Observed Signal · Sep 17, 2026 · Policy Update · Source: Adweek · Impact: 4/5 · Sentiment: Positive
Benioff Rejects SaaS Apocalypse, Cites 72% Jump in AI Agents on Salesforce
Salesforce CEO Marc Benioff is pushing back against the 'SaaSpocalypse' narrative, which posits that AI agents will render traditional enterprise software obsolete. To support his stance, Salesforce reported a 72% increase in the number of agents created on its platform and a 90% rise in agentic work units (tokens) driving outcomes. These metrics, shared by SVP Gautam Vasudev, highlight the growing adoption of Salesforce's Agentforce platform. The company is also rethinking its pricing and interfaces to adapt to the AI-driven shift. Benioff has steadfastly defended the tech sector against Wall Street fears fueled by AI companies like Anthropic, in which Salesforce holds a stake. The article covers these developments, emphasizing Salesforce's efforts to demonstrate scalability and data protection in the AI era.
Salesforce is a major enterprise software and marketing cloud provider; its shift to AI agents signifies a market trend affecting how advertising and marketing campaigns are run, with implications for AI-driven media buying and customer engagement.
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Key Takeaways & Evidence Grounding
- Salesforce CEO Marc Benioff rejects the 'SaaSpocalypse' narrative.
- The number of AI agents built on Salesforce increased by 72%.
- Agentic work units (tokens) on Salesforce increased by 90%.
- Gautam Vasudev, SVP, shared the growth metrics.
- Salesforce holds a stake in Anthropic.
Connected Companies & Entities
2 Entities mapped“companies like Anthropic (which Salesforce has a stake in) will destroy traditional SaaS business models....”
“Salesforce is trying to back up CEO Marc Benioff’s increasingly forceful rejection of SaaSpocalypse....”
Ontology Mapping & Concepts
Related Market Signals & Shifts
Recent verified developments and strategic activity across this market segment.
Samsung to invest $1B in AI infrastructure firm Helix
Samsung Electronics and five affiliates will invest a combined $1 billion in Helix Digital Infrastructure, an AI infrastructure company launched by KKR and backed by Nvidia. Samsung Electronics contributes $500 million, with the rest from Samsung C&T, Samsung SDS, Samsung SDI, Samsung Life Insurance, and Samsung Fire & Marine Insurance. Helix, led by former AWS CEO Adam Selipsky, focuses on hyperscale data centers, power generation, transmission, and fiber-optic networks. The investment adds to over $10 billion already committed by other investors including KKR, Kuwait Investment Authority, Nvidia, and Vistra. The move allows Samsung to leverage its semiconductor, cooling, data center construction, and battery capabilities to expand in the AI infrastructure market.
Modal Labs closing in on $750M round at $15.75B valuation
AI inference infrastructure provider Modal Labs is nearing a $750 million funding round led by Accel at a $15.75 billion valuation, according to a source. This would more than triple its valuation from $4.65 billion in May. The round comes amid surging demand for inference services, with other startups like Baseten, Fireworks, and Fal also raising at higher valuations. Modal Labs, founded in 2021 by Erik Bernhardsson and Akshat Bubna, provides infrastructure for training and running AI models without managing servers. The company has surpassed $300 million in annualized revenue as of May. The funding talks follow a security incident in July where a customer's data was compromised, but Modal's platform was not breached.
GLM-5.3 Sparse Attention Impact on DRAM Memory TAM
This article analyzes the impact of sparse attention mechanisms, specifically DeepSeek Sparse Attention (DSA) used in Z.ai's GLM-5.3 model, on the total addressable market (TAM) for DRAM memory, including HBM and NAND. It explains that while sparse attention reduces KV cache memory and bandwidth during the attention operation, it does not reduce overall memory capacity requirements because the top-k selection still requires full context in HBM. The article discusses system optimizations like HiSparse, which offloads KV cache to host DRAM to overcome capacity bottlenecks. It also provides detailed performance and cost comparisons for serving GLM-5.3 on different hardware (GB200, GB300, MI355X) using inference engines like Dynamo-SGLang, Dynamo-TRT-LLM, and ATOM, highlighting cost-efficiency and interactivity trade-offs. The analysis includes a deep dive into GLM-5's architecture, including the lightning indexer, MLA configuration, and post-training pipeline.
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