The New Stack
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1 week ago
IBM's second-quarter revenue fell short of expectations at $17.2 billion versus the forecasted $17.86 billion, as enterprise customers redirected spending from software services toward AI hardware like servers and storage. CEO Arvind Krishna acknowledged the company failed to anticipate the magnitude of this shift and adapt quickly enough, causing numerous large deals to slip. Developers will face tighter software budgets and increased pressure to build custom integrations using open-source tools rather than licensed enterprise middleware.
The New Stack
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1 week ago
● 5 sources
Enterprises are increasingly concerned about protecting their data from AI labs while facing high inference costs from closed-source models like OpenAI and Anthropic, leading some to propose using lower-cost open-source models trained on private data instead. Major AI labs including OpenAI, Anthropic, and xAI have stated that API usage is not used to train their models and some offer zero data retention plans, but enterprises remain skeptical of these assurances. As a result, companies like Microsoft are considering alternatives such as open-weight Chinese models or homegrown models to reduce dependency on AI labs that are simultaneously competing in software categories where enterprises operate.
The New Stack
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1 week ago
● 3 sources
Open-source and open-weight AI models are claimed to be approximately four months behind proprietary frontier models while costing roughly ten times less per token, with Featherless demonstrating annual costs of $90,000 for their optimized GLM 5.2 model versus $1.5 million for GPT-5.5 or Claude Opus at 100 billion monthly tokens. The cost comparison shows that enterprises paying for proprietary models are primarily paying for enterprise wrapper features like integration and observability rather than raw model intelligence. As open-source models improve and approach frontier capability levels, enterprises may find themselves locked into expensive proprietary contracts while open alternatives become increasingly viable alternatives.
OpenAI Blog
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1 week ago
● 2 sources
Enterprises need to evaluate AI investments by measuring the amount of useful work generated per dollar spent rather than relying on traditional metrics. The article emphasizes efficiency improvements and identifying workflows that deliver the highest return on investment as key benchmarks for assessment. Organizations can allocate resources more effectively by focusing spending on AI systems that complete high-value tasks at scale.
The Neuron
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1 week ago
● 3 sources
Researchers have identified model distillation—training one AI system on outputs from another—as a threat to the business model of frontier AI companies, with Chinese competitors using the technique to quickly develop cheaper alternatives to expensive US models. Chinese companies have built networks of overseas "transfer stations" charging as little as 10% of official prices to bypass access restrictions, allowing them to collect vast datasets for training their own systems. If distillation becomes widespread, it could erode the returns on the billions spent by OpenAI, Anthropic, and Google on developing leading models, with some researchers warning that restrictions may simply push developers toward open-source alternatives instead.
Sifted
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1 week ago
Munich-based AI tax and accounting startup Skalar raised €12m in pre-seed and seed funding. The company was founded by Armin Vyß, who previously sold a startup to Klarna for €110m. Skalar plans to use the funding to expand its AI-powered tax and accounting software across European markets.
