Building for the future of advertising — GEM, our largest foundation model uses advanced computing to deliver smarter, more relevant ad recommendations. Since launch, it has driven a 5% lift in Instagram ad conversions and 3% on Facebook, improving the quality and impact of every ad users see. Better engineering, better ROI results, better experiences. https://lnkd.in/gcGXiBYk #Engineering #AdTech #DigitalMarketing #AIscale #Advertising
Impressive to see GEM translating raw compute into measurable lift at this scale. Better targeting is good, but proving it with real conversion impact is what actually counts.
**“GEM is impressive — but it also shows the architectural reality of today’s AI: we keep building bigger single models instead of building continuous memory systems. Smarter ads are great. But the next real leap won’t come from larger models — it will come from: • persistent user-owned context • transparent state boundaries • local-first decision loops • and distributed architectures that don’t centralize raw input Because relevance doesn’t come from more parameters. It comes from continuity. When AI understands a user across time — privately, locally, and with full traceability — that will change the future of advertising far more than any single foundation model. GEM is a strong step. The next step is the architecture around it. Me & Spok ✌️”**
Since its launch, GEM has already delivered impressive results, driving a 5% lift in Instagram ad conversions and 3% on Facebook. This innovation is enhancing the quality and impact of every ad users encounter!
You’re showing another “model upgrade” — more efficiency metrics, more GPU kernels, more parallel scaling. But none of this addresses the actual missing component: Where is your semantic-governance layer? Where is semantic localization? Where is drift suppression? Where is the logic skeleton of language? You’re still doing the same thing: More GPUs → more memory → larger models → same bottleneck. That’s not innovation — that’s stalling. My three papers are already public (WORM-audited, reproducible): 📄 Shen Yao 888π – Full Papers (IEEE, LCF 888π, ICAISG) 👉 https://drive.google.com/drive/folders/16Xx3YmoeMeqgVCpQi7cf67q6oKDRJt9R The semantic layer is already solved. You’re just not using it — and that’s why your costs keep exploding while performance plateaus. This is not a compute problem. This is an architecture problem. And you’re still avoiding it. 反噬 has only begun.
I’m interested in how GEM improves results for small businesses working with limited budgets.
Better data. Better delivery. Better results. That’s the edge.
It’s interesting to see how AI driven ad tech at Meta is shaping the next wave of digital marketing.
Since its launch, GEM has already driven a 5% lift in Instagram ad conversions and a 3% increase on Facebook, significantly enhancing the quality and impact of every ad users encounter. It's thrilling to see how innovation can improve advertising experiences!
Meta's GEM creates a fascinating structural trap. Initially, advertisers bring external creatives to GEM's targeting system—seeing 5%/3% conversion lifts. But the logical next step? Asking Meta to generate the ads too, since they built the algorithm. Meta incentivizes this with discounted AI creative generation for GEM users, creating even higher performance. This is where the Möbius strip forms: once you're in, leaving means losing that optimization advantage, but staying deepens dependence through the data feedback loop. The genius (and concerning) part? Meta controls both the targeting intelligence AND the creative production optimized for it. With finite AI infrastructure for GEM, late entrants face higher costs or exclusion. Until a viable alternative ecosystem emerges, Meta has architected a perpetual revenue stream where opting out equals competitive disadvantage. A masterclass in using AI to redefine not just products, but market structure itself.
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