Audr

Fractional CAIO (Chief AI Officer)

JobGenie

Artificial Intelligence Research & Development Agency creating AI products focused on social impact.

jobgenie.ai
doineedai.com
ai4families.com

Founder / Creative Director 

jobworx.ai

Strategic AI Consultant

Breakthru Analytics

Web and mobile app development and automation for small businesses and freelance clients.

Founder

Cleverman Inc.

- NLP Specialist
- Built and deployed recommendation systems

Data Scientist

Hispanic Counseling Center

Youth Counselor

CAIO, AI Advisor & Founder | Building AI-First companies & sharing best practices

I’m a Fractional CAIO, Advisor and the Founder of JobGenie - an AI research and development company focused on using AI for social impact.

Our portfolio includes:

1- Do I Need AI?: one-day workshop that identifies your business’s AI opportunities and risks, crafts tailored action plans and gets your team using AI confidently. (Clients varied across 7 industries and 3 countries - Featured in Forbes)

2- AI For Families: AI & social media safety resources to help families navigate technology responsibly. (Available for individuals + nonprofits + schools)

3- Ayuda Rapida: A free people-first, gig-economy marketplace making it easy to find and share local services. (In Puerto Rico)

4- JobGenie v1 ( Job Application Agent): One of the first AI agents for job applications. Launched in 2021, enabling users to apply to 100 jobs with a single click.

5- Symptom Diagnostic AI: 24/7 medical assistant tool for assessing the severity of menopause and postpartum symptoms.

I've also developed data-driven frameworks guiding top PE/VC firms in investment decisions and advised startups on high-impact AI adoption strategies to achieve results with limited resources.

I'm an AI generalist passionate about clear thinking, meaningful frameworks and using data to create positive impact.

If you want to work with me in any capacity send me a DM or check out: jobgenie.ai

Google just leap frogged the AI world. 

Here's what you need to know:

For the first time in years, Google isn’t playing catching up.

They’re ahead.

Gemini 3 is beating GPT-5-level systems in reasoning, math, multimodal work, tool use.... the whole package.

But the real threat isn’t the model.

It’s Google’s distribution.

Gemini goes straight into Gmail, Calendar, Maps, Drive, Android, YouTube…

The layer billions of people can touch every day without switching apps.

Add in:
- Gemini Enterprise (a real end-to-end enterprise AI platform)
- Antigravity (free agent-dev environment)
- Ironwood TPU (24× the compute of the world’s largest supercomputer)

…and Google suddenly owns the model + compute + ecosystem story.

Meanwhile Microsoft is rolling out AI employees with emails, Teams access, and HR profiles. 

A full agentic workforce.

The enterprise OS is shifting fast.

OpenAI’s GPT-5.1 update?
Good but safe.
“Safe” is dangerous these days

We’re entering a new era:
Ecosystems > chatbots.
Distribution > intelligence.
Platforms > products.

The game is always changing. This is the next shift.

Check out full breakdown in my substack. 
(Link in comments)

Jared Bonilla

leohomeonline

jobgenie

breakthru-analytics

Talks about #startup, #jobsearch, #datascience, #entrepreneurship, and #productmanagement

Data leverage for the AI & Attention Economy

Distribution is the new MOAT and the smartest companies are jumping on it.

Most companies are all consumed with AI right now.

“How can I use it operationally” 
”How can I use it to benefit my customers”

Operationally = necessary
Customer-facing = potential benefit but risky

Both = can be replicated by competitors in weeks
(Unless you have proprietary data = the other AI era MOAT)

Competitive advantages are things that can’t be easily replicated.

So what can’t be replicated when customer-facing features and operational workflows can?

Attention. Audience. Mindshare. Trust.
(Being top of mind in a good sense)

How does a company build that?
Authentic media.

That’s why these legacy orgs are launching content studios.

Building attention engines to make sure they stay top of mind.

Owning the conversation or narrative that leads to sales.

In the AI era, distribution isn’t just posting content, it is:
- where you show up
- how often you show up
- why people recognize you when you do
- how people feel when they see/hear you
- how consistently people choose to listen

Distribution is more than just reach. 
It’s relationship building at scale.

The companies that win won’t just automate faster.

They’ll communicate deeper through stories and experiences.

Ecosystems that people actually want to be part of.

A new AI feature might help you get seen.
but distribution is what makes sure you’re not forgotten.

PS - I dive deep into this + how to start building a distribution engine for your company in my substack. Link in comments.

Data-driven growth strategies for startups in an AI era | building 🇵🇷 gig economy

An investor changed my year plan without writing a check.

I started ARPR because service discovery in Puerto Rico is broken.

Talent exists but opportunities are hidden.

Phonebook-era infrastructure is holding people back.

So I started with distribution.
Built an MVP. Validated with users.
Began looking for funding.

The first investor I spoke to looked at me and said:
“You’re under-leveraging your superpower — 6+ years in data science & AI, 4 Forbes mentions, VC/PE/billion-dollar clients. Fund ARPR through the thing you already have credibility for.”

That hit harder than I expected.

Later that night I saw Alex Lieberman say he was building the McKinsey of AI.

And something clicked:
Why not build the BCG of the AI & Attention Economy and use it to fund Puerto Rico’s future?

Because AI + attention all come back to one thing: data.

And the data is clear. 

Companies are jumping into:
AI implementation without understanding their business data. 
Marketing campaigns without understanding audience data. 

Leadership teams guessing instead of measuring.

This is the gap I’ve spent years helping companies close.

So here's the new plan:

Acho Labs → turns business data into AI & attention advantage.

Ayuda Rápida PR → turns that advantage into opportunities for Puerto Ricans.

Two sides of the same mission:
- Use data + distribution to create opportunity.
- The consulting funds the infrastructure.
- The infrastructure lifts the island.
- Both compound each other.

The BCG of the AI & Attention Economy.
Built to benefit an island that's been neglected.

(And yes, we’ll eventually invest in the companies we help.)

I’ve worked with 2 failed AI startups.

Here are 8 lessons and how to avoid them:

1- They chased “AI” instead of a painful problem.
↳ Tech isn’t a moat. Urgency is.

2- They had great products with weak distribution.
↳ Distribution = positioning + consistent execution.
↳ Not random posts and sales calls.

3- They chased the ICP they wanted, not the one they could best serve.
↳ Build for who needs you most, not who you wish would.

4- They assumed what worked before would work again.
↳ Past wins don’t transfer to the AI-era
↳ New market = new rules + new strategies

5- They built features, not a category.
↳ If people can’t easily explain what you are, you’re in trouble.

6- They focused on looking valuable instead of being valuable.
↳ Brand beauty is nothing without real impact.

7- They confused curiosity for traction.
↳ Look for customer obsession, not customer curiosity.

8- They relied on AI as differentiation.
↳ Everyone has AI now. 
↳ Emotional resonance + distribution wins.

Despite the failures, it was among the most valuable experiences I’ve had.

I worked alongside and met truly brilliant people.
Some of the sharpest minds I’ve ever met.

I studied why 8/10 venture-funded businesses fail and it shaped how I build today.

That research didn’t just make me confident...it made me prepared.

Prepared to build better and move smarter.

Prepared to avoid the mistakes I watched firsthand.

I’ve also worked with many startups who are dominating their industries. 

I’ll share a list on what’s lessons I’ve learned from them next. 

Do any of these lessons resonate with you?

“Does a startup’s name matter?”

I hear this all the time.

I recently heard a VC say it’s extremely important.

But the data says it’s more nuanced than a simple yes-or-no.

Here’s what research actually shows:

1- Easy to pronounce → better outcomes

- Higher linguistic & phonetic fluency increases funding odds

- Chan et al. showed even a small decrease in fluency reduced funding probability

2- Unusual names → higher early attention

- Distinctiveness drives memorability, media mentions & curiosity spikes

- Novelty is especially helpful in early-stage recognition

3- Short names → stronger long-term adoption

- MIT research found companies with shorter names grew faster

- Fewer syllables = easier recall, sharing & global transferability

This is why category leaders sound nothing like their product:

Google, Amazon, Yahoo, Spotify, TikTok, Wix, Canva

Here’s a useful naming framework:
(I call it SOAR)

-Short → under 10 characters or 2 syllables
-Obvious pronunciation → no phonetic guessing
-Attention-getting → not generic or bland
-Repeatable → others can share it easily

If it doesn’t SOAR, it might not scale.

Hot take 🌶️
AI writes better prompts than most people

It can also ask questions to improve them 

Conclusion = use AI to write prompts

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