All posts tagged: Pragmatic

New CFTC advisory signals ‘pragmatic shift’ for sports prediction markets, says expert

New CFTC advisory signals ‘pragmatic shift’ for sports prediction markets, says expert

A new advisory from the US Commodity Futures Trading Commission (CFTC) could potentially reshape the debate around sports prediction markets. As platforms continue to offer contracts tied to real-world outcomes, from elections to the Super Bowl, regulators are signaling they may be willing to oversee the industry rather than shut it down altogether. For years, prediction markets, or the like, have tested the edges of US financial regulation. That said, the CFTC’s latest announcement does not exactly settle that discussion. Instead, it sketches out how exchanges should manage sports-related contracts if they are ultimately allowed to exist. The advisory represents a pragmatic shift. By referring to Designated Contract Market (DCM) Core Principles, the CFTC is saying ‘if this is allowed, it must be done like other products in our markets.’ In practical terms, this signals that the Commission is open to these listings if the courts determine that they are legal and the CFTC has authority over them. Peter Sanchez Guarda, former CFTC Special Counsel To some observers, the tone matters. Peter Sanchez Guarda, who …

Pragmatic by design: Engineering AI for the real world

Pragmatic by design: Engineering AI for the real world

Drawing on data from a survey of 300 respondents and in-depth interviews with senior technology executives and other experts, this report examines how product engineering teams are scaling AI, what is limiting broader adoption, and which specific capabilities are shaping adoption today and, in the future, with actual or potential measurable outcomes. Key findings from the research include: Verification, governance, and explicit human accountability are mandatory in an environment where the outputs are physical—and the risk high. Where product engineers are using AI to directly inform physical designs, embedded systems, and manufacturing decisions that are fixed at release, product failures can lead to real-world risks that cannot be rolled back. Product engineers are therefore adopting layered AI systems with distinct trust thresholds instead of general-purpose deployments. Predictive analytics and AI-powered simulation and validation are the top near-term investment priorities for product engineering leaders. These capabilities—selected by a majority of survey respondents—offer clear feedback loops, allowing companies to audit performance, attain regulatory approval, and prove return on investment (ROI). Building gradual trust in AI tools is …