and leaving them there, offer practice tasks, feedback, and certification programs. This help people actually qualify instead of pushing them toward buying accounts.
• Create tiered pay scales: Maybe some tasks can be done anywhere at a global rate, while specialized, high-paying work remains restricted. Gives people a legitimate path to income.
• More predictable work: If contractors knew they'd have consistent work (even at lower rates), they'd be less desperate. It's the feast-or-famine cycle that drives people to extremes.

Fix 5: Educate Workers on Protecting Themselves

You and I observe day to day that a lot of people getting scammed don't even realize they're being scammed until it's too late.

Workers need to know:

• How to spot phishing attempts
• Why buying accounts is risky (you lose money AND can face legal issues)
• How to report fraud groups
• What legitimate communication from these platforms looks like
• Their rights as contractors

Some platforms are starting to do this. Not enough of them, and not consistently enough.

What Happens Next (The Arms Race Continues)

Here's my prediction: this problem gets worse before it gets better. As AI becomes more valuable, these jobs become more lucrative. As they become more lucrative, more people try to access them. As more people try to access them, the black market grows. Meanwhile, the fraud techniques get more sophisticated. The scams get more convincing. The workarounds get harder to detect.

But eventually, some workaround will come :

• Either platforms figure out how to make fraud economically unviable through better security and enforcement...Or
• they expand access to reduce the demand for black market accounts...Or
• governments step in with regulations about gig work, identity verification, and cross-border labor...Or
• AI training shifts to entirely different methods that don't rely on massive human labor forces.

My guess? All of the above, gradually, over the next few years.

What You Should Actually Know About This

If you're thinking about buying an AI training account:

• Don't. Seriously. The chances of getting scammed are high, the chances of getting caught are decent, and the consequences of both suck.
• Find legitimate platforms that accept workers from your country. They exist, even if they pay less. I know short-term games give you quick wins, but a big disaster in the long term.

If you're working legitimately:

• Protect your account. Use strong passwords. Don't click suspicious links. Don't share your login with anyone, ever.
• Report fraud groups when you see them. It helps everyone.

If you're just a person using AI:

Understand that those slick AI models are built on the backs of thousands of underpaid contractors dealing with this messy reality.  And maybe be a little more forgiving when the AI makes mistakes, because the training data isn't always as clean as you'd hope.

This literally opens my eyes as we are building synthetic data to train next frontier AI models, and avoiding this garbage is a big challenge for all big tech giants. But I'm optimistic that we as humans will find a way.

Conclusion

After digging a lot into this topic, I learned lot of important things we all should understand as we are moving towards a big technological shift.  I can say there's a massive underground economy built around buying and selling access to AI training work.

It's part scam, part desperation, part exploitation, and entirely predictable when you create high-paying opportunities and artificially restrict access to them. The platforms know about it. They're trying to stop it.

But as long as the incentives exist, the black market will keep existing too. Fixing this requires more than just better security.

The AI industry is built on human labor. If that foundation is rotten with fraud, the whole thing gets shakier. And we're all depending on these AI systems more every day. Something to think about.

It requires rethinking how we structure global remote work, who gets access to opportunities, and what responsibilities platforms have to workers worldwide.

In the meantime? Don't buy accounts. Don't rent accounts. Don't fall for scams. And if you see this stuff happening, report it. This is what I can suggest to all my readers.

My browser had 47 tabs open. FORTY-SEVEN. My laptop was practically on fire. This year? I decided to let AI do the heavy lifting.

Recently, I tested the major AI shopping assistants that everyone's talking about, and honestly? Some of them are genuinely useful. Some are... well, they're trying.

Let me break down what actually works and what's just hype.

Best AI Shopping Assistants for your online shopping

There are three AI shopping assistants that you can actually use, and most of them are currently available only in the United States. Each one has a different way of handling your shopping research queries.

1. ChatGPT Shopping Research - The Overachiever Friend Who Does Your Homework

What it is: OpenAI just rolled out this shopping research feature in ChatGPT, and it's basically like having that friend who's really good at research do all your deal hunting for you.

How it actually works:

I tested this yesterday when I was looking for a new air fryer. Instead of Googling "best air fryers 2025" and falling down a rabbit hole of affiliate blog posts. I just told ChatGPT: "I need an air fryer under $100 that's easy to clean and fits on a small counter."

Here's what happened:

ChatGPT asked me follow-up questions.

"How many people are you cooking for?" "Do you care about brand?" "Cleaning features?"

Then it went off and did its thing.

Came back with a personalized buyer's guide that wasn't just a generic list. It explained WHY each option made sense for what I asked for, included current prices from multiple retailers, and even flagged which "deals" weren't actually deals.

What I actually liked:

• It asks smart questions instead of just vomiting out a generic list. Like a real person would if you asked them for advice.
• It actually researches. I spot-checked some of its findings, and yeah, it was pulling from real product pages, real reviews, real pricing. Not just making stuff up.
• The comparisons are genuinely helpful. It didn't just list five air fryers. It broke down pros and cons based on what I actually said I cared about.

What's annoying:

• You need ChatGPT Plus ($20/month) to use this feature. The free version doesn't have it yet.It's US-only right now. If you're outside the US, you're out of luck.
• Sometimes it's TOO thorough. I asked for headphone recommendations once and got a full dissertation. Dude, I just want Bluetooth earbuds that don't fall out when I run.

Best for:

When you kind of know what you want but need help narrowing it down. When you're overwhelmed by choices and need someone (something?) to cut through the noise.

Quick warning: If you let an AI access your computer, it might change or delete files without asking. It could also expose your private data. These tools will get safer over time, but for now, use them carefully. This post only talking about research assistants not AGENTIC AI tools

2. Google's Gemini Shopping Assistant - The One That'll Actually Call Stores For You

What it is: Google integrated its Gemini AI into Google Shopping, and it does some genuinely cool stuff that the others don't.

The feature that blew my mind:

There's this thing called "Let Google call." I'm not kidding. If you're looking for something at local stores and want to know if they have it in stock, Gemini will CALL THE STORES FOR YOU.

Here's how it went down:

I was looking for this specific LEGO set for my nephew. Couldn't find it online for a decent price, and most stores don't show real-time inventory online. So I searched "LEGO Star Wars Millennium Falcon near me" on Google.

It asked a few clarifying questions (which specific set, which stores I wanted it to check)

Then it literally called like five stores in my area while I made coffee. Texted me back 10 minutes later with which stores had it in stock and at what price.

I felt like I was living in the future. A slightly dystopian future where robots make phone calls for me, but still.

What else does it:

• Price tracking across different retailers. It'll show you the same product at Target, Walmart, Best Buy, etc., all in one view.
• Virtual try-on for some products. Mostly clothes and accessories. It's... okay. Not amazing, but better than nothing.
• Deal validation. It'll tell you if a "50% off" deal is actually 50% off or if they just jacked up the original price to make it look like a deal.

What I actually liked:

• The store-calling thing is genuinely useful for supporting local small businesses that don't have fancy inventory systems online.
• It integrates with everything Google, so if you already use Chrome and Google services, it just... works.
• The price comparison is cleaner than most other tools. No cluttered interface, just straightforward info.

What's annoying:

• It's tied to the Google ecosystem. If you're not using Chrome and logged into a Google account, it's less useful.
• The AI can be a bit aggressive with suggestions. Like, I searched for one thing and suddenly my entire Google feed was trying to sell me related products.
• Privacy. It's Google. They're tracking what you shop for. If that bothers you, maybe skip this one.

Best for:

• When you're trying to shop local and need to know the inventory.
• When you want quick price comparisons without opening 15 tabs. When you're already deep in the Google ecosystem anyway.

3. Perplexity AI Shopping Tool - The Research Nerd

What it is: Perplexity is this AI search engine that's been gaining traction, and they recently added shopping features that are... interesting.

Full disclosure: I'm less familiar with Perplexity than the other two, so I went in skeptical.

How it works: You ask Perplexity questions about products, and it searches the internet in real-time, then gives you answers with actual sources cited. It's like if Google and ChatGPT had a baby that was really into fact-checking.

I asked it: "What's the best budget laptop for students on Black Friday?"

What it gave me:  A breakdown of current deals with links to where it got the information. Not just "here's a laptop," but "here's a laptop, and here's the review from TheWindowsClub, Newegg, and Target,  and here's the price history from CamelCamelCamel."

What I actually liked:

• The citations. Every claim has a source. I can click and verify. This is huge if you're skeptical of AI making stuff up.
• Real-time search. It's not pulling from old training data. It's searching right now for current prices and deals.
• Good for comparing specs. If you're trying to understand the difference between two similar products, Perplexity is really good at breaking down technical details.

What's annoying:

• The interface is more bare-bones. It's not as polished as ChatGPT or Google.It's not really designed for shopping specifically, so the experience feels more DIY. You have to know what questions to ask.
• Sometimes it gives you TOO much information. I asked about wireless earbuds and got a 2,000-word essay with 20 citations. Dude, just tell me which ones are good.

Best for:

When you want to verify that a deal is legit. When you're comparing technical specs and want sources. When you don't trust AI to not make stuff up.

Which AI Shopping Assistants You Actually Use?

Here's my honest take after testing all three:

Use ChatGPT Shopping Research if:

• You kind of know what you want but need help deciding it.
• You want personalized recommendations based on your needs
• You don't mind paying $20/month for ChatGPT Plus
• You want something that feels like talking to a smart friend

Use Google's Gemini Shopping Assistant if:

• You're shopping local and need to check store inventory
• You want quick price comparisons across major retailers
• You're already using Chrome and Google services anyway
• You want something free and easy

Use Perplexity AI Shopping Tool if:

• You're skeptical and want to verify everything
• You're comparing technical specs and need real sources
• You want real-time deal research with citations
• You're okay with a less polished, more DIY experience

My personal workflow this Black Friday:

I'm using all three, honestly. Here's how:

1. Start with ChatGPT to figure out what I actually need and narrow down options
2. Check Google/Gemini for local availability and price comparisons
3. Verify with Perplexity if a deal seems too good to be true

Takes maybe 10 minutes total instead of the four-hour nightmare from last year.

What are the missing things in these AI Shopping Tools (And What You Still Need to Do Yourself)

Look, these AI tools are helpful, but they're not magic. Here's what they can't do:

• They can't tell you about super niche retailers. If you're looking for something from a small independent shop, AI probably won't find it.
• They sometimes miss the best deals. Lightning deals, member-only sales, store-specific promotions. AI can miss these if they're not publicly visible.
• They can't predict restocks. If something's sold out, AI can't tell you when it'll be back. [Gemini CALL ME feature is good for this]
• They don't know about stacking deals. Like using a coupon code on top of a sale price. Humans are still better at this game.

My Honest Tips for Using AI Shopping Assistants

• Be specific in what you ask for. Don't just say "I need a TV." Say "I need a 50-inch TV under $400 for a bright living room that's good for sports." The more detail you give, the better the AI can help.
• Double-check the prices. AI pulls prices from across the web, but they can be outdated. Always click through to verify the actual current price.
• Read real human reviews too. AI can summarize reviews, but it misses nuance. I always read at least a few real reviews before buying anything expensive.
• Use AI for research, not for the final purchase. Let AI narrow down your options, then use your human judgment to make the final call.
• Don't blindly trust "this is a great deal" claims. Use tools like CamelCamelCamel or Honey to verify price history. Sometimes what looks like a Black Friday deal is actually the normal price with fake urgency.

The Weird Stuff I Noticed While Testing

1. I observed that AI has opinions now. ChatGPT will straight-up tell you "I wouldn't recommend this one because..." Like it has preferences. It's based on data and reviews, but it's still weird.
2. The answers change based on how you ask. I asked ChatGPT the same question three different ways and got slightly different recommendations each time. The way you phrase things matters.
3. AI is weirdly good at gift shopping. I told ChatGPT "I need a gift for my mom who likes gardening but her knees hurt" and it suggested this kneeling pad thing I never would've thought of. She loved it.
4. Sometimes it's confidently wrong. I caught Perplexity citing a price that was outdated, and ChatGPT once told me a product had a feature it definitely doesn't have. Always verify.

Here is My Black Friday Strategy Using These AI Shopping Tools

Here's what I'm actually doing this year:

Week before Black Friday:

• Ask ChatGPT to help me make a list of what I actually need
• Have it research options for each item
• Set up price tracking on Google for the finalists

Black Friday week:

• Check if any of my tracked items hit my target price
• Use Gemini to call local stores for anything I want to buy in person
• Double-check "deals" with Perplexity to make sure they're legit

Day of purchases:

• Verify final prices are still good
• Read a few recent human reviews
• Pull the trigger

Final Verdict:

This time I invested 2-3 hours total instead of the all-day marathon I usually do. This is way productive than my earlier Black Friday research. But still it required a lot of improvements.

As per my understanding, these AI shopping assistants aren't going to magically find you a $2,000 TV for $100. They're not going to eliminate all the work of smart shopping. But they CAN:

• Save you time researching
• Help you avoid fake deals
• Find products you might not have discovered otherwise
• Compare prices faster than you can manually
• Ask questions you forgot to consider

So you must use carefully and tactically to fulfill your end goal. Please do share your comments and experience with these AI Shopping tool and you can share if you are using some diffrent tool as well.

He was right. The dog's back legs were doing this weird morphing thing. I'd been fooled because of my negligence in spotting this mistake. That sucked.  So I spent my weekend learning how to spot fake videos before anyone can share them. And honestly? It's not that hard once someone shows you what to look for. Let me break it down in the simplest way possible.

How to spot fake AI videos generated using LLM

Sign 1: The Video Looks Like It Was Filmed in 1990's

What does this mean?
The video is blurry, grainy, or pixelated-like it was filmed on a phone from 15 years ago.

Why is this suspicious?
Everyone has a decent camera now. Even cheap phones take pretty good video. So if someone posts a blurry mess in 2024, ask yourself: why?

The trick:
AI creators make videos blurry ON PURPOSE. Why? Because blur hides mistakes. If you can't see details clearly, you won't notice when the AI messes up.

Easy test:
If the video shows something amazing but looks like it was filmed through a dirty window, that's your first clue.

Example from my life:
Saw a "security camera" video of a "ghost" in a store. Super blurry. Went viral. Turns out it was AI, and the blur was hiding the fact that the "ghost" had weird, melting hands.

Sign 2: The Video you're watching is really, really short

What does this mean?
Most fake AI videos are like 5-10 seconds long. Rarely longer than 30 seconds.

Why is this a clue?
Making AI video is expensive and difficult due to its computational cost. The longer the video, the more the AI system struggles and makes mistakes. So fakers keep things short.

What to watch for:

• Video is under 10 seconds
• Or it's longer but has little jump-cuts every 8-10 seconds (those are separate AI clips glued together)

Easy test:
Think about it- if you saw something crazy happen in real life, would you stop recording after 6 seconds? Nope. You'd keep filming until your phone died or your arm got tired.

My rule:
If something "amazing" happens but the person stopped filming almost immediately, I'm suspicious.

Sign 3: Something Looks "Off" But You Can't Quite Say What

This is the hardest one to explain, but it's also the most important.

What to look for:

Skin that's too smooth:
If you see real people have pores, freckles, and uneven skin. AI people look like plastic dolls sometimes.

Weird hair:
Watch hair closely. Does it flicker? Does it look painted on? Real hair moves naturally and has individual strands. AI hair sometimes looks like a helmet.

Clothes with patterns:
Stripes, checkered shirts, any pattern-watch these closely. AI sometimes makes patterns morph or flicker between frames.

Backgrounds doing strange things:
Objects in the background might warp, bend, or move when they shouldn't. Trees look too perfect. Windows are wonky.

Movement that feels wrong:

• Lipsing is one of the most important indications.
• Hands move too smoothly or too stiffly
• Walking looks like floating
• Eyes don't quite look at what they should be looking at

Mouth and words don't match:
Even by just a tiny bit. Your brain notices even if you don't consciously realize it.

How I check:
I pause the video randomly and just... look at it. Does everything look real? Do the shadows make sense? Would a real photo look like this? You can also try to zoom-in on the video.

Sign 4: Bad Video Quality BUT Perfect Audio

What does this mean?
The video is grainy and blurry, but the sound is crystal clear and perfect.

Why is this weird?
If you're filming on a bad camera, the microphone is usually bad too. If video quality is terrible but audio is perfect, something's fishy.

What to listen for:

• Sound is TOO clear for the setting
• No background noise (real life always has background noise)
• Voice sounds slightly robotic or "acted"
• Sound effects are too crisp

Easy test:
Close your eyes and just listen. Does it sound like it was recorded in the place you're seeing? Or does it sound like it was recorded in a studio and added later?

Real example:
Video of a "street performance" that looked like security cam footage but sounded like a professional recording studio. Made no sense. Was AI.

Sign 5: The Camera Angle is Weird

What does this mean?
The video is filmed from a strange angle, or zoomed in weirdly, or cropped in a way that doesn't make sense.

Why does this matter?
Sometimes AI can't generate the full scene properly. So creators crop it, zoom it, or angle it weird to hide the parts that look fake.

Questions to ask:

• Why would someone film it this way?
• Why is it zoomed in so tight?
• Why is half the screen cut off?
• Why is the angle so awkward?

My test:
If the framing seems designed to HIDE things rather than SHOW things, I get suspicious.

Sign 6: It Looks Too Perfect

What does this mean?
The lighting is beautiful. The composition is perfect. The emotion is dialed up to maximum. It looks like a movie scene.

Why is this suspicious?
Real life is messy. Real videos are usually badly lit, poorly framed, and someone's thumb is probably covering part of the lens.

AI loves making:

• Super cute animal videos that are TOO cute
• Emotional moments that feel fake-perfect
• Dramatic scenes with movie-quality lighting
• "Magical" moments that seem too convenient

Easy test:
If it looks like a professional filmed it, but it's supposed to be spontaneous, ask yourself: how?

Example:
I saw a video of a "random encounter" between a kid and a butterfly that had perfect lighting, perfect framing, and perfect focus.

In real life? The camera would be shaky, the kid would be half out of frame, and the butterfly would fly away before you could focus.

Sign 7: It Went Viral Super Fast With No Source

What does this mean?
If the video has millions of views, but:

• Nobody knows who originally filmed it
• Posted by an anonymous account
• Can't find any original source
• No context or backstory

Why is this a red flag?
Real viral videos usually have a trail. You can find the original person who filmed it. There's context. Maybe news covered it.

Fake AI videos appear out of nowhere from accounts like "ViralClips247" or "AmazingContent_2024." Authenticity is questionable in this case.

What I do:
Before sharing, I click on the account that posted it. If the account is:

• Brand new
• Has no other content
• Has a generic name
• Posts only viral-bait content

I don't share it.

Sign 8: Someone "Happened" to Be Filming at the Perfect Moment

What does this mean?
The video shows something crazy happening, and the camera was perfectly positioned to capture everything.

Why is this suspicious?
Think about real life. When something unexpected happens:

• The camera is usually pointed the wrong way at first
• The person filming gets surprised and the camera shakes
• They might drop their phone
• The framing is usually terrible because they're reacting, not directing

Questions to ask:

• How convenient that they were filming
• Why was the camera pointed exactly there?
• How did they stay so calm and keep filming perfectly?

My rule:
If the "spontaneous" event was captured with movie-quality camera work, I'm skeptical.

The Big Emotional Trick (This is Important)

Here's what I learned: Most of the Fake videos are designed to make you FEEL something strong so you share it without thinking.

If a video makes you instantly feel:

• Shocked - "OMG I can't believe this!"
• Happy - "This is the cutest thing ever!"
• Sad - "This is so touching!"
• Angry - "This is outrageous!"

STOP. Wait 30 seconds. That strong emotion to have doubt? That's exactly what the AI creator wants. They want you to share it immediately before your brain catches up.

What I do now:
If something makes me feel a strong emotion, I literally count to 30 before I share it. During those 30 seconds, I check if it's real. This is very easy process.

More factors to Check If Something is Real

Here's my simple process. It takes like 2 minutes:

Step 1: Look at who posted it
Is it a real person with a real account history? Or a sketchy account that only posts viral content?

Step 2: Read the comments
Usually, someone in the comments has already figured out if it's fake. Look for people saying "this is AI" or "this is fake because..."

Step 3: Reverse image search

• Take a screenshot of the video
• Go to Google Images
• Click the camera icon and upload the screenshot
• See if you can find the original source

Step 4: Check the basics

• Is it blurry? ✓
• Is it super short? ✓
• Does anything look weird? ✓
• Is the source sketchy? ✓

If I check 3+ boxes, I don't share it.

Your Super Simple Checklist to Spot AI Video

Before you believe or share a video, I have composed a simple checklist you can ask yourself:

About the video quality:

•  Is it weirdly blurry or low quality?
•  Is it really short (under 15 seconds)?
•  Does something look "off" even if you can't say what?

About the filming:

•  Is the camera angle strange?
•  Is it TOO perfectly filmed for something "spontaneous"?
•  Does the audio not match the video quality?

About the source:

•  Posted by a random or anonymous account?
•  Do they not have a clear original source?
•  Did it go viral super fast?
•  Can't find any verification or news coverage?

About your reaction:

•  Does it make you feel a STRONG emotion immediately?
•  Do you want to share it RIGHT NOW without thinking?

If you checked 3 or more boxes: STOP. Don't share until you verify it's real.

Why This Actually Matters

I know what you're thinking: "It's just a video, who cares if it's fake?"Here's why it matters:

1. It trains you to not question things.
The more fake videos you see and believe, the easier it becomes to fool you with worse stuff.

2.It spreads like a contagious virus.
When you share a fake video, all your friends see it. Some of them will share it. Now hundreds of people believe something false.

3. It can be used for bad stuff.
Fake videos aren't just cute animals. They can be:

• Political lies
• Scams to steal money
• Fake evidence to ruin someone's reputation
• Misinformation that hurts people
• Harmful propaganda for a specific society

4. It makes real videos less believable.
When everything might be fake, nothing feels real anymore.

The Future of Fake AI Video (And Why This Gets Harder)

I know the way AI systems are evolving. I have Bad news for you: In 1-2 years, all these tricks for spotting fake videos might stop working.

AI is getting better. Eventually, you won't be able to tell by looking.

What will work in the future:

• Special cameras that sign their footage (like a watermark you can't fake)
• Websites that verify videos are real before posting them
• Special computer programs that can detect AI in ways humans can't [More advanced SynthID,C2PA]

What this means for you:

Stop trusting what you SEE. Start trusting WHERE IT CAME FROM.

Bottom Line

Remember guys, you don't need to be a tech expert to spot most fake videos right now. You just need to follow the above checklist for a better understanding.

That's it. Those five things will catch like 80% of fake AI videos.

Let me save you that headache.

Quick thing before we start: I'm talking about removing watermarks from videos YOU made using free trials or demo versions. Not stealing someone else's work and scrubbing off their logo. That's just... don't do that. We're all trying to make cool stuff here without being jerks about it.

Method 1: Unwatermark AI - The One I Actually Use Most

Best for: Quick results with minimal effort | AI-powered accuracy
Difficulty: Easy | Time: 5-10 minutes

Okay, I'll be straight with you. Most of these tools are either terrible or way too complicated. But this one? This one actually works without making you want to throw your laptop out the window. I found Unwatermark AI after trying like six other tools that either crashed, added their own ugly watermarks, or just... didn't work. This one uses some AI magic to figure out what's behind the watermark and fill it in.

Sounds fancy, but really you just upload and click a button.

Step 1: Access the tool
Go to unwatermark.ai

Step 2: Upload your video
Drag and drop works, or hit that upload button if you're feeling traditional. Supports MP4, MOV, and most common formats.

Step 3: Mark the watermark
They give you a brush tool-just paint over the watermark like you're back in kindergarten art class. If the watermark moves around (because AI tools love to be annoying), you'll need to mark it at a few different points in the video.

Step 4: Process the video
Hit the remove button. Now go make yourself some coffee because this takes a minute. Maybe five minutes. Depends on how long your video is.

Step 5: Download and review
Check it first before you do anything with it. Sometimes the AI gets creative with shadows, and you'll need to try again.

What's Good:

• Actually removes watermarks (shocking, I know)
• Handles both static and moving watermarks
• Results look natural most of the time
• No complicated software to learn

What’s Annoying:

• The free version puts a tiny credit at the end
• Processing can take a while for longer videos
• Occasional weird artifacts if the background is complex

The real deal: It's not perfect, but it's the best balance between "easy to use" and "actually works" that I've found.

Method 2: HitPaw Watermark Remover - The Old Reliable

Best for: Simple corner logos | Beginners
Difficulty: Easy | Time: 10-15 minutes. I've been using HitPaw for about 2 years now. It's not the fanciest, but it gets the job done if you're dealing with a simple corner logo.

Step 1: Navigate to HitPaw
Head over to HitPaw's website. They have this online watermark remover thing that's pretty straightforward.

Step 2: Upload your video
Click, select and upload your file.

Step 3: Select the watermark area
Draw a box around the watermark. & I mean, really around it. If you miss even a tiny bit, you're starting over. I made a lot of mistakes, so this is important.

Step 4: Choose the removal method
They've got blur or this "AI fill" thing. AI fill works better unless your watermark is moving all over the place.

Step 5: Process and download
Wait for it to process. HD videos take forever. I once waited 15 minutes for a 2-minute video.

What's Good:

• Super simple interface-my mom could use this
• Reliable for stationary watermarks
• Web-based, no downloads required
• Decent results for basic removal

What's Annoying:

• Free version adds its own watermark (ironic, right?)
• Slow processing for HD videos
• Struggles with moving or semi-transparent watermarks
• Can't handle complex backgrounds well

When to use it: Quick fixes on simple corner logos where you don't need perfection.

Method 3: Vmake AI -  The New Kid That's Actually Pretty Good

Best for: Clean, professional results | Batch processing multiple videos
Difficulty: Easy | Time: 5-8 minutes

I also stumbled upon Vmake AI like two months ago when Unwatermark was taking forever on a big file. Decided to try something new, and honestly? I was impressed.

What I like about Vmake is that it feels more polished than some of the other tools. The interface is cleaner, the processing seems a bit faster, and the results are consistently good. Not always perfect, but consistently good.

Step 1: Go to Vmake
Navigate to vmake.ai. They have a bunch of other tools too, but we're here for the watermark remover.

Step 2: Upload your video
Click the big "Upload Video" button. You can drag and drop or browse your files. They support all the usual suspects-MP4, MOV, AVI, WebM.

Step 3: Select the watermark area
Here's where Vmake is different. You can either:

• Draw a box around static watermarks (easier)
• Use their brush tool for irregular shapes or moving watermarks

I usually start with the box tool because I'm lazy, then switch to the brush if I need to be more precise.

Step 4: Adjust settings (optional)
They have this little slider for "Removal Strength" which I usually leave on medium. High strength can sometimes make things look weird.

Step 5: Process and download
Hit that process button and wait. Processing time is pretty reasonable-shorter than Unwatermark in my experience, especially for 1080p videos.

Step 6: Preview before downloading
They show you a preview first, which is clutch. You can see if it worked before wasting time downloading.

What's Good:

• Faster processing than most competitors
• Clean, intuitive interface
• Preview before download (huge time-saver)
• Handles larger files well
• The removal strength slider gives you control
• Works on both videos and images

What's Annoying:

• Free version has a daily limit (I think it's 3 videos, but you need to check what's latest)
• Adds a small "Made with Vmake" credit
• Sometimes overly aggressive with removal, taking out stuff you wanted to keep
• You need to refresh the page to do another video

Pro tip: If your watermark is semi-transparent, try the medium removal strength first. High can sometimes create a "hole" effect that looks worse than the watermark.

Method 4: AniEraser - The Wondershare Surprise

Best for: Stubborn watermarks that other tools miss | Both images and videos
Difficulty: Easy | Time: 3-7 minutes

I also found AniEraser while doing my research on these AI tools.  It's made by Wondershare, which is the most popular media tool-making company. I was skeptical at first because some of their stuff is hit or miss, but this one actually surprised me in a good way.

What makes AniEraser different is that it seems to handle the tricky watermarks better. You know, the ones with transparency, or the ones that fade in and out, or the ones that overlap with important stuff in your video.

Those are usually a nightmare, but AniEraser handles them pretty well.

Step 1: Navigate to AniEraser
Go to aniEraser. It loads up in your browser-no download needed.

Step 2: Choose your format
They have separate buttons for "Remove from Image" and "Remove from Video."

Click the video  option.  P.S : Supports the usual formats-MP4, MOV, M4V.

Step 3: Upload your file
Drag and drop or click to upload. They're pretty good with file sizes-I've uploaded some chunky 4K clips without issues.

Step 4: Mark the watermark
Here's where AniEraser gets interesting. You can:

• Use the rectangle tool for simple logos
• Use the lasso tool for irregular shapes (this is actually really good)
• Adjust the size of your selection as you go

The lasso tool is honestly one of the better ones I've used. It's responsive and doesn't lag like some other tools.

Step 5: Let the AI work
Click "Erase" and watch it do its thing. The processing is pretty quick-faster than Unwatermark, about the same as Vmake.

Step 6: Download your result
Preview it first (they show you before downloading, which I appreciate). If it looks good, download. If not, you can try again with different settings.

What's Good:

• Handles complex watermarks really well
• The lasso selection tool is excellent
• Fast processing speed
• Works great on semi-transparent watermarks
• Minimal quality loss
• Can handle both static and moving watermarks
• Works on images, too, if you need that

What's Annoying:

•  The free version limits you to 720p output
• Adds a watermark if you don't pay (because of course it does)
• Sometimes removes a bit too much around the edges
• The website can be a bit cluttered with ads for their other products

When to use it: When other tools are struggling with your specific watermark. This tool is good for watermarks with transparency or gradient effects. Also great when the watermark overlaps with important details in your video.

Method 4: VEED.IO - The Swiss Army Knife

Best for: When you need to edit AND remove watermarks | Professional touch-ups
Difficulty: Easy to Medium | Time: 10-15 minutes

VEED.IO isn't technically just a watermark remover. It's a whole video editor. But that's actually kind of the point. I use VEED when I need to do more than just remove a watermark. It's having heterogeneous functionality.

Like, maybe I also want to trim the video, add subtitles, or adjust the colors. Doing everything in one place saves me from bouncing between five different tools.The watermark removal feature is solid, but where VEED really shines is giving you options.

You can blur it, crop it out, cover it with something else, or actually remove it. That flexibility is pretty nice.

Step 1: Create a project
Go to veed.io and click on their watermark remover tool (or just start a new project). You'll need to sign up for a free account, which is mildly annoying but not the end of the world.

Step 2: Upload your video
Drag it into the editor. The interface looks a bit like iMovie or basic editing software-timeline at the bottom, preview in the middle.

Step 3: Add a removal element
Here's where VEED is different from the others:

• Click on "Elements" in the left sidebar
• You can add a blur box, a solid color shape, or use their remove background tool
• Position it over the watermark

Step 4: Adjust and track
If your watermark moves, you'll need to keyframe the covering element to follow it.

Click the element, then click the keyframe button, and move through your video adding keyframes as needed.

This sounds complicated but it's actually not that bad once you do it once.

Step 5: (Optional) Use other editing features
This is where VEED gets fun. While you're here, you might as well:

• Trim unnecessary parts
• Add text or captions
• Adjust brightness/contrast
• Add background music

Step 6: Export
Hit that export button in the top right. Free version exports at 720p with a VEED watermark (ironic, I know). The paid version gives you full quality. I'm using the paid version so I can export with better quality.

What's Good:

• Full video editor, not just watermark removal
• Multiple removal methods (blur, cover, crop, remove)
• Good for when you need to make other edits anyway
• Professional-looking interface
• Can add keyframes for moving watermarks
• Tons of other features included

What's Annoying:

• Free version adds VEED watermark (the irony)
• Requires account signup
• More complex interface if you just want quick watermark removal
• 720p limit on free tier

When to use this: When you've got a video that needs multiple fixes. Like, if you're removing a watermark AND need to cut out 10 seconds AND want to add text... this is your tool.

Common Screw-Ups During Watermark Removal (That I Definitely Didn't Make Multiple Times)

Here are some go-to queries that can help you during your editing process:

Problem 1: The watermark still shows through after processing

What happened: You didn't select the entire watermark area. That tiny sliver you missed will absolutely haunt you.

The fix:

• Go back and carefully select the ENTIRE watermark
• Add a little extra margin around the edges
• For moving watermarks, mark it at multiple timestamps

Problem 2: The video looks worse after removal

What happened: Your export settings are wrong. I once exported something at 480p by accident, and it looked like a potato.

The fix:

• Match your original video quality (check properties before editing)
• Use "High Quality" or "Best" export settings
• Keep the same resolution and frame rate as the original
• Don't compress twice-export once at full quality

Problem 3: Tools keep crashing or timing out

What happened: Your video file is probably too big. Some online tools can't handle 4K footage or really long videos.

The fix:

• Compress your video first using HandBrake (free)
• Reduce resolution temporarily (1080p is usually fine)
• Cut your video into smaller segments
• Try a different tool designed for larger files

Problem 4: Moving watermarks are impossible to remove

What happened: Yeah, they kind of are unless you use professional tools.

The fix:

• Use Anieraser Pro Resolve with tracking (best option)
• Pay for professional software like Hitpaw & Anieraser  Effects
• Or... just crop your video to exclude the watermark entirely
• Sometimes the easiest solution is the best one

Problem 5: The background looks weird after removal

What happened: The AI guessed wrong about what should be behind the watermark.

The fix:

• Try a different removal method
• Use clone stamp manually in Veed.io
• Accept that some backgrounds are just too complex
• Consider if a small blur might look better than a weird artifact

Final Tips Before You Go

• Always save your original - Never overwrite the source file. You might need to try again.
• Test on a short clip first - Don't process your whole 10-minute video only to find out it doesn't work.
• Check on different devices - What looks good on your phone might look terrible on a big screen.
• Consider if it's worth it - Sometimes the watermark honestly doesn't matter that much.
• YouTube is your friend -  Stuck? Someone's definitely made a tutorial for your exact problem.

You'll get better at this with practice. The first video I cleaned up took me two hours and looked mediocre. Now I can do it in 10 minutes, and it looks great. Most of the time.

The trick is knowing which tool to use when. You don't need Veed.io for a simple corner logo, and you can't use Vmake blur for professional work. Do let me know your queries in the comments to get a better idea about it.

Getting Started

You’ll need a Google account and access to Gemini’s tools.

1. Open gemini.google.com.
2. Click Tools in the menu.
3. Toggle on Create Images. This activates Nano Banana (Gemini 2.5 Flash Image).

That’s it. You’re ready to create images.

Step 1: Enable Image Creation

1. Go to gemini.google.com.
2. Click Tools → toggle Create Images.
3. This unlocks Nano Banana (Gemini 2.5 Flash Image).

Step 2: Create Ads from Product Photos

1. Upload a product image.

Use a prompt like:

“Create ads to fit various environments with the attached image - subway, train station, bus stop, luxury magazine.”

Gemini 2.5 Nano Banana places your product into real-looking ad spaces with proper lighting and perspective.

Step 3: Transform Sketches into Color Art

1. Upload a black-and-white sketch.
2. Nano Banana keeps the character structure intact while adding clean, detailed color.

Step 4: Turn Images into Videos

1. Toggle Create videos with Veo. This feature converts your generated images into animated clips with sound in about two minutes.
2. Pro Tip: Test Campaigns Before Spending Big

Run quick mockups across different platforms. At $0.04 per image via API, you can test multiple ideas on social media to see which resonates. Use the results to decide where to invest in full production.

Create Ads with Product Photos

Here’s how you can generate ad mockups:

1. Upload your product photo.
2. Type a clear prompt, such as:

Create ads to fit various environments with the attached image - subway, train station, bus stop, luxury magazine.

1. The model places your product into realistic ad environments. It adjusts lighting, perspective, and shadows automatically.

You can generate multiple settings in one run. This makes it easy to preview how your product looks in outdoor billboards, indoor displays, and print media without hiring a design team.

Why It Helps

Brands usually spend thousands on professional mockups before launching. With Gemini 2.5 Nano Banana, you can test ten different ideas for less than a single coffee. Share these mockups with your team or test them on social media to see what your audience reacts to.

Transform Sketches into Colored Art

Nano Banana also works with sketches.

1. Upload a black-and-white drawing.
2. Add a prompt like:

“Colorize this sketch while keeping the original character structure.”

1. The model fills in the color while keeping lines and proportions accurate.

Artists can use this to move quickly from concept art to polished visuals. If you’re a designer, you can sketch by hand, snap a photo, and instantly have a digital colored version.

Pro Tip: Run Campaign Tests

Instead of guessing what ads will work, mock up entire campaigns first. Create different versions of your product in multiple environments, post them on your channels, and track engagement. Since each image costs only about 4 cents, you can run 100 variations for the price of one professional shoot.

This approach improves decision-making. You spend money only on ideas that already show results.

Industry View

Sam Altman, CEO of OpenAI, once said: “The cost of creativity is falling faster than anyone expected.” Google’s Gemini 2.5 Nano Banana is proof of that trend. Tools that once cost thousands are now within reach of individual creators.

FAQ

1. Do I need design skills to use Nano Banana?
   No. Upload your photo or sketch, write a simple prompt, and the model does the rest.
2. Can I use the generated ads commercially?
   Yes, but check Google’s Gemini 2.5 latest terms of service for commercial rights.
3. How long does it take to generate an image?
   Usually less than 30 seconds.
4. Does it support batch processing?
   Yes. Through the API, you can upload multiple prompts and images at once.
5. What file formats are supported?
   JPEG and PNG for uploads; PNG and MP4 for outputs.

Conclusion

Nano Banana (Gemini 2.5 Flash Image) makes ad testing, sketch coloring, and animation creation fast and affordable. With a few clicks, you can create visuals that once required a full studio. For marketers, designers, and small business owners, it’s a simple way to test big ideas without big costs.

Why Promotional Items Are Important

Promotional items provide an important link between the event attendees and your brand, creating a link with guests at each interaction. A clever giveaway can instantly communicate the essence of your brand while simultaneously providing genuine utility to recipients. But most importantly, promotional items help your organization stand out from competitors while reinforcing your presence meaningfully and personally.

Choosing the Right Items

Not all promotional items are equal. You will need to choose items with unique and useful attributes to maximize their return on investment. Portable chargers, reusable water bottles and eco-friendly tote bags all resonate well with audiences as they align with current trends while serving an important purpose. Your items should speak volumes about who your brand is. Tech startups might distribute screen cleaners or USB drives branded with their industry expertise. Skincare companies could find success offering mini samples or travel-sized products as part of their product distribution strategy. It's important to choose products that not only suit their function but are also in alignment with your message and values.

The Power of Add-Ons

Promotional add-on items, like quality books and circle stickers, play a big role in leaving an amazing impression at events tables. While these small items may seem inconsequential at first, their value extends far beyond just an event itself. Quality books serve as tangible “gifts” which reinforce your brand as knowledgeable and resourceful. Or when you give visually appealing giveaways like circle stickers to boost your brand recognition among potential customers. A selection of add-on items shows your attention to detail while creating tangible relationships between potential customers and your brand.

Build Brand Recall

Promotional items become mini ambassadors of your brand. A well-planned giveaway can spark curiosity among the attendees and keep this spark at the top of their mind throughout their attendance at the event. Items with high usability, such as pens, mugs or phone stands, are especially great because they seamlessly fit into the recipients' daily lives and remind them of your brand without having to exert extra effort in doing so. However, it doesn't stop with branding an item with your logo. Adding a humorous tagline can transform it from generic to memorable. Humor and innovation should be your allies here. An amusing or unique slogan or design creates a conversation starter among attendees and can encourage them to share their experience with others.

Conclusion

Promotional items might seem like small details when planning an event, but their impact is far-reaching. From drawing attention and providing value to making good impressions long after an event has concluded, promotional items make a statement about your brand that guests won't soon forget. With careful selection, creativity, and quality in mind, promotional giveaways can transform an ordinary table into something amazing. Never underestimate their significance when it comes to making a great first impression at your next gathering.