Forecasts for tech's near future: AI-driven recommendations kill favorites, users opt into personalized ads, top talent will have Polygamous Careers, and more
Scott Belsky / Positive Slope :
Context & Ripple Effects
Scott Belsky's Positive Slope forecast belongs to a genre he helped establish with an earlier list of eight near-future tech themes — decentralized workplaces, community-as-a-service — that treated consumer taste and work structure as the variables to watch. This update narrows the lens: AI-driven recommendations replacing users' saved favorites, users opting into personalized ads, and top talent running multiple concurrent 'polygamous careers'.
The predictions have since found corroboration in later coverage: the displacement-of-curation thesis lines up with the New Yorker's examination of personalized, AI-generated entertainment and its risk of submerging human originality, while the careers thesis sits alongside Sam Altman and other tech leaders warning of seismic workforce changes at a 2023 conference.
First-order effects
- If recommendations displace favorites, discovery power moves fully to platform algorithms, and businesses whose traffic depended on being bookmarked must instead optimize for AI-mediated surfaces — the opening that Generative Engine Optimization startups like Profound and Bluefish AI now serve.
- Users consenting to personalized ads flips the current default: targeting becomes something audiences grant rather than something regulators and browsers restrict, changing how advertisers and platforms negotiate data access.
Second-order effects
- A labor market of concurrent careers pressures single-employer norms on compensation, benefits, and loyalty — the same workforce anxiety captured in reporting where managers at Google, TikTok, Adobe, and Dropbox used AI to justify firing decisions and accelerated workloads.
- Ad-supported platforms gain an incentive to make opt-in personalization attractive enough to win consent, shifting competitive energy toward recommendation quality rather than data acquisition.
Third-order effects
- If algorithmic feeds fully replace user-curated collections, shared cultural reference points weaken — the outcome the New Yorker flags when personalization submerges human originality — and taste itself becomes a product of the intermediating model.
- Careers structured as portfolios rather than tenure would push benefits, training, and identity off employers and onto individuals, a structural shift consistent with the profession-elimination scenario Altman and other leaders described.
The trend: Consumer tech is converging on AI as the intermediary for both taste and work — deciding what people see, what they're shown, and how they earn — making forecasts like this one checkpoints on whether curation and employment stay human-held.