Tubegraph

Trends and shifts in AI workforce hiring

6 min read

From: aidotengineer

Heath Black, Managing Director of Product at SignalFire, shares insights gleaned from SignalFire’s proprietary AI/ML platform, Beacon, regarding current trends and shifts in AI workforce hiring [00:02:27]. Beacon tracks over 650 million employees, 80 million companies, and 200 million open-source projects, enabling SignalFire to build ranking systems and market insights to power their firm and support portfolio companies [00:02:33].

This data is used to identify the right people, in the right locations, at the right time, and close them with the right narrative [00:03:02].

Dcredentialization in AI Hiring

One significant trend observed over the last decade is a stark “de-credentialization” in AI hiring [00:03:49]. AI startups are increasingly hiring engineers without PhDs or degrees from prestigious schools [00:03:53].

In 2015:

By 2023:

  • These numbers declined by approximately 50%, with 15% from top schools and 7% with PhDs [00:04:09].

Even for research scientists, less than half (about 40%) hold advanced degrees [00:04:29]. This shift reflects a change in market focus from foundational ML research (around 2015) to applying models to real-world usage, emphasizing ML Ops, product experience, and understanding user interaction [00:04:51].

Hiring Implications

  • Work experience now far surpasses education in importance [00:07:04]. Focus on the body of work, including open-source contributions for new workers [00:07:17].
  • Assess if a PhD researcher is truly necessary or if an experienced engineer will suffice [00:07:36].
  • Consider removing or softening academic requirements in job postings to attract experienced talent [00:07:48].

Talent Mobility and Location

There has been a significant shift in AI talent concentration [00:05:22]. Historically, AI talent was centered at large tech companies like Google, Uber, Meta, and Apple [00:05:30]. Over time, this talent has concentrated in nine companies collectively referred to as the “AI V-League” [00:05:41]. Interestingly, traditional tech giants are now actively recruiting from these AI V-League companies [00:06:12].

Beacon also tracks net employee movement between AI V-League companies. For example, OpenAI has a positive flow of talent from DeepMind, while Cohere shows a negative trend [00:06:30]. Understanding where people are coming from and where they are going is crucial for effective filtering [00:06:50].

Despite debates about its demise, San Francisco remains a leading hub [00:08:10]:

  • San Francisco makes up about 29% of all startup engineers, slightly down from 33% in 2013, but ticking up since 2021 [00:08:23].
  • New York and Seattle have doubled their market share of engineers [00:08:36].
  • 50% of big tech engineers still reside in the San Francisco Bay Area [00:08:49].

Specifically for AI:

  • San Francisco leads, with 35% of all AI engineers residing there [00:09:00].
  • Seattle accounts for 22%, and New York for 10% [00:09:08].
  • These three cities disproportionately attract AI talent and funding, with San Francisco accounting for nearly 38% of all early-stage funding into AI startups in the US [00:09:28].

Location and Funding Markets

  • Location still matters, even in a distributed world [00:10:22]. San Francisco, Seattle, and New York are premier locations for AI talent [00:10:27].
  • Monitor location and funding markets to see where talent and capital are flowing [00:10:33].

Timing in Recruitment

Effective recruitment involves finding the right people at the right time [00:10:46]. This means identifying when individuals are most likely to leave their current roles and when they are likely to join a company at your stage [00:11:11].

Analyzing retention rates of AI V-League companies can help identify “poachability scores” [00:11:32]. For example, Anthropic has about a 66% four-year retention rate, while Perplexity hovers around 43-44% [00:11:39].

Generational behavior also plays a role:

  • In 2023, nearly 27% of Gen Z left their jobs, more than double the rate for Gen X [00:12:21].
  • Within four years of graduating, Gen Z averages 2.2 jobs, compared to 1.1 for Gen X [00:12:39]. This may be due to slower promotion rates, market layoffs, or a greater willingness of Gen Z to take risks and bet on themselves [00:12:52].

SignalFire’s “historical composition” tool provides insights into target companies’ organizational structures at different points in time, revealing who the first engineers were, or who led sales growth [00:13:42]. This helps in understanding a candidate’s risk profile, motivations, and potential as a “10x hire” [00:14:10].

Strategic Timing

  • Understand when competitors are likely to lose people [00:14:34].
  • Track changes in target profiles to identify potential shifts [00:14:41].
  • Study generational patterns of job changes [00:14:50].
  • Know when people join and leave companies to identify 10xers and those likely to join your stage of company [00:14:59].

Crafting the Narrative Beyond Compensation

Relying solely on pay and equity as recruitment narratives is no longer sustainable [00:16:23].

  • From November 2022 to November 2024, the average tech salary increased by only 1.6%, with a sharp decline in equity granted [00:16:32].
  • AI Engineers are in high demand, commanding a 5% salary premium and a 10-20% equity premium over other engineering roles [00:16:50]. This makes already expensive roles even more costly [00:17:04].
  • The use of equity as a selling point has declined due to concerns over valuations, the cost of liquid capital to exercise shares, and rapid market shifts [00:17:47]. In Q2 2024, only 33% of people exercised vested shares, down from 55% a couple of years earlier [00:17:33].

Companies must focus on non-monetary aspects of their narrative [00:18:11]:

  • A close-knit environment and collaboration with founders [00:18:15].
  • Speed and lack of friction in getting work done [00:18:20].
  • A big mission and opportunities for career growth [00:18:25].
  • Exploding markets and the chance to solve complex problems [00:18:31].

Drawing on Kurt Vonnegut’s concept of story shapes, companies should understand their own narrative arc—their triumphs, where they are today, and where they are going [00:15:11]. This enables a compelling story that goes beyond compensation alone [00:16:06].

Narrative Strategy

  • Salary and equity should not be the sole selling points [00:17:17].
  • Articulate the unique non-financial benefits and opportunities your company offers [00:18:15].
  • Understand and communicate your company’s story arc to attract top talent [00:16:06].

Conclusion

In a competitive landscape where many companies are “fishing in the same engineering pond,” using recruiting data provides a significant edge [00:18:48]. Just as data is used to build products and models, it should be leveraged to build effective teams, as the team itself is a company’s most valuable product [00:19:01].

Key takeaways for recruiting AI talent:

  • Acknowledge “de-credentialization” and filter accordingly, focusing on work experience over education [00:19:13].
  • Location still matters; monitor talent movement [00:19:16].
  • Utilize data to identify the right time to reach out to candidates and understand their tenure at previous companies [00:19:23].
  • Craft a compelling narrative that highlights non-monetary benefits and the company’s unique journey [00:19:31].

For job seekers, understanding these trends can also be beneficial [00:19:47]:

  • Know where people you admire go, not just companies but the space [00:19:50].
  • Observe how long they stay to gauge how they are treated and the future prospects of the area [00:19:55].
  • Understand your own career arc and what you want from it [00:20:03].

Graph View

Backlinks