datalabeling

🔍 $SAPIEN Consolidating – AI Data Play Holding Steady 📊

Price: $0.1479 (+0.20% 24h)
Volume: 16.74M $SAPIEN – moderate activity

Around key MAs: MA(7): 0.1473 | MA(25): 0.1490
Chart: Range-bound after pullback, testing support near $0.142–0.145 🤖

Trading Setup:
🔹 Entry: Accumulate $0.142–0.145 dip or bounce confirmation
🔹 Targets: $0.1538 → $0.16 → $0.173
🔹 Stop: Below $0.140
🔹 Leverage: Low 3x max – wait for AI sector strength

Pro Tips:
🔹 Monitor volume spikes for breakout
🔹 Scale in on support holds
🔹 Watch broader AI narrative rotation

$SAPIEN powers decentralized human data labeling for AI training – turning global knowledge into high-quality datasets via staking & rewards on Base! 🧠

#SAPIEN #AICrypto #DataLabeling #BaseChain #Binance #Crypto

AI COMPANIES UPGRADE WORKFORCE TO TRAIN SMARTER MODELS

The AI industry is undergoing a strategic shift—cheap, repetitive data labeling jobs are being phased out in favor of skilled, higher-paid professionals to train more intelligent models.

Previously, workers in countries like Kenya and the Philippines handled basic annotation tasks. Now, as companies develop reasoning-capable AI systems like OpenAI’s o3 and Google’s Gemini 2.5, there’s a growing demand for domain experts in biology, finance, and more.

🚀 Scale AI, Turing AI, and Toloka are leading the charge.
🔹 Meta invested $15B in Scale AI, raising its valuation to $29B.
🔹 Turing AI raised $111M in March, now valued at $2.2B.
🔹 Toloka secured $72M in a round led by Bezos Expeditions.

“The AI industry was for a long time heavily focused on the models and compute. Finally, it is accepting the importance of the data for training.” – Olga Megorskaya, CEO of Toloka

Turing CEO Jonathan Siddharth emphasized that complex tasks need real human input and insight into how AI models break down under pressure. His company now pays experts 20–30% more than their current salaries, showing that high-quality data is worth the premium.

Meanwhile, human oversight is expanding. More quality assurance workers now review AI-generated content, especially in local languages and culturally nuanced contexts. AI models are being trained to solve problems step-by-step—a method only possible with expert-driven “chain-of-thought” demonstrations.

The future of AI won’t just rely on bigger models—it will be built by smarter data, curated by smarter people.

#AI #MachineLearning #DataLabeling #OpenAI #ScaleAI