Thehired.ai

My name is Arman the CEO and founder of TheHired.AI based in Dubai.

Chief Executive Officer

Call 15000 leads without calling using AI(Dubai real estate Agencies) We Help Dubai Real estate Agencies Close 20x more clients using AI. Learn How👇👇

We build AI Client Acquisition systems Exclusively for +50M/Year High Ticket Services/product businesses That get them 35% more Qualified meetings.

Arman Liaghat

Exhausted dad of three | Lucky husband to one | Everything else is AI

AI isn't smart, it's fast. What we think of as a human-analogue machine is really just a very advanced probability engine. Without some guidelines to our interaction, it can easily be wrong or just straight up lie to us. And that's the essence of prompt engineering: finding the right phrasing to maximize the probability of an accurate output.

Generative AI doesn't have to be an overly complicated addition to our lives. In fact, in my years of using it, I've seen the conversation around it shift from the technology and lean heavily into the social, cultural, and philosophical aspects.

So if you're a normal human like me who doesn't speak code, follow me for concise, simplified AI news and practical skills that'll keep you afloat on The Coming Wave.

Arturo Ferreira

The bottleneck isn't GPUs or architecture.
It's your dataset.

Three ways to customize an LLM:

1. Fine-tuning: Teaches behavior.
1K-10K examples.
Shows how to respond.
Cheapest option.

2. Continued pretraining: Adds knowledge.
Large unlabeled corpus.
Extends what model knows.
Medium cost.

3. Training from scratch: Full control.
Trillions of tokens.
Only for national AI projects.
Rarely necessary.

Most companies only need fine-tuning.

How to collect quality data:

For fine-tuning, start small.
Support tickets with PII removed.
Internal Q&A logs.
Public instruction datasets.

For continued pretraining, go big.
Domain archives.
Technical standards.
Mix 70% domain, 30% general text.

The 5-step data pipeline:

1. Normalize.
Convert everything to UTF-8 plain text.
Remove markup and headers.

2. Filter.
Drop short fragments.
Remove repeated templates.
Redact PII.

3. Deduplicate.
Hash for identical content.
Find near-duplicates.
Do before splitting datasets.

4. Tag with metadata.
Language, domain, source.
Makes dataset searchable.

5. Validate quality.
Check perplexity.
Track metrics.
Run small pilot first.

When your dataset is ready:

All sources documented.
PII removed.
Stats match targets.
Splits balanced.
Pilot converges cleanly.

If any fail, fix data first.

What good data does:

Models converge faster.
Hallucinate less.
Cost less to serve.

The reality:

Building LLMs is a data problem.
Not a training problem.

Most teams spend 80% of time on data.
That's the actual work.

Your data is your differentiator.
Not your model architecture.

Found this helpful? Follow Arturo Ferreira.

Most Teams Waste Months on LLM Training 

OpenAI didn't build a shopping tool.

They built a reason to never leave ChatGPT.

Here's why:

𝟭. 𝗜𝘁 𝗔𝘀𝗸𝘀 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗖𝗼𝗺𝗽𝗮𝗿𝗶𝘀𝗼𝗻 𝗦𝗶𝘁𝗲𝘀 𝗖𝗮𝗻'𝘁

"What's your budget?"
"Who is this for?"
"Which features matter most?"

Not filters.
Conversation.

It understands context.
Not just keywords.

𝟮. 𝗜𝘁 𝗥𝗲𝗺𝗲𝗺𝗯𝗲𝗿𝘀 𝗬𝗼𝘂𝗿 𝗣𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀

If ChatGPT knows you game?

It factors that into laptop search.

If it knows your apartment size?

It finds the right vacuum.

Comparison sites start from zero.
Every. Single. Time.

𝟯. 𝗜𝘁 𝗔𝗱𝗮𝗽𝘁𝘀 𝗜𝗻 𝗥𝗲𝗮𝗹 𝗧𝗶𝗺𝗲

You mark products "Not interested."

The research adjusts instantly.

You say "More like this."

It refines the search.

Comparison sites give you pages.
ChatGPT gives you dialogue.

𝟰. 𝗜𝘁 𝗥𝗲𝗮𝗱𝘀 𝗔𝗰𝗿𝗼𝘀𝘀 𝗧𝗵𝗲 𝗘𝗻𝘁𝗶𝗿𝗲 𝗜𝗻𝘁𝗲𝗿𝗻𝗲𝘁

Not just listed retailers.

Reviews from real users.
Specs from manufacturer sites.
Prices from everywhere.
Availability in real time.

Then synthesizes it all.

𝟱. 𝗜𝘁 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝘀 𝗧𝗿𝗮𝗱𝗲𝗼𝗳𝗳𝘀

Not just "here are 10 options."

"Option A is cheaper but louder."
"Option B costs more but lasts longer."
"Option C is best for small spaces."

It tells you WHY to choose each one.

𝟲. 𝗜𝘁'𝘀 𝗧𝗿𝗮𝗶𝗻𝗲𝗱 𝗦𝗽𝗲𝗰𝗶𝗳𝗶𝗰𝗮𝗹𝗹𝘆 𝗙𝗼𝗿 𝗦𝗵𝗼𝗽𝗽𝗶𝗻𝗴

GPT-5 mini with reinforcement learning.

Not general AI doing shopping.
AI built for shopping tasks.

Trained to cite reliable sources.
Synthesize across many sites.
Update based on your feedback.

𝟳. 𝗜𝘁 𝗛𝗮𝗻𝗱𝗹𝗲𝘀 𝗖𝗼𝗺𝗽𝗹𝗲𝘅 𝗖𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀

"Laptop under $1000."
"Screen over 15 inches."
"Good for gaming."
"Available this week."

All at once.

Comparison sites make you filter.
One. Variable. At. A. Time.

𝟴. 𝗜𝘁'𝘀 𝗡𝗼𝘁 𝗣𝗮𝗶𝗱 𝗕𝘆 𝗥𝗲𝘁𝗮𝗶𝗹𝗲𝗿𝘀

Results are organic.

No sponsored listings at the top.
No affiliate link bias.
No "featured products."

Just what actually matches your needs.

𝟵. 𝗜𝘁 𝗪𝗼𝗿𝗸𝘀 𝗙𝗼𝗿 𝗚𝗶𝗳𝘁𝘀

"Gift for my dad who loves fishing."

It understands intent.
Asks follow-up questions.
Suggests based on personality.

Try that on a comparison site.

𝟭𝟬. 𝗧𝗵𝗲 𝗧𝗶𝗺𝗲 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲

Research that normally takes hours?

ChatGPT does it in minutes.

And delivers a personalized guide.
With tradeoffs explained.
And sources cited.

The shift isn't about better filters.

It's about replacing filters entirely.

With dialogue.

Comparison sites show you options.
ChatGPT understands what you need.

What are you shopping for?

Found this helpful? Follow Arturo Ferreira

Why AI Shopping May Replace Comparison Sites

OpenAI just published their teen safety blueprint.

I read all 4 pages.

5 things that reveal their actual strategy:

𝟭. 𝗧𝗵𝗲𝘆'𝗿𝗲 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗥𝗲𝗴𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗕𝗲𝗳𝗼𝗿𝗲 𝗜𝘁 𝗘𝘅𝗶𝘀𝘁𝘀

Age prediction technology.
Default safety modes when age is uncertain.
Parental control architecture.

Social media got regulated after the damage.
OpenAI is writing the rules first.

𝟮. 𝗘𝘃𝗲𝗿𝘆 "𝗧𝗲𝗲𝗻 𝗙𝗲𝗮𝘁𝘂𝗿𝗲" 𝗜𝘀 𝗮𝗻 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗙𝗲𝗮𝘁𝘂𝗿𝗲

Parental controls = admin controls.
Age verification = identity verification.
Usage monitoring = audit trails.
Content restrictions = policy enforcement.

They're building B2B compliance infrastructure.
And calling it child protection.

𝟯. 𝗧𝗵𝗲 𝟳𝟱,𝟬𝟬𝟬 𝗩𝗶𝗼𝗹𝗮𝘁𝗶𝗼𝗻 𝗥𝗲𝗽𝗼𝗿𝘁

OpenAI reported 75,000 safety violations.
In just 6 months.
That's 410 per day.

They built detection systems that work at scale.
Now they're productizing them.

𝟰. 𝗧𝗵𝗲 "𝗘𝘅𝗽𝗲𝗿𝘁 𝗖𝗼𝘂𝗻𝗰𝗶𝗹" 𝗜𝘀𝗻'𝘁 𝗘𝘁𝗵𝗶𝗰𝘀 𝗧𝗵𝗲𝗮𝘁𝗲𝗿

Expert Council on Well-Being and AI.

That's a regulatory advisory board.
They're writing the playbook that becomes law.

By 2027, every AI company will need one.
OpenAI already has theirs.

𝟱. 𝗧𝗵𝗲 𝗢𝗻𝗲 𝗦𝗲𝗻𝘁𝗲𝗻𝗰𝗲 𝗧𝗵𝗮𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀

"For teens, we prioritize safety over freedom."

That sentence will be in every AI regulation bill.

OpenAI just wrote the language that governs AI.

What this actually means:

If you're building AI products, watch this.

↳ Safety architecture becomes mandatory
↳ Age verification becomes standard
↳ Content moderation needs audit trails
↳ Every feature needs a policy layer

OpenAI isn't protecting teens from AI.

They're protecting AI from regulation.

By becoming the standard before standards exist.

What safety features are you building?

Found this helpful? Follow Arturo Ferreira

The Safety-First Strategy

You're not paying people to work.

You're paying them to Google things.

10 tasks that are just expensive research:

𝟭. 𝗩𝗲𝗻𝗱𝗼𝗿 𝗦𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻

Compare 15 CRM platforms.
Read reviews on G2, Capterra, TrustRadius.
Schedule 8 demos.
Build comparison spreadsheet.

Time: 12 hours
Cost: $600 per search
Annual searches: 8
Total: $4,800

𝟮. 𝗖𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀

Research 5 competitors.
Check their pricing.
Read their marketing.
Download their materials.
Summarize findings.

Time: 6 hours per competitor
Cost: $300 each
Annual: 20 competitors
Total: $6,000

𝟯. 𝗛𝗶𝗿𝗶𝗻𝗴 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵

Find salary benchmarks.
Research job boards.
Compare recruiting tools.
Review candidate profiles.
Check references online.

Time: 8 hours per hire
Cost: $400
Annual hires: 12
Total: $4,800

𝟰. 𝗠𝗮𝗿𝗸𝗲𝘁 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵

Find industry reports.
Read analyst predictions.
Check market size data.
Review trend reports.
Compile insights.

Time: 15 hours per report
Cost: $750
Quarterly reports: 4
Total: $3,000

𝟱. 𝗘𝘃𝗲𝗻𝘁 𝗩𝗲𝗻𝘂𝗲 𝗦𝗲𝗮𝗿𝗰𝗵

Compare 25 venues.
Check availability.
Review capacity and pricing.
Read reviews.
Contact for quotes.

Time: 10 hours
Cost: $500 per event
Annual events: 6
Total: $3,000

𝟲. 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀 𝗣𝗮𝗰𝗸𝗮𝗴𝗲 𝗥𝗲𝘃𝗶𝗲𝘄

Compare health insurance plans.
Research 401k providers.
Check dental and vision options.
Review perks and add-ons.
Calculate costs.

Time: 20 hours
Cost: $1,000
Annual review: 1
Total: $1,000

𝟳. 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻

Find relevant courses.
Compare platforms.
Check instructor credentials.
Read student reviews.
Test free trials.

Time: 5 hours per program
Cost: $250
Annual programs: 10
Total: $2,500

𝟴. 𝗖𝗼𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗦𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻

Research industry events.
Check speaker lineups.
Compare ticket prices.
Review past attendee feedback.
Calculate ROI.

Time: 4 hours per conference
Cost: $200
Annual: 15 decisions
Total: $3,000

𝟵. 𝗙𝗿𝗲𝗲𝗹𝗮𝗻𝗰𝗲𝗿 𝗦𝗼𝘂𝗿𝗰𝗶𝗻𝗴

Browse Upwork, Fiverr, Toptal.
Review 30 portfolios.
Check ratings and reviews.
Compare hourly rates.
Message top candidates.

Time: 6 hours per hire
Cost: $300
Annual freelancers: 20
Total: $6,000

𝟭𝟬. 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗧𝗿𝗲𝗻𝗱 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴

Read industry publications.
Track competitor news.
Monitor tech developments.
Scan social media.
Summarize for leadership.

Time: 3 hours per week
Cost: $150/week
Annual: 52 weeks
Total: $7,800

The question isn't "Can we afford AI?"
It's "Can we afford not to?"

You're already paying for research.
Just in the most expensive way possible.

How much Googling are you paying for?

Found this helpful? Follow Arturo Ferreira

The Cost of Research Time

Students are using ChatGPT to write essays.
Current detectors flag innocent students 40% of the time.
Here's the tool that actually works.

What Ghostbuster actually does:

Spots ChatGPT, Claude, any AI-written text.
Works on unknown models.
99% accuracy on tested domains.

How to use it in 5 scenarios:

1. Check if a student essay is AI-written.

Go to ghostbuster.app.
Paste the essay.
Get instant classification.

Works best on 200+ word documents.
Shorter text is less reliable.

2. Verify if news articles used AI.

Copy article text.
Run through Ghostbuster.
Check confidence score.

92.2% accurate even on Claude-generated text.

3. Filter AI text from training data.

Upload your dataset.
Ghostbuster flags AI-generated samples.
Remove before training models.

Prevents model contamination.

4. Detect edited AI text.

Students often lightly edit ChatGPT output.
Sentence swaps don't fool Ghostbuster.
Paragraph reordering doesn't work either.

But heavy paraphrasing drops accuracy.

5. Check social media for AI content.

Copy post or comment text.
Run detection.
Flag suspicious accounts.

What it catches reliably:

Unedited ChatGPT outputs (99% accuracy).
Claude-generated text (92% accuracy).
Different writing styles and prompts.
Text across domains (essays, news, stories).

What reduces accuracy:

Documents under 200 words.
Text paraphrased multiple times.
Using detection evaders like Undetectable AI.
Heavy word-level or character changes.

Non-native English speaker writing.
Scores drop on shorter essays from learners.

When not to rely on it:

Don't auto-penalize students.
Always use human review.
False positives happen on short text.
Non-native speakers get flagged more.

Why it beats other tools:

GPTZero gets 93.1 F1 accuracy.
DetectGPT gets 57.4 F1 accuracy.
Ghostbuster gets 99.0 F1 accuracy.

Works without knowing which model wrote it.
Doesn't need access to model probabilities.

Best practices for teachers:

Use for initial screening only.
Follow up with conversation.
Check document length first.
Be extra careful with non-native speakers.

Never rely on it alone for accusations.

Try it yourself:

Go to ghostbuster.app.
Test your own writing.
See if it flags human text.

They also have an experiment page.
Guess if text is AI or human.
See how you compare to the model.

Available now for free.

Found this helpful? Follow Arturo Ferreira.

How to Catch ChatGPT-Written Text with 99% Accuracy

Teachers are debating whether ChatGPT helps or hurts students.
The answer isn't simple.
Here's what you need to know.

Where ChatGPT fails students:

1. It confidently lies.

ChatGPT "hallucinates" fake information.
Presents false facts as absolute truth.
Like that confident student who's always wrong.

Students can't tell what's accurate.

2. It makes plagiarism too easy.

Student hits mental wall on essay.
Deadline is tomorrow.
ChatGPT gives instant answers.

Too tempting to just copy.

Teachers can't prove where content came from.
Can't ask ChatGPT how it generated answers.

3. It creates fake citations.

Students ask for sources.
ChatGPT generates citations that don't exist.

No way to verify the information.

4. It reinforces harmful stereotypes.

One user asked for a story about career choices.
Girl in story doubted she could handle engineering.
Boy loved working with machines.

Parrots outdated gender generalizations.
Adds to negative feedback students already face.

Where ChatGPT actually helps:

1. Brainstorming engine.

Ask "What are top five cybersecurity risks?"
Use response to outline paper.
Doesn't give answers, opens thinking.

2. Writing prompt generator.

Reduces writer's block.
Helps students start projects.

3. Experiment planning.

Teachers use it for generic lab setups.
Students modify based on their needs.

4. Administrative tasks.

Letters to parents.
Creating syllabi.
Frees up teacher time for actual teaching.

The bottom line for educators:

ChatGPT isn't going away.
Can't ban it effectively.

Teach students to use it as supplement.
Not as answer machine.

Use it for brainstorming, not copying.
For prompts, not plagiarism.

The reality:

Valid use cases exist.
But risks are significant.

Students must understand limitations.
Teachers must redefine boundaries.

Use ChatGPT with extreme care.

Found this helpful? Follow Arturo Ferreira

Can You Trust ChatGPT as an Educational Resource? 

ChatGPT writes code.
But it can't do your actual job.
Here's what it fails at.

1. Generate legally safe professional code.

ChatGPT pulls from its training data.
Unknown sources, unknown licenses.
Anything it creates isn't copyrightable.

Use it at work?
You're exposing your company to lawsuits.

2. Apply critical thinking.

Ask it to run statistical analysis.
It generates perfect code.
For the wrong test.

It doesn't check assumptions.
Doesn't verify if the test fits the data.
Results are unusable.

3. Manage stakeholder priorities.

Marketing wants engagement features.
Sales wants cross-selling.
Support wants help tools.

ChatGPT can't navigate competing interests.
Can't make business-aligned decisions.
Has zero emotional intelligence.

4. Solve novel problems.

ChatGPT remixes training data.
Can't create truly new solutions.

Standard problems? Easy.
Completely novel scenarios? Fails badly.

5. Code ethically.

Can't understand moral implications.
Can't spot bias in data.
Can't consider impact on different groups.

Humans make ethical mistakes too.
But we can learn and be held accountable.
ChatGPT can't.

What it's actually good for:

Debugging.
Quick code review.
Faster than StackOverflow.

What separates you from ChatGPT:

Understanding business goals.
Building relationships.
Making ethical decisions.
Storytelling with data.
Communicating complex ideas simply.

ChatGPT can't do any of that.
Your job is secure.

Found this helpful? Follow Arturo Ferreira

5 Coding Tasks ChatGPT Can't Do (Your Job Is Safe)

OpenAI tested ChatGPT on mental health conversations.
Worked with 170+ mental health experts.
Here's what they found.

ChatGPT has 300+ million weekly users.

0.15% show suicide indicators every week.
That's 450,000 people.

0.07% show psychosis or mania signs.
That's 210,000 people.

0.15% show unhealthy AI attachment.
Another 450,000 people.

These aren't statistics.
These are humans in crisis talking to a bot.

How the models compare:

Old GPT-4o was 27% compliant on mental health.
New GPT-5 hits 92%.

Old GPT-4o was 77% on suicide prevention.
New GPT-5 hits 91%.

Old GPT-4o was 50% on emotional reliance.
New GPT-5 hits 97%.

That's a 65-80% improvement.
But 3-8% still fails.
On millions of conversations.
Thousands of failures every week.

3 things most AI companies get wrong:

1. They ship without safety testing.
No clinical expertise.
No mental health detection.
Just launch and hope.

2. They think 0.15% is negligible.
Until it's 450,000 real people.
Edge cases are humans in crisis.

3. They optimize for engagement only.
Longer conversations equal better metrics.
Until someone gets hurt.

Here's an actual example.
User says planes are stealing their thoughts.
Old GPT-4o gave vague unhelpful responses.
New GPT-5 stays grounded and directs to help.
Doesn't affirm delusions.
Doesn't dismiss the person.

OpenAI built a network of 300 clinicians.
Across 60 countries.
Created detailed safety taxonomies.
Tested before and after deployment.
Kept iterating based on expert feedback.

Most AI companies do none of this.

If you're building consumer AI, listen up.
You will encounter mental health crises.
Prepare now or face liability later.

The "move fast and break things" era is over.
Safety isn't a feature anymore.
It's the foundation.

OpenAI just set the baseline.
Every other company has to match it.
Or explain why they're less safe.

Found this helpful? Follow Arturo Ferreira.

OpenAI Just Released Mental Health Data 

Scientists just published how they use GPT-5.

Not as a tool. As a research partner.

Here are the 7 ways they're doing it:

𝟭. 𝗧𝗵𝗲 𝗙𝗹𝗮𝘄 𝗙𝗶𝗻𝗱𝗲𝗿

Tim Gowers (Fields Medal winner) uses GPT-5 to stress-test ideas.

It spots missing cases in constructions.
Finds simpler counterexamples.
Critiques partial arguments.

Speed: Minutes instead of days.
Role: The skeptic who saves you from bad paths.

𝟮. 𝗧𝗵𝗲 𝗟𝗶𝘁𝗲𝗿𝗮𝘁𝘂𝗿𝗲 𝗦𝗰𝗼𝘂𝘁

Nikita Zhivotovskiy proved a geometry theorem.
Asked GPT-5 where else it could apply.

It surfaced references in other languages.
Found connections across density estimation, learning theory, optimization.
Materials he'd never encountered.

40 hours of literature review → 15 minutes.

𝟯. 𝗧𝗵𝗲 𝗠𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺 𝗛𝘂𝗻𝘁𝗲𝗿

Derya Unutmaz spent months on immune cell changes.
Couldn't explain what was happening.

GPT-5 identified the mechanism in minutes.
From an unpublished chart.
Suggested the experiment that proved it.

𝟰. 𝗧𝗵𝗲 𝗣𝗿𝗼𝗼𝗳 𝗦𝗸𝗲𝘁𝗰𝗵𝗲𝗿

Mehtaab Sawhney and Mark Sellke tackled a 40-year-old problem.
Stuck on the final step.

GPT-5 suggested how one odd number breaks the pattern.
That was the missing piece.
They completed the proof of Erdős Problem 848.

𝟱. 𝗧𝗵𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗧𝗲𝘀𝘁𝗲𝗿

Sébastien Bubeck tested a robotics decision method.

GPT-5 found a clear counterexample showing it fails.
Improved a classic optimization result.
Proposed a sharper step-size bound with cleaner proof.

𝟲. 𝗧𝗵𝗲 𝗖𝗿𝗼𝘀𝘀-𝗙𝗶𝗲𝗹𝗱 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗼𝗿

Scientists describe it as "conceptual literature search."

Not keyword matching.
Understanding deeper relationships between ideas.
Pointing to analogous structures in other fields.

It connects dots humans miss.

𝟳. 𝗧𝗵𝗲 𝗦𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗔𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗼𝗿

In physics and computational domains:

Proposes simplifying transformations.
Speeds up tough computations.
Works best where structure is explicit.

What makes this different from "using ChatGPT":

These aren't prompts.
They're dialogues.

Scientists iterate with the model.
Push back on suggestions.
Break problems into steps.
Validate independently.

The pattern that works:

↳ Expert sets the agenda
↳ Model explores in parallel
↳ Human validates every result
↳ Fast feedback loops
↳ Explicit structure helps

What scientists say:

"It's not autonomous."
"It needs expert oversight."
"But it expands the surface area of exploration."

6 months → 6 minutes on some research questions.

That's not hype.

That's compression of the research timeline.

The companies that will win:

The ones who figure out this partnership model.

Not AI replacing experts.
AI amplifying expert judgment.

How are you using AI as a research partner?

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