In the fiercely competitive market for artificial intelligence (AI) talent, startups face a significant challenge: competing against tech giants like Meta and OpenAI, which boast the capacity to offer lucrative million-dollar salaries. This compensation gap raises the critical question of how smaller, early-stage companies can attract and retain top AI professionals without the deep financial resources of these corporate behemoths.

Experts attending TechCrunch Disrupt 2025 have shared valuable insights on how startups can strategise compensation to remain competitive and sustainable. Instead of trying to match the high salaries offered by large firms, startups are advised to focus on crafting generous, fair, and flexible compensation frameworks that include substantial equity packages. In Vu, co-founder of Pulley, stresses that startups typically attract candidates through different channels than stable tech firms, and that the imperative to compete directly on salary is often misplaced. She advocates for offering equity-based compensation oriented toward the 90th percentile, underscoring the importance of generosity in startup equity allocations to appeal to candidates and foster loyalty as the company grows.

Randi Jakubovits, head of talent at 645 Ventures, concurs, emphasising the necessity for startups to clearly communicate and align candidate goals with the compensation package, including a thorough explanation of vesting cliffs. This transparency is key to building trust and preventing future dissatisfaction. Moreover, startups should avoid locking in rigid compensation structures too early. Instead, they should ensure fairness from the outset and maintain the flexibility to adapt packages as the company evolves, thus reducing legal risks and internal conflicts.

Rebecca Li Witting, founder of Epigram Legal and fractional general counsel, highlights the importance of establishing clear and equitable compensation standards. This practice not only mitigates legal pitfalls, such as unequal pay based on gender, which is increasingly regulated and prohibited, but also aligns with ethical hiring principles. She advises startups to focus not just on the compensation process, but also on understanding the motivations of the types of talent they aim to attract.

These insights align with findings from industry data showing that despite salary disparities, startups can successfully compete by offering compelling equity stakes and strong company cultures. The prevalence of reverse acquihires, where large tech companies recruit talent directly from startups, presents a challenge by potentially destabilising smaller firms. However, the promise of equity-based rewards, which may translate into meaningful financial gains as startups scale, remains a powerful retention tool.

Additionally, broader research into AI-related roles reveals that non-monetary benefits such as parental leave and remote work options play a significant role in attracting talent. These perks, combined with transparent and fair compensation packages, contribute to a comprehensive and appealing employment proposition.

Nevertheless, the landscape is complex. Studies have shown that the integration of AI tools in the workplace sometimes leads to reduced compensation perceptions for workers using such tools, potentially exacerbating inequality for those without robust contractual protections. This underscores the importance for startups to establish clear compensation frameworks that shield workers and maintain equity as AI technologies and roles evolve.

In conclusion, startups operating with limited budgets can creatively and fairly compete for AI talent by emphasising transparent, equitable, and adaptable compensation strategies. Prioritising substantial equity offerings, aligning compensation with candidate goals, and embedding flexibility to adjust offerings as the company scales form the cornerstone of a successful talent acquisition and retention approach. This balanced strategy not only mitigates legal and ethical risks but also cultivates motivation and long-term engagement from valuable AI professionals.

📌 Reference Map:

  • [1] (Mezha.net) - Paragraphs 1, 2, 3, 4, 5, 7, 8
  • [2] (TechCrunch) - Paragraphs 2, 3, 4
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  • [4] (Arxiv.org 2507.20410) - Paragraph 6
  • [5] (Arxiv.org 2501.13228) - Paragraph 7
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Source: Noah Wire Services