From: aidotengineer
Heath Black, Managing Director of Product at SignalFire, leverages insights from SignalFire’s proprietary AI/ML platform, Beacon, to discuss key strategies for talent acquisition. Beacon tracks over 650 million employees, 80 million companies, and 200 million open-source projects to provide proprietary ranking systems and market insights [00:02:33]. The focus is on effectively filtering candidates, identifying optimal locations, mastering recruitment timing, and crafting a compelling narrative to attract talent [00:03:02].
De-credentialization in AI Talent
A significant trend observed over the past decade is the “stark de-credentialization” in AI hiring [00:03:49]. AI startups are increasingly hiring engineers without PhDs or prestigious schooling [00:03:53].
- 2015: 27% of engineer hires were from top schools, and 16% had PhDs [00:04:03].
- 2023: These numbers dropped to 15% from top schools and 7% with PhDs, representing about a 50% decline for both metrics [00:04:09].
- Even for research scientists, approximately 40% possess advanced degrees (not necessarily PhDs), making up less than half of those in such roles [00:04:33].
This shift is attributed to a change in the market focus. While 2015 concentrated on foundational ML research, current work emphasizes applying models to real-world usage, requiring more ML Ops, product, and software experience, and understanding user interaction [00:04:51].
“Work experience has always been important, but it now far surpasses education in terms of the main aspect that you should be looking at here” [00:07:04]
This suggests recruiters should prioritize a candidate’s compiled body of work and open-source contributions over academic credentials [00:07:17]. Companies are advised to consider removing or softening academic requirements in job postings to broaden their talent pool [00:07:51].
Talent Mobility and Concentration
Historically, AI talent was concentrated in established tech giants like Google, Uber, Meta, and Apple [00:05:30]. However, there has been a notable shift of this talent to a group of nine companies termed the “AI V-League,” which have seen a massive concentration of talent [00:05:41]. This indicates a constantly moving market where traditional tech companies are now actively recruiting from these AI V-League firms [00:06:03].
Understanding net employee movement between companies is crucial. For example, OpenAI has a positive flow of talent from DeepMind, while Cohere shows a negative trend [00:06:30]. Knowing where people are coming from and where they are going is essential for effective filtering in team building [00:06:50].
The Significance of Location
Despite debates about San Francisco’s relevance, data indicates it remains a premier location for AI talent.
- San Francisco constitutes about 29% of all startup engineers, slightly down from a 2013 peak of 33%, but has been ticking up since 2021 [00:08:26].
- For Big Tech, 50% of engineers still reside in the San Francisco Bay Area [00:08:49].
- Specifically for AI, San Francisco leads with 35% of all AI engineers [00:09:03]. Seattle accounts for 22%, and New York about 10% [00:09:11]. Combined, these three cities demonstrate a disproportionately high concentration of AI talent compared to their overall share of engineers [00:09:24].
- San Francisco’s dominance is reinforced by its funding landscape, making up nearly 38% of all early-stage funding into AI startups, despite only holding 26% of total early-stage funding in the U.S. [00:09:48].
“Twitter doesn’t determine whether a market is dead; data does. Location still matters even in a highly distributed world that we live in today.” [00:10:15]
Recruiters should monitor location and funding markets to identify where talent and capital are flowing [00:10:35].
Timing for Recruitment
Effective recruitment involves understanding when people are most likely to leave their current roles and when they are inclined to join a company at a specific stage [00:11:11].
Retention Rates
Analyzing retention rates of AI V-League companies helps predict “poachability” [00:11:32]. For instance, Anthropic boasts a 66% four-year retention rate, while Perplexity hovers around 43-44% [00:11:43]. Understanding these rates helps determine the likelihood of a candidate responding to outreach [00:12:01].
Generational Differences
Generational behavior significantly impacts job mobility. In 2023, nearly 27% of Gen Z employees left their jobs, more than double the rate of Gen X [00:12:21]. Within four years of graduating, Gen Z averages 2.2 jobs, compared to Gen X’s 1.1 [00:12:39]. This trend is partly due to slower promotion rates for Gen Z and a general “penchant for people to take risk and bet on themselves” [00:13:05].
Historical Composition
SignalFire’s “historical composition” tool allows users to see snapshots of admired companies’ organizational structures at different times [00:13:42]. This includes identifying early engineers, sales leaders who scaled the company, and key hires for product launches [00:13:54]. This information helps recruiters:
- Identify candidates’ risk profiles [00:14:10].
- Understand their motivations [00:14:17].
- Spot potential “10x hires” crucial for company growth [00:14:20].
“You need to know when people join and leave companies because that will help you identify your 10xers and help you identify people that are likely to join company at your stage.” [00:14:59]
Crafting a Compelling Recruitment Narrative
Traditionally, pay and equity were the primary components of a recruitment narrative [00:16:23]. However, their effectiveness has diminished:
- Salary: From November 2022 to November 2024, the average tech salary only increased by 1.6% [00:16:36]. AI engineers, being a “hot ticket,” command a 5% salary premium and a 10-20% equity premium over other engineering roles, making them even more expensive [00:16:50]. Relying solely on salary is no longer feasible [00:17:14].
- Equity: There has been a “precipitous decline” in the exercise of vested shares [00:17:27]. In Q2 2024, only 33% of vested shares were exercised, down from 55% a few years prior [00:17:33]. This is driven by concerns over high valuations, the cost of liquid capital to exercise shares, and rapid market shifts in AI [00:17:47].
Companies must move beyond money and equity to craft a compelling recruitment narrative [00:18:11]. This involves highlighting:
- A close-knit, collaborative team environment [00:18:15].
- Speed and lack of friction in getting work done [00:18:23].
- A compelling mission [00:18:25].
- Opportunities for mental and career growth [00:18:29].
- Exploding markets and the chance to solve complex problems [00:18:31].
Conclusion
In a competitive landscape where many companies are “fishing in the same engineering pond” [00:18:51], using recruitment data provides a significant edge. Just as data is used to build products and models, it should be used to build teams, as a company’s team is its most valuable product [00:19:01].
For recruiters, this means:
- Adapting filters to account for the de-credentialization trend [00:19:13].
- Monitoring talent movement and concentration in key locations [00:19:16].
- Utilizing data to identify the optimal time to reach out to candidates and understand their career stages [00:19:23].
- Developing a narrative that transcends salary and equity [00:19:31].
For job seekers, these insights suggest:
- Knowing where admired individuals and companies are moving [00:19:50].
- Tracking how long people stay in roles to gauge treatment and potential [00:19:55].
- Understanding one’s own career arc and what is desired in a role beyond monetary compensation [00:20:03].