February 2024

In the golden era of startup funding, venture-backed AI startups could freely allocate substantial resources to attract top AI talent, necessary for competing with the vast resources of big tech firms. However, the AI sector, despite experiencing a 9% year-over-year growth in funding since 2022, now faces a paradigm shift.

The focus of investors and board directors has pivoted towards profitability. This shift is particularly impactful for AI founders, who must be acutely budget-conscious due to the incremental costs involved in delivering AI solutions, including data acquisition, model training, and deployment.

Moreover, the necessity to pay premium salaries for the unique skillsets of AI talent adds another layer to the budgeting puzzle. This scenario creates a complex budgeting conundrum for AI teams.

That said, the dream of achieving the Rule of 40, a growth and efficiency metric used by VCs where revenue growth rate plus EBITDA margin equal 40%, is well within reach. The aforementioned issues can be effectively addressed through intelligent talent acquisition strategies.

In my experience as a software investor, 70% of a startup’s expenses are in human capital, with a significant portion allocated to R&D. By limiting “overspending” on our product team, we can control our cost infrastructure providing the flexibility to make other high ROI investments such as go-to-market strategies.

Here are a couple highly effective AI talent strategies:

1. Leveraging AI Experts on a Fractional Basis
2. Develop a Nearshore Talent Acquisition Strategy

Leveraging AI Experts on a Fractional Basis

Hire a 20-year AI Expert for $200/hour on a fractional basis versus footing the bill for a full-time employee, which could cost in excess of $400,000/year (plus equity).

Note: It’s rare to find “discounts” on the most experienced AI experts. Given the shortage of experts, their talent is in demand globally and their English speaking ability enables them to access global opportunities.

What you’re looking for is more of a Fractional AI Officer. Many startups, primarily those without a technical founder with AI expertise, attempt to hire a senior full-time AI specialist to build their AI infrastructure. What we find is that this strategy often leads to unexpectedly rapid turnover.

Senior AI specialists, with their advanced experience and skills, may find themselves underutilized in roles that don’t offer continuous high-level challenges. Mundane tasks, albeit essential in early development stages like data cleaning, are not engaging for these professionals.

Focus on fractional hiring. This approach allows startups to utilize senior AI talent for strategic, high-level work without committing to the costs and obligations of a full-time position. The startup can then leverage more junior AI professionals to carry out the execution (with check-ins from the Senior AI professional). It’s a budget-friendly solution that also ensures the AI projects progress to a point where they can attract additional funding.

Note: Just make sure you own the IP, especially when working with a software development shop.

Supplement Your AI Team With Nearshore Talent

The average LATAM-based, English speaking ML Engineer with 4-6 years experience commands a salary of roughly $5,500 – $6,500/month. This is more than 50% of the average US-based ML Engineer with equivalent experience.

In the investment world, we call this hybrid strategy “averaging down” – bringing down the average cost per engineer by supplementing the team with near shore talent.

How can I comfortably make it to my next round of funding without running out of cash, especially considering that what was once a $50mm round has become a $10mm round? Nearshore talent is the secret weapon.

The reduced cost of nearshore talent is not indicative of lower quality, it is the result of a lower cost of living. Latin America itself is producing homegrown unicorns all across the region, powered by the highly skilled AI specialists and software engineers.

Outsourcing is not an unfamiliar strategy to founders – however, most have historically turned to Eastern European and Middle Eastern markets for cost efficiencies. Leveraging nearshore LATAM AI talent is an increasingly popular strategy for startups.

This approach offers several advantages. Firstly, it provides access to a diverse pool of skilled AI talent at a lower cost compared to hiring in more expensive labor markets. Compared to European or Middle Eastern markets, the reduced geographical and time zone differences facilitate smoother collaboration and communication, ensuring that project workflows and team interactions are as efficient as they would be with a domestic team.

For startups, this means the ability to scale their AI capabilities rapidly and cost-effectively, without compromising on the quality of talent or the agility of operations. Nearshoring not only aids in optimizing the budget but also enriches the team with diverse perspectives, potentially leading to more innovative solutions in AI projects.

Navigating the AI talent landscape in today’s budget-conscious startup ecosystem demands more than just deep pockets; it requires a strategic, nuanced approach.

With Tesoro AI as your strategic talent acquisition partner, you can successfully maneuver through these challenges, ensuring your AI initiatives are not only cutting-edge but also in line with your startup’s financial realities and growth aspirations. Let us empower your startup with the right AI talent – domestic, hybrid, or nearshore – setting the stage for groundbreaking achievements and sustainable growth.