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Employers are struggling to remain relevant to an increasingly digital and borderless workforce. Remote working trends, accelerated by COVID-19, are a prime example of deep-rooted change which is breaking down traditional workplace, workforce, and worklife dynamics. However, the shift towards a more talent-centric paradigm started long before Covid-19 but has rapidly accelerated since, culminating in the Great Resignation.
Alternate employment models are gaining traction, and the very definition of ‘work’ is in a state of flux. Independent professionals leading the charge towards a decentralized workforce.
WorkTech tools have become omnipresent but fragmented in the daily lives of digital workers. Platforms accumulate rich worker data that remains siloed in the hands of the companies who own the products that workers use. Companies then monetize the data and charge users for services. There is a clear lack of joined up thinking, systems, and data flows in the WorkTech arena to benefit users.
Platform data is not a source of truth. It is used as a stand-in for high-level, old-school referrals and networks. Each party manipulates the available data as a first-pass filter based on needs, but there is no inherent trust in the final results, i.e. further verification is resquired.
In a recent survey, 78% of candidates admitted lying on their resumes. As such, 73% of U.S. employers perform employment pre-hire checks and 51% verify education credentials and certifications [Velocity Network]
Diversity & Inclusion remains a huge challenge. Companies are increasingly challenged to establish and act on DEI initiatives; however, achieving DEI goals is particularly difficult when using biased legacy systems that rely on biased data. DEI is not a policy nor strategy to be acted upon but should be a principle incorporated into processes that requires rethinking from the ground up.
Passive referral systems, like LinkedIn’s recommendations or Upwork’s and Glassdoor's 5-star systems are broken. 1.) Users likely do not know the recomender, and therefore have no reason to trust the recommendation and 2.) The recomender has no "skin in the game," likely biasing reviews. These systems can also be gamed further reducing trust in outcomes.
Ultimately, the Journal identified 506 spikes at 403 companies. Most of the spikes were positive, with just over 400 at 328 companies having a higher percentage of five-star reviews than the surrounding months. [Wall Street Journal]
Recruitment agencies don’t really know the candidates they approach. As such, they waste a lot of time trying to accumulate rudimentary knowledge of a candidate through non-scalable processes. It's no surprise that freelancers primarly rely on their personal networks, leveraging more intimate relationships, to find work.
In the US, 41% of freelancers find work through a previous client, 38% through friends and family, and 37% through professional contacts. “General freelance websites” account for only 29% of job matches and employment agencies account for a mere 12% among freelancers.
The proliferation of Web3 technologies across both financial and media sectors (there are more but these two are obvious) has shown the transformative force of the technology upon established industries and heretofore unchallenged business models. It is fair to assume that Web3 innovation does not stop here. Any legacy industry, service, or tool that has failed to put users first stands to be disintermediated.
Web3 is inherently composable. It allows for a new type of horizontal organisational model that leverages ecosystem partnerships and existing tools and platforms to reach our design and objectives i.e. don't build a DeFi primitive -> integrate or fork the code. This creates huge advantages for speed and experimentation.
DAO’s are now a viable construct to organize self-sustaining platforms. Web3 communities have shown potential to organize around shared core beliefs, and shared upside. By handing governance and execution over to users, a DAO's incentive structure aligns with user needs and is able to deliver optimal value with lower overhead. Having skin-in-the-game also incentivizes participation that fuels the DAO's value creation.
Web3 has shown that anonymous/pseudonymous groups can self-organise and build some great things, all under the guise of a cartoon avatar and a moniker, or alter-ego. The decoupling of a personal identity from work or output is an exciting concept to explore for the future of decentralized markets as well as a means to potentially reduce biases.
Freelancers want to work at companies that align with their values. Startups represent a particularly interesting client-base, but freelancers have difficulty connecting with nascent companies that have limited public presence, and they are weary about potential financial risk in cash-strapped companies.
Freelancers offer on-demand skills with no overhead. Hiring full-time talent is time-consuming, expensive, and risky. Freelancers offer flexibility that is particulary valuable for early-stage startups that must remain agile.
Getting started is hard for freelancers and clients. Professionals are not taught how to be freelancers, which requires specific skills (e.g. project management, sales/marketing, financial savvy, etc.). Establishing personal baselines such as fees, contracts, and hours can be challenging and requires a lot of independing resarch, trial and error. Likewise, employers may lack experience engaging with freelancers and confront frictions around onboarding, management, and payment.
Personal relationships are pivotal. Freelancers primarily rely on personal networks and word-of-mouth to find jobs. This creates a cold-start problem where you require experience to gain experience. Personal referrals cut down the search time and expedite the vetting process for both employers and talent.
Current platforms commoditize talent. As such, talent struggles to differentiate themselves and competition for work is fierce. Employers then have difficulty comparing talent based on abstract data points (e.g. star ratings), such that hiring anyone but the best is risky.
High platform fees fuel churn and lowers quality. Top talent that is able to effortlessly secure jobs remain on platforms, despite high fees. However, good talent, particualrly those getting started, leave as soon as they build a network to avoid high fees. The remaining talent inhabit a pool of increasing mediocrity with little loyalty to the platform. Employers only return for the quantity and diversity of talent to address new talent needs quickly, not expecting quality.
Recruiting tools cater to traditional FTE hiring practices. The onslaught of products/tech has also created fatigue and diminishing returns. Meanwhile, companies are increasingly focusing on lean headcount and core needs. Solutions are trending towards self-service and in-house teams to facilitate new intros to better talent, but few tools are being developed to support a truly decentralized workforce.
Freelance support is fragmented. The only way a 100-million-worker-scale HR would work is by a peer-to-peer support model, where passionate enthusiasts of a labor marketplace take it upon themselves to share their wisdom/best practices with newer entrants. Hiring a customer service/HR department at a massive scale has to be impractical/costly. Workers need to share their stories.