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2025 Was the Year AI Teams Got Real

What we learned building AI teams across the Americas, and what we’re taking into 2026

If 2023 was the year generative AI went mainstream, and 2024 was the year companies experimented, then 2025 was the year the industry had to grow up.

This was the year AI moved from “innovation theater” to operational reality. The year AI budgets stopped being a line item and became a mandate. The year leaders stopped asking “Should we do AI?” and started asking “How do we hire the people who can actually deliver it?”

At Tesoro AI, we felt that shift in the most concrete place possible: hiring conversations. The questions changed. The timelines tightened. The definition of “qualified” became sharper. And for the first time, “talent strategy” started to look less like recruiting and more like competitive advantage.

This is our 2025 wrap-up, but it’s not a highlight reel. It’s a set of lessons and signals we believe will matter in 2026 for anyone building AI teams in the Americas.

The biggest transformation in 2025: execution replaced experimentation

In early 2024, many companies were still testing AI in safe, contained ways: internal chatbots, basic automations, a few pilots in customer support. In 2025, the tone changed.

AI became tied to core outcomes: revenue protection, cost compression, fraud reduction, faster fulfillment, better underwriting, smarter forecasts. The conversation moved from “demo quality” to “production reliability.”

What that meant in hiring:

  • Companies stopped hiring “AI generalists” and started hiring operators.
  • Job descriptions shifted from “ML experience preferred” to “ship models, integrate into systems, monitor performance.”
  • Teams became more cross-functional, because AI is not a model. It’s a workflow.

Lesson: The winners aren’t the companies with the most AI pilots. They’re the ones with repeatable execution muscle: data foundations, deployment discipline, and talent that can work end-to-end.

What we heard most from CTOs and hiring leaders in 2025

Across conferences, client calls, and hiring pipelines, five themes kept repeating.

1) “We need speed, but we can’t compromise quality.”

AI roles compressed hiring cycles. Leaders wanted shortlists faster, interviews tighter, and candidates who could contribute quickly. But speed without rigor created risk.

What we did differently: We leaned harder into real-signal evaluation: live problem-solving, communication under ambiguity, and practical experience with modern AI stacks.

2) “Everyone says they’ve built AI. How do we know it’s real?”

This year exposed a new kind of hiring friction: inflated resumes, AI-generated portfolios, and candidates who could speak in buzzwords but couldn’t explain tradeoffs.

Signal from the field: credibility became a differentiator. Leaders started asking for “proof of work” again, but upgraded for 2025: shipped features, measurable outcomes, evaluated models, real integrations.

3) “We’re not just hiring skills. We’re hiring trust.”

As distributed teams grew, communication and reliability mattered as much as code. The best engineers weren’t just technically strong. They were the ones who created clarity, wrote clean updates, handled feedback, and stayed consistent.

Lesson: soft skills weren’t a bonus in 2025. They were the deciding factor in whether an AI hire succeeded.

4) “We need global talent, but we need global standards.”

Hiring across borders accelerated, and so did the need for standardization: compliance, IP protection, data handling, remote-work expectations, security hygiene.

Lesson: the best distributed teams don’t “figure it out later.” They design the operating system upfront.

5) “Retention is now a KPI, not an afterthought.”

The market tightened. AI talent got more options. Companies that treated LATAM hiring as “cheap and fast” struggled. The companies that treated LATAM talent as core team members grew faster and kept people longer.

Lesson: retention is not HR’s job alone. It’s a leadership practice.

The 2025 AI hiring reality: roles got sharper, not broader

A subtle change happened this year: AI job titles stayed similar, but what companies meant by them became more precise.

Here’s the practical shift we saw:

ML Engineer stopped meaning “build models”

In 2025, ML Engineers were increasingly expected to:

  • integrate with product systems
  • optimize inference costs
  • implement evaluation and monitoring
  • handle data workflows and retrieval patterns
“AI Product” became a real function

AI PMs and AI-forward product leaders gained influence because someone had to translate:

  • business goals into model behavior
  • customer needs into safe UX
  • edge cases into evaluation plans
AI Ops started to emerge as essential

Teams realized: the model is not the work. The work is everything around it:

  • deployment pipelines
  • observability
  • governance
  • regression testing
  • human-in-the-loop workflows

Takeaway for 2026: hiring will increasingly prioritize “workflow builders,” not “model theorists.”

Why LATAM strengthened its position in 2025

2025 made one thing obvious: building world-class AI teams is no longer about one city or one country.

Latin America’s advantage sharpened because it isn’t a single factor. It’s the combination:

  • real-time collaboration with North America
  • strong engineering communities
  • increasing AI tool fluency
  • cultural alignment that reduces friction
  • a growing ecosystem of builders, not just job seekers

But here’s the nuance: LATAM’s edge isn’t automatic. It shows up when companies hire with intention: clear expectations, strong onboarding, and a culture that treats distributed teammates as equals.

This is why Tesoro AI’s approach stayed consistent even as the market changed: we focus on cultural alignment and communication clarity, because that is what makes distributed AI teams durable.

The Tesoro AI playbook we refined in 2025

If you’re building AI teams in 2026, here are the practices we believe matter most, based on what worked this year.

1) Hire for “execution evidence,” not keywords

Ask candidates to show:

  • what shipped
  • what broke
  • what they measured
  • what they improved
  • how they communicated it
2) Evaluate communication like it’s a core skill

Because it is. Great distributed engineers:

  • narrate tradeoffs
  • write crisp updates
  • surface risk early
  • ask good questions
3) Design onboarding as a retention strategy

The first 30 days decide whether talent stays. The best teams:

  • set clear success metrics
  • give a real first project
  • assign a partner/mentor
  • create rhythm (standups, demos, retros)
4) Treat compliance and culture as one system

If your contracts are compliant but your team doesn’t feel included, you still lose.

5) Build the “AI operating system,” not just the AI team

That means documentation, evaluation, deployment habits, and a culture that learns quickly.

What we’re carrying into 2026

If 2025 was the year AI teams got real, then 2026 will be the year AI teams get competitive.

The gap will widen between companies that:

  • treat AI as a featureand companies that
  • treat AI as an operating capability.

At Tesoro AI, we’re entering 2026 focused on one mission: helping teams across the Americas hire talent that doesn’t just look good on paper, but delivers in production and thrives in distributed environments.

A holiday note from Tesoro AI

As the year closes, we want to say thank you to the founders, CTOs, HR leaders, and builders across the Americas who trusted us in 2025. This work is moving fast, but it’s still deeply human. And we’re grateful to be building the future alongside you.

Merry Christmas and Happy Holidays from all of us at Tesoro AI.

We’re rested, focused, and ready to face 2026.

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3 Predictions for AI Talent & Hiring in 2026

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1. The AI/ML Talent Demand Surge Will Continue — Across All Industries

By 2026, demand for AI and ML professionals won’t just be confined to “tech companies” — it will be baked into companies across finance, healthcare, manufacturing, retail, climate tech, and more.

  • Even today, the supply-demand imbalance is already severe: recent industry data shows that globally, demand for AI talent outstrips supply by roughly 3.2 to 1, with over 1.6 million open positions but only about 518,000 qualified candidates.
  • Moreover, in 2024 nearly one in four new tech-job postings explicitly required AI or ML skills — roughly double the share from just two years prior.
  • As companies across sectors double down on AI-powered automation, insights, and compe-titive advantage, this demand will push further upward in 2026.
Implications for hiring leaders and CTOs:
  • Expect a highly competitive job market for AI/ML talent, with long recruiting cycles unless you move fast.
  • Salary inflation: AI roles will continue to command significant premiums over traditional software jobs; companies may need to reevaluate compensation bands, equity, and benefits to attract and retain top talent.
  • Strategic prioritization: AI hiring should become a core component of workforce planning, not just a “nice-to-have.” Firms should budget early and strategically for AI headcount, treating AI teams as essential infrastructure rather than experiments.

This is one of the foundational reasons a firm like Tesoro AI — with expertise in connecting employers to a broad, pre-vetted pool of AI professionals — will remain vital in 2026.

2. AI-Driven Hiring Tools — Especially Candidate Matching — Become Standard in Recruitment Workflows

As adoption of AI expands within organizations, AI won’t just help build products — it will build the teams too. The recruiting process itself will increasingly be powered by AI tools.

  • According to a recent global survey, 88% of companies report using AI in at least one business function, up from 78% the year before.
  • Many of those using AI go beyond pilot phase: roughly a third say they’ve begun scaling AI programs enterprise-wide.
  • As companies scale, AI-driven candidate sourcing, matching, and screening will become especially appealing — allowing them to sift through large applicant pools and quickly surface individuals whose skills and experiences best align with evolving AI needs.
Implications for HR and TA leaders:
  • Faster, more efficient hiring cycles: AI matching tools can dramatically reduce time-to-hire, helping companies secure scarce talent before competitors.
  • Emphasis on skills over pedigree: As AI-driven tools focus on skills, project history, and demonstrable performance rather than just degrees, companies may shift toward more flexible hiring models (bootcamps, remote work, micro-credentials).
  • Need for process redesign: HR teams will need to learn how to integrate and trust AI-driven recruiting tools, and ensure fair, unbiased, and legally compliant candidate evaluation practices.

For talent-acquisition firms like Tesoro AI, this trend underscores the value of curated, skills-focused talent pools — not just résumés — and positions the firm as a bridge between AI-driven hiring platforms and the human intelligence behind top candidates.

The Human Element in an AI-Powered Hiring World

While AI-driven hiring tools are quickly becoming standard—making sourcing, matching, and screening dramatically faster—the future of recruitment won’t be automated. It will be augmented.

Yes, AI will handle the heavy lifting behind the scenes. But the deeper truth is that candidates and hiring managers still want what they’ve always wanted: a genuine human connection.

Candidates want to know there’s a real person on the other side of the process—someone who is sincerely interested in their story, not just their résumé. They want to feel seen, heard, and valued. And hiring managers still rely on something no algorithm can fully replicate: human intuition. The ability to understand cultural fit, ambition, communication style, and the subtle cues that determine whether someone will thrive on a team.

So, while AI accelerates the search, it’s the human touch that builds trust.

It’s the conversation, the empathy, the nuance—that’s what ultimately helps a candidate feel confident and helps a team make the right hire.

That’s exactly why the most effective recruiting models in 2026 won’t be AI-only or human-only. They’ll be a thoughtful blend of both. At Tesoro AI, we embrace this balance intentionally: AI enhances our speed and precision, but it’s our people, relationships, and deep understanding of the AI landscape that deliver the experience candidates and hiring managers expect.

The future of hiring belongs to companies that combine AI efficiency + human authenticity—and know when to lead with each.

3. Global Talent Hubs Expand — and LATAM (Latin America) Emerges as a Leading Source of AI Talent

By 2026, the geography of AI talent sourcing will continue to diversify. While traditional hubs (U.S., Europe, India, China) remain important, alternative regions — especially Latin America — will rise sharply in strategic importance.

  • In Latin America and the Caribbean, 84% of employers already say they plan to upskill their existing workforce to meet demand for tech and digital talent.
  • For many global companies, remote hiring from LATAM has become an attractive strategy: a leading regional staffing firm recently reported a 285% surge in remote applicants in 2024.
  • Additionally, LATAM offers favorable alignment for U.S. companies: similar time zones, cultural compatibility, and competitive cost structures — making the region a natural nearshore hub for AI software development and support.
Implications for organizations and AI leaders:
  • Remote-first or hybrid hiring policies will be essential. Companies that cling to on-site-only hiring risk losing access to vast, skilled talent pools in LATAM and beyond.
  • Investment in remote infrastructure, cross-cultural onboarding, and global team management will become critical to integrating geographically distributed AI teams.
  • For Latin American AI professionals, this trend means rising opportunity and demand — creating upward pressure on wages and competition for top talent, especially as global firms increasingly tap into the region.

For Tesoro AI, this underscores the firm’s core value proposition: connecting companies in the Americas with vetted, world-class AI talent — no matter where that talent lives.

What This Means for CTOs, HR Leaders & AI Teams

Bringing together the three trends above, the 2026 landscape for AI talent and hiring will be shaped by a perfect storm: soaring demand, AI-enabled hiring tools, and a geographically expanding talent pool. For leaders building AI teams, this means:

  • Time is of the essence. The most skilled AI professionals will be snapped up fast. Speed — both in sourcing and hiring — will be a competitive advantage.
  • Flexibility will win. Teams that embrace remote or hybrid models, skill-based hiring, and global sourcing will be better positioned to assemble the talent they need.
  • Hiring strategy becomes value-driven. Rather than reactive recruiting, organizations must adopt a forward-looking, strategic approach: forecast where AI skills will be needed next, build talent pipelines early, and partner with intermediaries that understand the AI talent market — like Tesoro AI.
  • Culture and infrastructure matter. Hiring across borders isn’t just about finding the right technical skills — it’s about building cohesive, distributed teams. That means investing in tools, communication norms, continuous learning, and inclusive culture.

Why 2026 Could Mark a Turning Point — and How Tesoro AI Fits In

By 2026, AI will no longer be a “nice to have” for forward-thinking companies — it will be core to their competitive strategy. The companies that win will be the ones that move quickly, hire smartly, and tap into global talent pools.

This is where Tesoro AI becomes a strategic advantage. As an AI talent-acquisition firm focused on the Americas, Tesoro AI is uniquely positioned to:

  • Surface and vet qualified AI/ML professionals (engineers, data scientists, ML ops experts, etc.) before the wider market knows about them.
  • Help companies adopt remote-first talent strategies — especially tapping into LATAM’s growing pool of skilled developers and data scientists.
  • Reduce hiring friction and time-to-fill, enabling CTOs and HR leaders to respond rapidly to evolving strategic needs.

In essence, Tesoro AI becomes the secret weapon for companies that expect 2026 to be a make-or-break year in their AI journey.

Final Thoughts

2026 is shaping up to be a defining year for AI talent — not just because of what AI can do, but because of who builds it. As demand surges, hiring models shift, and global sourcing becomes the norm, companies must adapt or be left behind.

For CTOs and HR leaders: plan ahead. Invest in processes, infrastructure, and partners. Think globally. Hire fast. Hire smart.

For AI professionals — especially in under-tapped markets like LATAM — the opportunity is immense. The next few years could define your career trajectory.

And for bridging the gap between supply and demand? That’s the role Tesoro AI was built for.

Sources

Harry Thomas, Frenos

Frenos: How Local AI Models Are Powering the Future of Cybersecurity

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In the world of cybersecurity, speed and precision are everything. A single vulnerability can disrupt an entire city’s power grid or halt production in a refinery. Yet, traditional security models have struggled to keep up with the growing complexity of modern systems.

On the Tesoro AI Podcast, host Darius Gant spoke with Harry Thomas, founder of Frenos, an AI-native operational technology (OT) security platform. What followed was a deep look into how artificial intelligence is redefining the way critical infrastructure defends itself against attacks.

From Nursing to Hacking: The Unlikely Journey of a Founder

Before launching Frenos, Harry Thomas’s career took an unconventional path. He began as a nurse specializing in Alzheimer’s care before transitioning into cybersecurity. Burned out by long hours in healthcare, he returned to school to pursue his passion for computers.

“I started as a penetration tester for financial institutions in Boston,” Thomas explained. “That evolved into working for companies like Security Matters and later Dragos, where we used machine learning for anomaly detection.”

His time at AWS Security proved pivotal. There, he led research on user behavior analytics, using AI models to detect when hackers impersonated employees. The experience helped him see AI’s potential to shift cybersecurity from reactive defense to predictive strategy.

The Birth of Frenos: AI as the Ultimate Security Analyst

The idea behind Frenos emerged from a recurring problem in operational technology: the overwhelming flood of security alerts. “In OT, there’s too much noise. Teams don’t know what to prioritize,” Thomas said.

Frenos uses machine learning and large language models (LLMs) to transform this chaos into clarity. The system builds a digital twin of a company’s network and unleashes a reasoning agent—an AI that acts like a hacker—inside the simulation. The agent identifies weaknesses, chains vulnerabilities together, and predicts how an adversary might exploit them.

“We can simulate how a hacker could move through a network without ever touching the live environment,” Thomas explained. “It’s completely safe, but incredibly realistic.”

This approach allows Frenos to provide proactive security, showing organizations where attacks could happen before they do. Instead of relying on humans to sift through thousands of alerts, Frenos uses AI to surface the few that matter most.

Why Local AI Models Matter

While most AI startups rely on cloud-based LLMs, Frenos took a different route. The platform is fully on-premise, meaning all processing happens locally on customer hardware. This design ensures that no sensitive data ever leaves the organization.

“The Frenos platform isn’t a wrapper around OpenAI or Anthropic,” Thomas clarified. “We run local models with about eight billion parameters. They’re smaller, but faster and safer.”

Local models also address growing concerns about data privacy, compliance, and government access to cloud data. For industries that manage critical infrastructure or personal information—like utilities, healthcare, and public agencies—this local-first approach is becoming essential.

Running models locally comes with challenges, especially in cost and optimization. Frenos fine-tunes each model for specific cybersecurity tasks, ensuring high accuracy despite smaller model size. “If you put our local model up against a larger general-purpose model for our specific use case, ours will win every time,” Thomas said.

The Economics of AI Infrastructure

Building Frenos wasn’t cheap. Training its models cost hundreds of thousands of dollars in experimentation and compute resources. “Training an AI model is not for the faint of heart,” Thomas admitted. “It’s like watching paint dry while burning money.”

He emphasized that startups entering AI need to understand the financial implications. Unlike traditional software, AI systems incur ongoing expenses for API calls, GPU usage, and model retraining. “Every API call adds up fast,” he said. “We chose to build locally so that we could scale more cost-effectively.”

This approach helps clients avoid the sky-high costs of cloud inference, where a single API-heavy workflow can reach tens of thousands of dollars a month.

The Build-or-Buy Dilemma

Many companies approach Frenos thinking they can build a similar platform internally. According to Thomas, that’s easier said than done.

“If you go down the local model route, you’ll need developers who understand model context protocols, retrieval systems, and tool integration,” he explained. “Speed becomes the biggest issue. We can deliver results in under an hour, while an internal system might take days.”

Frenos achieves this speed by combining multiple neural networks—machine learning for rapid computation and LLMs for contextual reasoning. The result is a hybrid approach that balances accuracy and efficiency.

Finding the Right AI Talent

When asked about the kind of people it takes to build a company like Frenos, Thomas highlighted two core traits: adaptability and curiosity.

“I care more about how fast someone learns than what they already know,” he said. “If I give you five research papers, can you summarize them for me by the end of the week? That tells me more than your resume.”

Frenos separates its AI team into LLM specialists and machine learning engineers, recognizing the distinct skill sets each role requires. Ideal candidates have both technical proficiency and domain expertise in cybersecurity. “It’s easier to train a cybersecurity engineer in AI than the other way around,” Thomas noted.

Balancing Innovation and Regulation

As AI regulations tighten worldwide, Frenos’s on-premise architecture gives it a competitive edge. Clients retain full control over their data, avoiding compliance risks tied to external model providers.

“The platform doesn’t send information anywhere. Everything is processed locally,” Thomas said. “That’s how we help clients who handle sensitive data stay compliant and confident.”

He believes that as privacy laws evolve, local AI solutions will become the default for critical industries.

Looking Ahead: The Future Beyond Transformers

Thomas closed the interview with a bold prediction. “Within two years, I think transformer-based large language models will be obsolete. Someone will develop a new architecture that’s smaller, faster, and more efficient,” he said.

As global companies explore new frameworks for AI, Frenos’s innovation demonstrates that bigger isn’t always better. By combining local intelligence with domain-specific expertise, the company is charting a path toward smarter, safer, and more sustainable cybersecurity.

Conclusion

The story of Frenos illustrates a broader shift in AI development. The future isn’t just about building bigger models; it’s about building smarter systems that fit the realities of business, regulation, and security.

From healthcare to hacking to entrepreneurship, Harry Thomas’s journey mirrors the evolution of AI itself—cross-disciplinary, fast-moving, and focused on solving real-world problems.

Frenos shows that when AI meets purpose, it can transform not just technology, but the way industries protect the infrastructure we all depend on.

 

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Sector Shifts: Where LATAM’s Hiring Demand Is Growing in 2025

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In 2025, Latin America (LATAM) is witnessing a transformation in hiring demand. No longer limited to broad “tech” roles, companies are increasingly targeting specialized talent in AI, cloud, fintech, and adjacent emerging sectors. For organizations looking to scale in LATAM or tap its talent pools, understanding which sectors are accelerating is key to making smart hiring decisions.

Below, we break down the leading sectors attracting hiring demand and what this means for companies and professionals alike.

1. Fintech & Digital Finance

Fintech remains one of LATAM’s most dynamic sectors for hiring. Growth in digital financial services, open banking, and underbanked populations has fueled demand for talent in payments, lending platforms, regtech, and embedded finance.

  • The number of fintech startups in LATAM grew over fourfold from 2017 to 2023 (from ~703 to more than 3,000) according to IDB and Finnovista with Brazil, Mexico, Colombia, Chile, and Argentina leading the count.
  • Fintech now accounts for a large share of VC investment in LATAM, making it a prime sector for recruiting roles in product, compliance, risk, and AI-driven credit scoring.
  • Within fintech, demand is especially strong for back-end engineers, data scientists, fraud detection experts, and compliance/AML specialists.

If your organization is in financial services or wants to embed finance into your product, LATAM offers deep talent pools and business innovation potential.

2. AI, Machine Learning & Data Intelligence

AI roles remain the fastest-growing niche in tech hiring across LATAM. Companies are moving beyond pilots toward embedding AI in operations, customer experience, and decision systems.

  • According to the World Economic Forum’s Future of Jobs Report 2025, data analysts, AI and machine learning specialists are projected to be among the fastest-growing roles in Latin America and the Caribbean.
  • A recent survey of Brazilian firms noted that generative AI tools are transforming work in sectors like content, customer service, graphic design, and software development.
  • LATAM is already producing research on “emerging AI powers” countries like Argentina, Colombia, and Uruguay are rising in rankings due to education, infrastructure, and finance pillars.

Roles in high demand include ML engineers, NLP specialists, prompt engineers, AI operations roles, and those combining domain expertise (e.g. healthcare, manufacturing) with AI skills.

3. Cloud Infrastructure & DevOps / Site Reliability Engineering

The shift from on-prem systems to cloud and hybrid architectures is driving demand for cloud engineers, DevOps, SRE, and reliability specialists across LATAM.

  • AWS plans to invest USD 4 billion in cloud infrastructure in Chile, marking its third cloud region in Latin America after Brazil and Mexico. This expansion will create new demand for local cloud operations, security, and infrastructure teams.
  • As companies adopt scalable cloud systems, they need engineering talent that can manage containerization, CI/CD, observability, security, and platform scaling.

Those who understand multi-cloud, Kubernetes, infrastructure-as-code, and resilience patterns are particularly sought after.

4. SaaS, B2B Tech & Platform Businesses

SaaS and B2B platforms are rising in prominence in Latin America. The de-centralization of software is enabling global players to build from LATAM and serve markets across the Americas and beyond.

  • The LatAm Tech Report highlights SaaS alongside fintech, e-commerce, healthtech, and climate tech as sectors drawing investor attention and talent.
  • Many B2B fintech, productivity tools, and industry platforms are expanding, requiring teams skilled in full-stack development, APIs, integrations, UX, and platform growth.

Hiring emphasis in this sector often falls on product engineers, API developers, backend architects, and customer success engineers who can scale SaaS offerings globally.

5. Data Enrichment & Crowd / Annotation Work

While not always front-of-mind, data enrichment and labeling work remains a backbone for AI systems. LATAM is becoming a hub for this labor.

  • A recent study surveyed 100 crowdworkers across Latin America and the Caribbean, analyzing their roles in data labeling and enrichment for global AI systems. The authors emphasized the importance of this work as foundational to deploying AI at scale.
  • Companies building AI models need data pipelines, quality assurance, annotation platforms, and governance. These roles are often entry-level but scalable and distributed.

For tech teams, investing in reliable data enrichment capacity can accelerate model training and maintain quality control across AI workflows.

What This Means for Hiring Strategy

These sector shifts signal where demand is heading. Here are strategic takeaways for HR leaders, founders, and talent teams:

  1. Target the intersection of sector + tech skills. Rather than hiring generic engineers, look for candidates with domain experience in fintech, health, or logistics plus AI/ML capability.
  2. Invest in local infrastructure talent. As cloud expansion hits LATAM, securing local SRE, DevOps, and infrastructure engineers becomes a competitive moat.
  3. Leverage data enrichment as a recruiting entry point. Roles in annotation and data pipelines often serve as on-ramps for junior talent to grow into ML engineering.
  4. Blend soft skills with technical skills. Reports show increasing employer focus on leadership, resilience, and adaptability. For 2025, 84% of LATAM employers plan to upskill internally to meet digital demand.
  5. Use regional differentiators. LATAM offers time zone alignment with North America, cultural proximity, and growing English proficiency—all of which amplify collaboration and team productivity.

At Tesoro AI, we are tracking these shifts closely. When clients come to us looking to scale their AI or cloud teams, we now anticipate sector-led hiring patterns and ensure our talent networks stay aligned with high-growth verticals.

As Latin America’s tech landscape continues to mature, the opportunity lies not just in recruiting more but in recruiting smarter.

Sources

  • Banco Interamericano de Desarrollo (IDB) & Finnovista. (2024). Fintech in Latin America: Growth and opportunities in a digital era. Inter-American Development Bank. Retrieved from https://www.iadb.org
  • Bloomberg Línea. (2025, May). Latin America’s tech hiring surge enters a new phase. Retrieved from https://www.bloomberglinea.com
  • Latitud. (2025). The LatAm Tech Report: Main Insights. Latitud. Retrieved from https://www.latitud.com/blog/the-latam-tech-report-main-insights
  • Reuters. (2024, June 20). Latin American fintechs quadruple in the past six years with more room to grow. Reuters Technology. Retrieved from https://www.reuters.com/technology/
  • Reuters. (2025, May 7). Amazon to spend $4 billion on cloud infrastructure in Chile. Reuters Business. Retrieved from https://www.reuters.com/business/
  • World Economic Forum. (2025, April). The Future of Jobs in Latin America and the Caribbean: Digital skills gap must close quickly to satisfy evolving employer demands. World Economic Forum. Retrieved from https://www.weforum.org
  • Arxiv. (2025, January). Mapping emerging AI powers in Latin America: Education, infrastructure, and finance. Cornell University. Retrieved from https://arxiv.org
  • Arxiv. (2025, May). Generative AI and productivity in emerging economies: Evidence from Brazil and Mexico. Cornell University. Retrieved from https://arxiv.org
  • AI Global Workforce Report. (2025). AI labor dynamics and data annotation in Latin America. Global AI Research Institute. Retrieved from https://www.aiglobalreport.org

DeShuna Spencer, Kweli TV

Breaking Barriers in Venture Capital: Austin Clements on Building Slauson & Co. and Funding the Future

When Austin Clements co-founded Slauson & Co., he wasn’t just launching another venture capital firm. He was rewriting the narrative of who gets funded, who gets a seat at the table, and how inclusive wealth creation can reshape innovation in America.

On the Build Tech Stack Equity, Austin sat down with Darius Gant to discuss his path from South Los Angeles to becoming one of the most thoughtful emerging managers in venture capital. What followed was an honest, behind-the-scenes look at the business of funding innovation, the importance of timing, and how AI is reshaping the world founders now build in.

From Gardena to Growth: The Making of a Venture Capitalist

Austin’s story begins in South Los Angeles, where curiosity about technology met a desire to understand wealth. After studying at Morehouse College, he returned home to work in finance, learning what separates those who build wealth from those who maintain it. But technology was always calling.

In the late 1990s, long before the world saw the power of e-commerce, Austin built websites for small businesses. “I was explaining to people why they needed a website when most didn’t think they’d ever want one,” he recalled. The experience taught him the power of technology to expand opportunity and visibility for underserved entrepreneurs.

Eventually, Austin discovered venture capital while studying for his CFA. Drawn by the mix of finance and innovation, he immersed himself in the writings of investors like Fred Wilson, Brad Feld, and Mark Suster, who became informal mentors through their blogs. The more he read, the clearer his path became.

“I realized that venture capital was exactly where I belonged,” he said. “It combined the parts of investing I loved with the future-building side of technology.”

After earning his MBA from NYU, Clements joined TenOneTen Ventures, where he learned the craft of early-stage investing. Those years gave him the foundation for something bigger.

Founding Slauson & Co.: Optimism, Naivety, and the Power of Timing

Austin teamed up with longtime friend Ajay “AJ” Thomas, who had helped build Queensbridge Venture Partners with Nas. Both had made their way into venture capital independently, but they shared one vision: to create a firm that reflected the communities they came from and the founders they wanted to serve.

Launching a venture fund wasn’t easy. “Like most entrepreneurial journeys, it started with optimism and naivete,” Austin laughed. “We thought everyone would line up to give us money.”

Instead, fundraising proved brutal. After leaving his job, Austin had a year’s worth of savings to make it work. He spent it cold-calling over 300 potential limited partners (LPs), often hearing polite rejections. Just as the savings were running out, 2020 changed everything.

The dual shocks of the COVID-19 pandemic and the George Floyd protests reshaped the conversation around inclusion in finance. Suddenly, investors were looking for credible, diverse fund managers who could bring new perspectives. “We didn’t change our story, but the world finally started listening,” Austin said.

The firm’s first fund ballooned from an initial $15 million target to $75 million, backed in part by legendary investor Ron Conway. The lesson, Austin noted, was simple: “Timing is everything. Sometimes you just have to stay in the game long enough to get lucky.”

A New Kind of Investment Thesis

Slauson & Co. focuses on pre-seed and seed investments across both B2B and consumer technology. On the B2B side, the firm targets tools that empower the next generation of small business owners. On the consumer side, they invest in what Austin calls “architects of the human experience” founders who deeply understand cultural and behavioral shifts shaping society.

“We look for lived experience as a competitive advantage,” he explained. “That means we’re backing people who understand the world they’re building for because they’ve lived it.”

Slauson’s checks typically range from $1 to $3 million, often as the first institutional capital into a company. Austin looks for founders who can identify macro shifts and align them with unique insights a framework he credits to the book Pattern Breakers by Mike Maples Jr.

“Your idea might pivot,” he said, “but your insight should stay constant. That’s what separates great founders from the rest.”

The Power of Storytelling in the Startup Era

Austin believes one of the biggest shifts in entrepreneurship is how founders connect with their audiences. “A few years ago, everyone thought tech platforms should be neutral. Now, people want authenticity,” he said.

He encouraged founders to embrace vulnerability and purpose in their messaging. “Your story matters. People want to know who you are, what you stand for, and why you built what you built.”

From Tesla to ChatGPT, Austin noted that public perception of leaders now directly impacts business performance. “It’s not enough to say your product speaks for itself,” he said. “If you don’t get your story right, it’s hard to build a billion-dollar company.”

Investing in AI: Finding Opportunity Beyond the Hype

When the conversation turned to artificial intelligence, Austin was clear: while the LLM race may be over for new entrants, opportunity still exists at the application layer.

He pointed to Abby, an AI-powered therapy app in Slauson’s portfolio. The platform is designed specifically for mental health, addressing issues that general-purpose AI tools like ChatGPT cannot. “Abby is built with the guardrails, empathy, and research that a therapy-focused product needs,” he said.

Austin believes the next wave of AI innovation will come from specialized, purpose-built systems that solve real problems rather than chasing massive infrastructure models. “Find your lane and own it,” he advised founders. “You don’t have to compete with OpenAI to build something transformative.”

Redefining Access: The Friends & Family Accelerator

For founders without the privilege of early funding networks, Slauson & Co. launched the Friends & Family Accelerator. The program invests $300,000 into early-stage startups and provides six months of mentorship, exposure, and resources.

“Our goal is to support founders who might not have a friend or family member who can write a five-figure check,” Austin said. “We want to close that access gap.”

The accelerator helps participants refine their ideas, connect with seasoned founders, and prepare for seed-stage investment. “Sometimes all a founder needs is a little capital and a strong network to change their trajectory,” he added.

Looking Ahead: Where Venture and AI Collide

Austin sees the future of venture capital and AI converging around human insight. As automation scales, creativity, lived experience, and emotional intelligence will become defining factors in successful entrepreneurship.

“The founders who thrive in this era are those who combine technology with humanity,” he said. “We invest in people who not only see the shift coming but know how to shape it.”

Slauson & Co. isn’t just betting on ideas—it’s betting on the next generation of innovators who reflect the diversity, resilience, and creativity of the world they serve.

To hear her full, inspiring journey and learn more about her advice for non-technical founders, listen to the complete conversation on the Build Tech Stack Equity Podcast. Follow us for more stories of the incredible entrepreneurs and investors shaping the future of technology.

 

From Capital to Code

From Capital to Code: How Darius Gant is Redefining AI’s Role in Global Growth

When Tesoro AI Venture Studio’s Founder, Darius Gant, took the stage at the Go Global Conference 2025 in Atlanta, his message resonated across the room. The future of growth, he explained, belongs to those who can blend access to capital with access to talent and technology.

Across two powerful sessions, one focused on Access to Capital – Funding Without Borders and the other on Technology – AI, Crypto & Humanoids, Darius shared a vision that connects financial strategy, human potential, and the intelligent use of AI.

Capital Is Leverage, But So Is AI

In the investment panel, Darius redefined the way entrepreneurs should think about funding. Investors, he said, are no longer focused solely on what you build. They are just as interested in how efficiently you build it.

“AI is not just a cost saver; it’s a growth accelerator,” Darius explained. “Every investor wants to know how you’ve already reduced their risk. AI is one of the most powerful ways to answer that question.”

He encouraged founders to weave their AI strategy directly into their fundraising story. Showing that AI drives efficiency, productivity, and smarter decision-making makes a company far more attractive to investors.

Darius also invited entrepreneurs to think strategically when seeking global investors. He reminded the audience that capital should come with more than funding it should come with access. “If most of your customers are in other countries, work with investors who can open doors there,” he advised. “The right capital partner should help you scale, not just spend.”

Derisking Investment Through People and Structure

Throughout the discussion, Darius emphasized that access to capital and access to talent go hand in hand. In his experience, the startups that succeed are the ones that invest in human infrastructure early.

By building distributed teams of AI engineers and developers across Latin America, companies can achieve scale without bloated costs. “When you combine efficient AI infrastructure with nearshore talent, you’re not just building faster,” he said. “You’re creating stronger business margins.”

This combination of technology and human capability gives startups the structural stability investors look for when deciding where to allocate capital.

Turning AI From Buzzword to Business Discipline

During the Technology session, Darius reflected on his journey from investor to founder and the lessons learned along the way. He recalled how, in 2016, he began investing in software companies and noticed early signs that AI would soon move beyond research labs into everyday business operations.

“I realized early on that AI wasn’t just another trend,” he said. “It was going to change how every company operates.”

At Tesoro AI Venture Studio, Darius now oversees five portfolio companies that focus on different areas of the AI ecosystem, including automation, infrastructure, and talent development. One of the standout initiatives he discussed was Quantum Academy, an education platform designed to prepare young professionals for an AI-driven economy.

“We’re teaching people not just how to use AI, but how to integrate it into real business applications,” Darius shared. “The goal is to make every graduate AI-native from day one.”

AI as the Great Equalizer

One of Darius’s most compelling insights was his belief that AI can be the most inclusive technological revolution in history. Drawing from the book The Fourth Industrial Revolution, he explained that AI offers an unprecedented opportunity for participation and wealth creation.

“In previous industrial revolutions, only those with access to capital or power could participate,” he said. “This time, the door is open. AI is still new enough that anyone willing to learn can take part.”

He urged the audience to take advantage of this open window of opportunity. “Don’t wait for someone to define the rules,” he said. “Right now, the field is open for everyone. Once the gates close, entering will be much harder.”

A Practical Starting Point for Businesses

For small and midsize companies curious about adopting AI, Darius’s advice was refreshingly simple: start with the tools you already use.

He encouraged business owners to explore AI features within familiar platforms like their CRM, accounting software, or logistics systems. “You don’t need a data science degree to begin,” he said. “Look for AI tools that solve one real problem in your business. Start small, learn fast, and build from there.”

He also reminded the audience to be mindful of data privacy. Cloud-based AI tools can be incredibly powerful, but understanding how and where your data is stored is key. “If you’re dealing with sensitive or regulated data, consider local AI models that run internally,” he advised. “You can still harness the power of AI while keeping your information secure.”

Blockchain, Creativity, and the Next Wave

Reflecting on his own entrepreneurial journey, Darius spoke about his early exploration of blockchain and its parallels with AI. Both technologies, he noted, began as experimental ideas and are now transforming industries.

He believes that the intersection of AI and blockchain will unlock new ways for creators and entrepreneurs to own their work and participate in global markets. “We’re entering a new economy of digital creativity,” he said, “and technologies like AI and blockchain will make it more inclusive than ever.”

Closing Reflections: The Power of Leverage

Across both panels, Darius returned to one central idea: leverage.

Access to capital is leverage.

Access to AI is leverage.

Access to global talent is leverage.

When combined, they create the ultimate formula for sustainable growth.

“The founders who win in this era won’t necessarily be the ones who raise the most money,” Darius concluded. “They’ll be the ones who know how to make every dollar, every engineer, and every line of code work together efficiently. That’s the future of business.”

Sources

  • Gant, D. (2025, October 4). Access to Capital Panel. Go Global Conference, Atlanta.
  • Gant, D. (2025, October 4). Artificial Intelligence in Business Panel. Go Global Conference, Atlanta.
  • Go Global Conference. (2025). Official Website. Retrieved from https://goglobalconference.com

AI Pilots Are Out. Execution Is In

Latam-GPT Goes Live: What It Means for Local AI Jobs

Why it matters: The debut of Latam-GPT, the region’s first large language model (LLM) developed with a focus on Latin American languages, data, and context, marks a turning point in the global AI race. For local talent, it means more than a new tool, it’s a catalyst for job creation, workforce transformation, and regional competitiveness in the age of generative AI.

A Regional Milestone in AI

In June 2025, Latam-GPT went live, positioning itself as Latin America’s answer to OpenAI’s GPT-4 and Anthropic’s Claude. Unlike its global counterparts, Latam-GPT was trained with an emphasis on Spanish and Portuguese linguistic nuances, regional legal frameworks, cultural contexts, and industry-specific datasets from Latin America.

This milestone is more than symbolic. It’s a direct assertion that Latin America is not just a consumer of global AI technology, but a creator of frontier innovation. By anchoring development in regional priorities, Latam-GPT strengthens local autonomy and narrows dependency on U.S. and European AI labs.

Crucially, the model’s launch also underscores the rising public-private-academic coalitions in the region. Governments provided policy backing, local universities contributed datasets and research capacity, and private investors supplied the capital to scale. This collaboration reflects a maturing AI ecosystem that’s determined to build sovereign capabilities.

The Job Creation Ripple Effect

Whenever a new foundational model is launched, a wave of job creation and role transformation follows. Latam-GPT is no exception. Here’s where the most visible impacts are emerging:

  1. AI Engineering & Fine-Tuning Roles
    • Local startups, universities, and corporations will need AI engineers and ML specialists to fine-tune Latam-GPT for vertical-specific use cases like healthcare, logistics, and fintech.
    • This means rising demand for prompt engineers, data annotation specialists, and AI infrastructure managers who can adapt workflows to each industry.
  2. Content & Language Jobs
    • By supporting Latin American dialects and cultural references, Latam-GPT will create demand for linguists, sociologists, and content reviewers to ensure accuracy, inclusivity, and ethical use.
    • This offers opportunities for non-technical professionals to actively participate in the AI economy.
  3. Industry Transformation Specialists
    • Sectors like banking, customer service, and education will need AI adoption leads professionals who bridge technical expertise with industry workflows.
    • These hybrid roles will grow in importance as organizations seek to embed Latam-GPT across operations.
  4. AI Governance & Policy Roles
    • Governments and regulators will increasingly recruit for roles in AI ethics, compliance, and auditing, ensuring Latam-GPT’s deployments adhere to regional and global frameworks.
    • This adds another layer of professional opportunities for legal and policy experts.

Beyond Disruption: How Existing Jobs Will Change

While job creation is exciting, the bigger story lies in job transformation. Routine tasks from call center scripts to loan processing will increasingly be automated by Latam-GPT-powered assistants. But rather than eliminating roles, this will upskill and reposition talent:

  • Customer support agents will shift from repetitive Q&A to handling complex, human-centric issues, supported by AI-generated responses.
  • Teachers will use Latam-GPT as a co-pilot, focusing on mentorship, creativity, and student engagement rather than rote content delivery.
  • Financial analysts will rely on automated reporting, freeing them for scenario planning and strategic decision-making.
  • Healthcare professionals will use AI-assisted diagnostic tools to identify early signs of disease, allowing doctors to concentrate on treatment strategies and patient care.

The net effect: human workers move up the value chain, focusing on creativity, judgment, and collaboration.

Regional Advantage: Why LATAM Matters in the AI Talent Market

Latam-GPT also highlights why global companies are doubling down on hiring AI talent in Latin America. Three factors stand out:

  1. Time Zone Alignment

    With minimal time difference, U.S. and Latin American teams collaborate in real time running agile sprints, resolving incidents instantly, and fostering tight cultural integration.

  2. Cost Efficiency

    Hiring senior developers in Mexico, Colombia, or Brazil costs significantly less than in Silicon Valley, without sacrificing quality. This cost-effectiveness is especially attractive for AI startups under pressure to scale lean.

  3. Cultural and Linguistic Expertise

    Latin American engineers and linguists bring unique knowledge of local dialects, idioms, and contexts something global models often miss. In industries like healthcare, law, and education, this contextual intelligence is invaluable.

Together, these factors position Latin America not just as a deployment market, but as a strategic talent hub where global AI companies will increasingly recruit.

Opportunities and Challenges Ahead

The promise of Latam-GPT is immense, but so are the challenges. The region must navigate:

  • Infrastructure gaps: Cloud costs and GPU shortages remain hurdles for startups trying to scale AI services.
  • Brain drain risks: As global companies hunt for Latin American talent, retaining engineers locally will require competitive wages and meaningful projects.
  • Ethical safeguards: Ensuring Latam-GPT avoids biases and protects user privacy is essential to build public trust.
  • Talent alignment: Workforce training must keep pace with rapidly changing skills, requiring partnerships between universities, bootcamps, and employers.

Addressing these challenges proactively will determine whether Latam-GPT sparks sustainable growth or creates dependency on external capital.

Tesoro AI’s Perspective

At Tesoro AI, we’ve already seen the ripple effects of Latam-GPT’s launch in our talent pipeline. Companies are no longer just asking for generic data scientists they’re seeking professionals who can:

  • Fine-tune models like Latam-GPT for specific industries.
  • Audit outputs for cultural sensitivity and ethical compliance.
  • Integrate LLMs into enterprise workflows at scale.

By deeply vetting AI engineers and connecting U.S. and LATAM firms with execution-ready professionals, we ensure this new phase of regional innovation is matched with the right human expertise.

Conclusion: From Importers to Innovators

Latam-GPT’s launch is a symbol of Latin America’s growing maturity in AI. It signals a shift from being importers of global AI products to becoming innovators in their own right. For talent, it means new roles, transformed workflows, and expanded opportunities to shape the global AI landscape.

This matters for three reasons:

  1. It proves Latin America can build world-class AI infrastructure tailored to its own needs.
  2. It accelerates the transformation of existing jobs, empowering professionals to take on higher-value roles.
  3. It positions the region as a strategic partner in the global AI economy, rather than just a market to be served.

For companies, the call to action is clear: invest in Latin American talent now. Those who do will not only benefit from cost-effective, highly skilled professionals but also gain an edge in creating culturally resonant AI solutions.

Latin America isn’t waiting for the future of AI it’s actively building it. And with tools like Latam-GPT and firms like Tesoro AI, the region is proving that the age of execution has truly arrived.

Sources

Bloomberg Línea. (2025, June). Latam-GPT launch marks milestone for AI in Latin America. Retrieved from https://www.bloomberglinea.com

Contxto. (2025). How Latam-GPT is reshaping the regional AI landscape. Retrieved from https://contxto.com

OECD. (2024). AI policy developments in Latin America. OECD Digital Economy Papers. Retrieved from https://oecd.org

World Bank. (2025). AI and the Future of Work in Latin America. Retrieved from https://worldbank.org

AI Pilots Are Out. Execution Is In

AI Pilots Are Out. Execution Is In: Latin America’s New Phase

For years, Latin America’s AI story has centered on pilots, small-scale experiments designed to test what machine learning could do. But in 2025, that narrative is changing. The region is entering a new phase: execution at scale. Governments, corporations, and startups are no longer content with proof-of-concept. They are embedding AI into the workflows that run economies, cities, and industries.

1. From Experimentation to Execution

In the past, many AI projects in Latin America ended as isolated demos. The problem wasn’t interest, it was infrastructure, funding, and integration. Now, those gaps are closing:

  • Cloud adoption has surged across Colombia, Mexico, and Brazil.
  • Universities and startups are collaborating on industry-grade datasets.
  • Governments are publishing AI roadmaps, creating clarity for investment.

As a result, AI projects are moving out of labs and into logistics centers, hospitals, and financial institutions.

2. Cross-Sector Collaboration as a Catalyst

At Tesoro AI, we’ve observed how multi-stakeholder partnerships are fueling momentum:

  • Healthcare: AI is now used for diagnostics in public hospitals across Chile and Argentina.
  • Agriculture: Startups are embedding predictive analytics into farming co-ops, helping farmers anticipate weather and crop yields.
  • Finance: Banks like BBVA and Nubank are deploying AI for fraud detection and credit scoring, built with LATAM engineers.

This collaboration ensures that AI adoption isn’t siloed. It scales across entire sectors.

3. The Role of Talent

Execution depends on people as much as technology. Latin America’s tech talent pool has matured significantly:

  • Senior engineers are returning from Silicon Valley to lead local teams.
  • Universities are embedding AI into core curriculums.
  • Bootcamps and accelerators are upskilling mid-career professionals into AI roles.

The result is a workforce ready not just to test ideas but to own entire AI workflows.

4. Case Studies in Execution

  • Logistics: Mexican logistics firms now use AI to route trucks in real time, reducing delivery costs by 20 percent.
  • Healthcare: Colombian hospitals are rolling out AI triage systems, cutting patient wait times by half.
  • Energy: Brazilian utilities are deploying predictive maintenance AI to prevent outages before they occur.

These aren’t pilots. They are operational systems with measurable ROI.

Conclusion

The AI era in Latin America is maturing fast. Pilots were valuable because they built confidence and proved feasibility. But the future belongs to execution. What sets this new phase apart is not only the scaling of technology but also the intentional alignment between governments, corporations, and startups. The ecosystem is converging around common goals, from efficiency gains in healthcare and logistics to resilience in energy and finance.

This matters for two reasons. First, it signals that Latin America is ready to play a more assertive role in the global AI economy, moving from consumer to producer of scalable AI solutions. Second, it demonstrates that AI is no longer a speculative investment here. It is becoming a foundational tool that businesses, policymakers, and communities rely on daily.

For companies, this is the moment to lean in. Those who wait risk falling behind, while those who embrace this execution wave will not only innovate but help define the region’s AI-driven future. At Tesoro AI, we are proud to be part of this movement, helping organizations scale beyond pilots and realize the transformative power of execution-ready AI talent.

Looking for execution-ready AI talent? Partner with Tesoro AI to scale your workflows today.

Sources

Hi Ventures. (2025). Latin America’s AI shift: From pilots to execution. Hi Ventures Report.

Nubank. (2025). AI for financial inclusion in Latin America. Retrieved from https://nubank.com

BBVA. (2025). Fraud detection and AI applications in LATAM banking. Retrieved from https://bbva.com

ProColombia. (2025). AI adoption across sectors in Colombia. Government of Colombia.

Frank Cohen, Clever Moe

From Plumbing Disaster to Tech Disruptor: The Story Behind CleverMoe

Every homeowner knows the feeling. A pipe bursts, a circuit trips, or a roof leaks, and you’re suddenly thrown into the chaotic world of finding a reliable contractor. You scroll through lead-generation sites, make endless calls, and hope the person you hire is trustworthy. For Frank El-Masry, a seasoned engineering leader with a background at Intel and Cisco, this wasn’t just an inconvenience it was a broken system he was determined to fix.

In a recent episode of the Tesoro AI Podcast, Frank sat down with host Darius Gant to share the personal story of the plumbing disaster that led him to create CleverMoe, an innovative platform built to restore trust and transparency to the home services industry.

The “Aha!” Moment in a Flooded Home

Frank’s journey from a corporate career to a startup founder began with a catastrophe. While on a family vacation, he received a call that a pipe had burst in his home, flooding the first floor. The disaster that followed was a masterclass in the failures of the modern contracting system.

“You’re calling people and you’re at their mercy,” Frank explained. “They tell you, ‘I’ll be there in three hours,’ and they don’t show up. It was a nightmare.”

He found himself dealing with contractors who were unreliable, and a system where he had no control or transparency. After a frustrating and expensive experience, a friend asked him a simple question: “You’re a problem solver. Why don’t you do something about this?”

That question sparked the idea for CleverMoe.

Fixing a Broken System

Frank realized that popular platforms like Angi, Yelp, and Thumbtack weren’t solving the core problem. They are primarily lead-generation tools, where contractors pay for leads, and homeowners are flooded with calls from unvetted strangers.

“The business model is broken,” Frank stated. “It’s a race to the bottom. The person who gets the lead is the fastest finger, not necessarily the best person for the job.”

This system creates a transactional, low-trust environment that fails both the homeowner and the skilled tradesperson. CleverMoe was designed to be the antidote.

The CleverMoe Difference: Quality Over Quantity

CleverMoe flips the script by focusing on trust, quality, and relationships.

 

The Old Way (Lead-Gen Sites) The CleverMoe Way
Contractors pay per lead. Contractors pay a flat subscription fee.
Homeowners are spammed with calls. Homeowners receive a curated list of vetted professionals.
Focus is on speed and volume. Focus is on quality, licensing, and insurance.
Creates a transactional relationship. Fosters a community of trusted “problem-solvers.”

 

“We don’t call them contractors; we call them problem-solvers,” Frank emphasized. By thoroughly vetting every professional on the platform verifying licenses, insurance, and background checks CleverMoe ensures that homeowners are connected with people who are qualified to do the job right.

The Future is AI-Powered

As the name suggests, CleverMoe is built on intelligent technology. The platform already uses AI to match homeowners with the right professionals based on their specific needs. But Frank’s vision goes much further.

He plans to build a “digital twin” of a home, using AI to analyze photos and videos uploaded by the homeowner to provide accurate, upfront quotes. This would eliminate the guesswork and surprise costs that plague the industry.

“You take a picture of your problem… and we’ve trained our models to tell you, ‘Hey, that’s a five-inch crack. It’s going to cost you X,'” he explained.

The Loneliness of the Founder

Frank’s story is not just one of innovation, but also of resilience. He spoke candidly about the “lonely” journey of a founder and the importance of having a strong “why” to push through the challenges. For him, it’s the burning desire to solve a problem that affects millions of people every day.

Frank El-Masry’s journey with CleverMoe is a testament to how personal frustration can fuel groundbreaking innovation. It’s a story about challenging a broken system and rebuilding it with trust, technology, and a deep-seated desire to help people.

To hear Frank’s full story, his advice for aspiring entrepreneurs, and his vision for the future of home services, listen to the complete conversation on the Tesoro AI Podcast. Follow us for more inspiring stories from the founders and innovators building the future of technology.

 

DeShuna Spencer, Kweli TV

From Trauma to Triumph: How DeShuna Spencer Built Kweli TV, A Global Home for Authentic Black Diaspora Stories

In the world of media, a dark mantra often dictates the headlines: “If it bleeds, it leads.” For a young DeShuna Spencer, working as a daily news journalist, this wasn’t just a phrase—it was a traumatizing reality. Tasked with reporting on crime, she found herself on the front lines of tragedy, a position that ultimately forced her to question the very nature of her profession.

In a recent episode of the Build Tech Stack Equity Podcast, Spencer sat down with host Darius Grant to share the powerful story of how a heartbreaking moment as a reporter became the catalyst for building Kweli TV, a global streaming platform dedicated to authentic Black stories.

The Breaking Point

As a young journalist, Spencer’s days were filled with covering the grim realities of crime. The turning point came during a call to the mother of a murder victim, a call she was pushed to make by her editor.

“I said, ‘I’m sorry, I’m Deshawna. I’m with Oakland Tribune, I’m sorry for your loss,'” Spencer recalled. “And they were like, ‘Sorry for your loss? We just left the hospital that says she was still alive.’ And then the woman starts crying hysterically.”

In that moment, Spencer realized she wasn’t just reporting the news; she was an instrument of trauma. “I realized that I was a part of a problem,” she said. “I wasn’t impacting my community through storytelling.”

Building a Home for the Global Black Community

That experience ignited a new mission: to create a space that celebrated the vast, diverse, and nuanced reality of the Black experience, free from the stereotypes and trauma-porn that dominate mainstream media.

That vision became Kweli TV (Kweli means “truth” in Swahili).

“From day one, it’s all about the Black people from around the world,” Spencer explained. Today, Kweli TV hosts over 800 titles, featuring stories from the Caribbean, Latin America, Europe, Australia, and Africa. It’s a platform built on a few core principles:

  • Authenticity: Telling stories that are true to the culture they represent.
  • A Safe Space: Creating a platform where the community won’t be bombarded with traumatic content. There is a strict “no reality TV” policy.
  • Representation for All: A significant focus is on children’s programming, ensuring Black children can see themselves represented authentically on screen.

The Unfiltered Reality of a Non-Technical Founder

DeShuna’s journey was far from easy. As a non-technical founder, she faced a steep learning curve. After winning a $20,000 journalism competition, she hired a developer to build the first version of the platform.

“He was about 90% done,” she recalled, “and I never heard from him again.”

Left with an unfinished product and no technical co-founder, Spencer was inspired by a quote from the co-founder of LinkedIn: “If you’re not embarrassed by the first version of your product, you launched too late.” She decided to release the buggy, 90%-done beta to the world.

For nearly three years, she ran the platform through sheer will, manually resetting user passwords via email and learning from a constant stream of customer feedback. “It was horrible,” she admitted, “but it’s the joys of being a startup.”

Taking Control of Her Destiny

Tired of waiting for a technical savior or VC funding that rarely materializes for Black female founders, Spencer decided to take matters into her own hands. She entered a pitch competition at Harvard Business School and won, providing the capital needed to partner with a professional third-party tech vendor.

This shift was crucial. Spencer learned that even giants like Netflix don’t build everything in-house. Her advice to other founders is to find a tech partner who believes in your mission and can grow with you.

“I decided to not rely on other people to dictate my destiny,” she stated. “How do we allow our community to keep ourselves alive versus relying on someone in Silicon Valley?”

DeShuna Spencer’s story is a masterclass in resilience, mission-driven entrepreneurship, and the power of creating the change you want to see in the world.

To hear her full, inspiring journey and learn more about her advice for non-technical founders, listen to the complete conversation on the Build Tech Stack Equity Podcast. Follow us for more stories of the incredible entrepreneurs and investors shaping the future of technology.

 

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