I had a great conversation with @MollyJongFast about the impact that #AI will have on jobs and the economy and the new edition of my book "Rise of the Robots: Technology and the Threat of a Jobless Future" on the Fast Politics Podcast. Our conversation starts at 21:45.
ECONOMY
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GPT-5.6 Variants Target Lower Dollars per AI Task
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we have heard enterprises on their concerns about AI costs, and 5.6 sol is a huge step forward for dollars-per-task, as are terra and luna
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Muse Spark 1.1 Compares Favorably on Agentic Inference Cost
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Meta Muse Spark 1.1 is a very good agentic model. But above all, it is incredibly affordable and cost-efficient! Take a look at the costs compared to the competition.
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GLM 5.2 Orchestrator Preview Targets Opus Performance at Lower Cost
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PERPLEXITY : A new GLM 5.2-powered preview orchestrator model is now available on Perplexity Computer. The model delivers the same performance as Opus 4.8 at 0.344x the cost. Tokensaving
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GLM 5.2 With an Advisor Cuts Benchmark Task Costs Versus Opus
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We measured cost per task against GLM 5.2 as the baseline. On WANDR, GLM 5.2 + advisor runs at 2.1x versus Opus at 6.1x, averaging roughly half the cost of Opus across all benchmarks.
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Perplexity Trains GLM to Escalate to a Frontier Advisor
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We’ve been post-training a version of GLM that is trained to escalate to a frontier model inside the Computer harness. When paired with an advisor, this model functions at Opus 4.8 grade performance at a fraction of the cost. Available now as a research preview!
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Perplexity Post-Trains GLM 5.2 for Lower-Cost Computer Orchestration
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We're releasing a research preview of a new orchestrator model in Perplexity Computer. The model is an adapted version of GLM 5.2, post-trained for the Computer harness. It delivers near-frontier performance at 0.344x of the cost of Opus.
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Grok 4.5 Approaches Opus 4.8 Benchmarks at Lower Cost
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Grok 4.5 is shaking up the community because it seems to be as effective as Opus 4.8, or even more effective on some benchmarks, for nearly a quarter of the price. It still needs to be tested under real-world conditions. [Traduit de FR vers EN]
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Self-Supervised Learning Can Reduce Model Costs
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How to Reduce #AI Model Costs with Self-Supervised Learning
by @antgrasso #ArtificialIntelligence #MachineLearning #ML -
Compute Capacity Shapes Competition With Anthropic
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I spoke with many in the industry at ACL in San Diego this week, and they say it's all about who has the compute. If you want to beat Anthropic, you have to have the compute.