The letter comes after Benioff joked at a company event on Monday that ICE was monitoring international employees in attendance, sparking immediate backlash. Employees at Salesforce are circulating an internal letter to chief executive Marc Benioff calling on him to denounce recent actions by US Immigration and Customs Enforcement, prohibit the use of Salesforce software by immigration agents, and back federal legislation that would significantly reform the agency. The letter specifically cites the "recent killings of Renee Good and Alex Pretti in Minneapolis" as catalysts, calling them the "devastating indictment of a system that has discarded human decency." It's unclear how many signatories the letter has received so far. The letter, which has not been reported on previously, is being organized amid Salesforce's annual leadership kickoff event this week in Las Vegas.

'No reasons to own': Software stocks sink on fear of new AI tool The new year was supposed to bring opportunities for beaten-down software stocks. Instead, the group is off to its worst start in years. The release of a new artificial intelligence tool from startup Anthropic on Jan. 12 rekindled fears about disruption that weighed on software makers in 2025. TurboTax owner Intuit tumbled 16% last week, its worst since 2022, while Adobe and Salesforce, which makes customer relationship management software, both sank more than 11%. All told, a group of software-as-a-service stocks tracked by Morgan Stanley is down 15% so far this year, following a drop of 11% in 2025.

The rapid expansion of cross-border e-commerce (CBEC) has created significant opportunities for small- and medium-sized sellers, yet financing remains a critical challenge due to their limited credit histories. Third-party logistics (3PL)-led supply chain finance (SCF) has emerged as a promising solution, leveraging in-transit inventory as collateral. We propose an advanced credit risk management framework tailored for 3PL-led SCF, addressing the dual challenges of credit risk assessment and loan size determination. Specifically, we leverage conditional generative modeling of sales distributions through Quantile-Regression-based Generative Metamodeling (QRGMM) as the foundation for risk measures estimation. We propose a unified framework that enables flexible estimation of multiple risk measures while introducing a functional risk measure formulation that systematically captures the relationship between these risk measures and varying loan levels, supported by theoretical guarantees. To capture complex covariate interactions in e-commerce sales data, we integrate QRGMM with Deep Factorization Machines (DeepFM). Extensive experiments on synthetic and real-world data validate the efficacy of our model for credit risk assessment and loan size determination. This study explores the use of generative models in CBEC SCF risk management, illustrating their potential to strengthen credit assessment and support financing for small- and medium-sized sellers.

Multiple Choice Question (MCQ) answering is a widely used method for evaluating the performance of Large Language Models (LLMs). However, LLMs often exhibit selection bias in MCQ tasks, where their choices are influenced by factors like answer position or option symbols rather than the content. This bias undermines the reliability of MCQ as an evaluation framework. Most existing selection bias metrics require answer labels and measure divergences between prediction and answer distributions, but do not fully capture the consistency of a model's predictions across different orderings of answer choices. Existing selection bias mitigation strategies have notable limitations: majority voting, though effective, is computationally prohibitive; calibration-based methods require validation sets and often fail to generalize across datasets. To address these gaps, we propose three key contributions: (1) a new unsupervised label-free Permutation Bias Metric (PBM) that directly quantifies inconsistencies in model predictions across answer permutations, providing a more precise measure of selection bias, (2) an efficient majority voting approach called Batch Question-Context KV caching (BaQCKV), to significantly reduce computational costs while preserving bias mitigation effectiveness, and (3) an unsupervised Low-Rank Adaptation (LoRA-1) fine-tuning strategy based on our proposed metric and the BaQCKV that mitigates selection bias, providing a computationally efficient alternative that maintains model generalizability. Experiments across multiple MCQ benchmarks demonstrate that our approaches reduce bias, increasing consistency in accuracy while minimizing computational costs.

In this section we provide a detailed proof for the correctness and completeness of the ICD algorithm. For easier referencing we describe ICD in Algorithm 1, and describe the ICD-Sep conditions. Z is a subset of ICD - Sep(A, B) given r { 0, .. . O that are connected in G and disconnected in D, and such that A is not an ancestor of B in D . O that are connected in G and disconnected in D .

Salesforce's CEO backtracks after saying Trump should send troops into San Francisco In tech this week: The CEO of the city's largest private employer apologizes, Amazon Web Services' outage and OpenAI's Sora makes waves What I'm watching this week: South Park's caricature of Peter Thiel and his obsession with the antichrist . Read our reporting on the show's inspiration: Thiel's bizarre off-the-record lectures on the subject. And now, let's get into things. The co-founder and CEO of Salesforce, said last week that Donald Trump should make good on his threats to send the US national guard into San Francisco, despite resistance from local leaders. Even Marc Benioff's own public relations manager was aghast at his remarks, according to the New York Times .

We introduce SCUBA, a benchmark designed to evaluate computer-use agents on customer relationship management (CRM) workflows within the Salesforce platform. SCUBA contains 300 task instances derived from real user interviews, spanning three primary personas, platform administrators, sales representatives, and service agents. The tasks test a range of enterprise-critical abilities, including Enterprise Software UI navigation, data manipulation, workflow automation, information retrieval, and troubleshooting. To ensure realism, SCUBA operates in Salesforce sandbox environments with support for parallel execution and fine-grained evaluation metrics to capture milestone progress. We benchmark a diverse set of agents under both zero-shot and demonstration-augmented settings. We observed huge performance gaps in different agent design paradigms and gaps between the open-source model and the closed-source model. In the zero-shot setting, open-source model powered computer-use agents that have strong performance on related benchmarks like OSWorld only have less than 5\% success rate on SCUBA, while methods built on closed-source models can still have up to 39% task success rate. In the demonstration-augmented settings, task success rates can be improved to 50\% while simultaneously reducing time and costs by 13% and 16%, respectively. These findings highlight both the challenges of enterprise tasks automation and the promise of agentic solutions. By offering a realistic benchmark with interpretable evaluation, SCUBA aims to accelerate progress in building reliable computer-use agents for complex business software ecosystems.

Artificial Intelligence (AI) has transitioned from a futuristic concept reserved for large corporations to a present-day, accessible, and essential growth lever for Small and Medium-sized Enterprises (SMEs). For entrepreneurs and business leaders, strategic AI adoption is no longer an option but an imperative for competitiveness, operational efficiency, and long-term survival. This report provides a comprehensive framework for SME leaders to navigate this technological shift, offering the foundational knowledge, business case, practical applications, and strategic guidance necessary to harness the power of AI. The quantitative evidence supporting AI adoption is compelling; 91% of SMEs using AI report that it directly boosts their revenue. Beyond top-line growth, AI drives profound operational efficiencies, with studies showing it can reduce operational costs by up to 30% and save businesses more than 20 hours of valuable time each month. This transformation is occurring within the context of a seismic economic shift; the global AI market is projected to surge from $233.46 Billion in 2024 to an astonishing $1.77 Trillion by 2032. This paper demystifies the core concepts of AI, presents a business case based on market data, details practical applications, and lays out a phased, actionable adoption strategy.

Salesforce has slashed another 4,000 jobs from its customer support workforce as the tech giant doubles down on artificial intelligence, even as the company reports strong financial results. AI agents now reportedly handle about one million customer conversations. In a recent episode of The Logan Bartlett Show, CEO Marc Benioff justified the cuts by saying he "needs less heads" as Salesforce invests heavily in AI across its operations. Earlier this year, Benioff boasted that AI was already doing 30 to 50 percent of the work, which he framed as efficiency gains – a 17 percent cost reduction achieved after shedding 1,000 people in February. On Wednesday, the Slack owner reported revenue topped 10.2bn for the quarter ending July 31, up 10 percent from the same period last year.

Ho et. al (2024) attempts to estimate the degree of algorithmic progress from language models. They collect observational data on language models' loss and compute over time, and argue that as time has passed, language models' algorithmic efficiency has been rising. That is, the loss achieved for fixed compute has been dropping over time. In this note, I raise one potential methodological problem with the estimation strategy. Intuitively, if part of algorithmic quality is latent, and compute choices are endogenous to algorithmic quality, then resulting estimates of algorithmic quality will be contaminated by selection bias.