Internal comms teams are entering the new year with stronger foundations and clearer priorities. However, most still feel stretched as expectations outpace resources. Email remains the most reliable and effective channel, measurement continues to lag, and intranets are both the biggest challenge and top improvement priority. Manager communication remains the leading trend, AI is now part of daily workflows, and automation is the next big focus. Teams plan to elevate employee voices, strengthen channel ecosystems, and invest in tools that improve efficiency.
The Uber Intelligence tool helps brands combine their own customer data with Uber's location and delivery insights using privacy-safe clean room tech. This unlocks targeting options for behaviors like business travel or dining habits. With in-app placements, in-car screens, and loyalty integrations, Uber is positioning itself as a retail media network built on movement data. Companies can now tap into this “terrestrial data” to create contextual ad experiences and explore non-traditional partnerships, like hospitality or CPG, that align with users' real-world journeys.
Experiential rewards drive significantly higher engagement and spending than physical items. Customers were up to 4x more likely to choose an experience, spent 43.3% more before the reward, and spent 63.7% more after redeeming it. Framing items as experiences increased repeat purchase intent by 8% and boosted positive word of mouth by 9.2%. Group-based activities also increased reward redemption likelihood by up to 21%.
Many content programs fail not from effort but from misaligned focus. Common mistakes include undifferentiated content, overemphasis on top-of-funnel posts, weak thought leadership, poor LinkedIn and executive presence, and case studies that don't show real results. Teams also underuse repurposing, try to address all buyers, spread across too many topics, and lack the right strategy or expertise to integrate SEO, social, and messaging. To fix this, audit your content, address the biggest gaps first, and consider fractional strategists or specialists to build a program that drives leads, authority, and measurable results.
The Salvation Army turned sold-out items into a successful campaign by using AI to create urgency. Instead of promoting available products, the ads featured recently sold pieces, triggering FOMO. With Gemini and Nano Banana, raw inventory images became high-end editorial visuals in just 30 days. The campaign lowered cost per store visit by 138% and delivered a 2.6X higher CTR than Google benchmarks.
A tutorial for creating high-quality images at scale by combining Screaming Frog with the Nano Banana Pro model through the Gemini 3 API. The process uses Screaming Frog as an easy access point for the “Gemini 3 pro image preview” model, which can generate accurate vector illustrations that match page titles. Limiting crawl speed to 2 to 3 URLs per second improves consistency, and exports provide all generated assets in one file. More specific prompts lead to stronger outputs and can outperform existing site images.
Gamma is a tool that turns short prompts into polished presentations, documents, and webpages. This page is a library of templates designed to speed up content creation. It organizes prompts by role and use case to simplify picking the right structure. Each prompt provides a clear starting point that can be tailored to specific needs.
Modern GTM is shifting from buying tools to creating custom internal systems that power growth. AI and low code now let teams build bespoke workflows and micro automations that sit beneath the visible stack and create differentiation that competitors cannot copy. This hypertail of custom logic is turning CMOs into system architects who must manage complexity, govern internal workflows, and decide where custom design drives advantage. Startups gain most by using fewer tools more deeply and extending them with purpose-built logic that improves activation and retention. The companies that win will focus on smart workflows and intentional AI rather than tool count.
Used responsibly, fear-based messaging can move audiences from indecision to action by surfacing existing concerns and offering immediate solutions. Examples range from pet insurance companies using real customer stories to cybersecurity firms offering fast resolutions to common threats. The tactic succeeds when tied to credible fears and clear next steps. Exaggeration, manipulation, and vague threats reduce trust and invite legal risk. Executing this approach well means listening closely to your audience, respecting their concerns, and delivering relief through simple, believable solutions.
OpenAI CEO Sam Altman made the call last week to pause the company's side projects for eight weeks and focus on improving ChatGPT. The decision was a major strategic course correction. Altman instructed employees to boost ChatGPT through the better use of user signals. OpenAI is set to release a new model this week, and it also plans to release another model in January with better images, improved speed, and a better personality, after which it will end the 'code red'.
Google plans to launch the first of its AI-powered glasses in 2026. The company has planned audio-only glasses that will allow users to speak with Gemini and glasses with an in-lens display to show users information. The glasses will be built on top of Android XR in collaboration with Samsung, Gentle Monster, and Warby Parker.
We're in the middle of a period of rapid transition in energy. Solar is decosting at an accelerating rate, coal is shrinking, and nuclear is flat. Primary production will soon be mostly solar. Batteries will be everywhere, and the grid will shrink. Datacenters, which are already building their own captive power plants, will become power sources for their communities. Substantial cost improvements are still needed to make space AI competitive.
Eli Lilly became the first pharmaceutical company to hit a trillion-dollar valuation on November 21. The company recently partnered with Nvidia to build an AI supercomputer. Lilly wants to position itself as a technology company. Its CEO, Dave Ricks, recently appeared on a podcast to discuss the company's aspirations. This article presents an analysis of the discussion.
Google's Director of AI Dr. Ali Arsanjani, and Datadog's VP of Engineering Sajid Mehmood, sat down to discuss the current and future states of AI, ML, and LLMs on Google Cloud. This guide distills the top 7 insights and actions for technical leaders [9], covering everything from upskilling teams to observability best practices. Read
We are in the early stages of a once-in-a-generation shift. The economics of building software have changed dramatically with agentic coding. Previous assertions that LLMs make too many mistakes or can't understand codebases or frameworks are rapidly becoming completely false. The technology is going to totally transform the software development industry.
Model weight first companies are companies where the majority of the software was code-generated. These companies do not require as much context engineering and do not need to adhere to classical principles as much as corporate companies. They achieve much higher velocity/productivity/success rates using AI because they are not forcing models to follow pre-existing dogma. Companies having problems with AI are likely trying to bend the technology to their whim rather than working with it.
Apple chip chief Johny Srouji told staff on Monday that he is staying at the company for now. There were recent rumors and speculations about his future at Apple, but he doesn't plan on leaving anytime soon. Apple CEO Tim Cook has been working aggressively to retain Srouji with a campaign that included a substantial pay package and the potential of more responsibility down the road.
Paramount Skydance is launching a hostile bid to buy Warner Bros. Discovery. It will go straight to shareholders with an all-cash, $30 per share offer. The offer is supported by equity financing from the Ellison family, Middle Eastern financing partners, Jared Kushner's Affinity Partners, and other parties. Those partners have agreed to forgo any governance rights as part of their investment. The modifications allow the deal to be outside of the jurisdiction of the Committee of Foreign Investment in the US.
With Agent Bricks, you can build agents that are accurate, reliable and grounded in your unique data. Now you have a clear path to production. Build agents that work.
Token pricing. Outcome-based value. Hybrid models. Every AI company is inventing their own pricing model because traditional SaaS economics don't apply when every user extracts wildly different value.
OpenAI's state of the enterprise AI report shares a comprehensive look at how enterprises are adopting AI, what workers say they're gaining, and how organizational leaders are turning experimentation into measurable productivity and new capabilities. The analysis draws on real-world usage data from OpenAI's enterprise customers and a survey of over 9,000 workers across 100 enterprises. It is clear that enterprise adoption is accelerating in both breadth and depth. The technology is reshaping how people work, how teams collaborate, and how organizations build and deliver products.
Anthropic is launching Claude Code in Slack, allowing devs to run full coding sessions directly from threads. With workflow automation, repo context detection, and progress updates, this shift embeds AI deeper into team collaboration rather than traditional IDEs.
Alignment research is part of the core research problem. Labs that treat alignment as a constraint will hit a ceiling, while those that figure out how to build models that genuinely understand human values will pull ahead. AGI requires alignment. The integrated approach looks like the strongest approach.
Nvidia, OpenAI, and Oracle are all putting pressure on each other. If Nvidia stopped investing in OpenAI, OpenAI might not have the cash to sign that deal with Oracle. This might mean that Oracle wouldn't have the money to buy Nvidia's chips. Some of the companies' reported revenues might be more fragile than they look.
This post explores backend alignment with Megatron, reinforcement learning improvements, speculative decoding with online SFT, and FP8 precision for scalable model training.
Not sure how to use Notion Agents? Here are 100 outcome-oriented examples [10] with “Agent Steps” that show exactly how and what a Notion Agent will produce. Think dashboards databases, and reports, all organized by function (analytics, strategy, event planning, CX,
Agentic browsers all face the threat of indirect prompt injection, which can appear anywhere and cause agents to take unwanted actions. Google has invested in a layered defense that includes both deterministic and probabilistic defenses to make it difficult and costly for attackers to cause harm. It created the user alignment critic, which vets an agent's actions using a separate model isolated from untrusted content. It also extended Chrome's origin-isolation capabilities to constrain what origins the agent can interact with.
A new self-supervised framework trains vision-language model judges without human annotations by generating and filtering multimodal data through reasoning traces.
OpenAI researchers use interpretability tools to address misalignment in language models by employing sparse-autoencoders and latent attribution methods. The study introduced an attribution method to identify SAE latents likely causing certain behaviors, which proved more effective than activation-based methods in identifying causally relevant latents.
President Donald Trump said that he will be issuing an executive order this week to curb state laws on artificial intelligence. All 50 states and territories introduced AI legislation this year. Legal experts say that the President doesn't have the legal authority to intervene in state legislation. Eliminating state laws would effectively remove guardrails for AI.
Nvidia will be allowed to ship its H200 AI chips to approved customers in China and elsewhere on the condition that the US gets a 25% cut. The same approach will be applied to AMD, Intel, and other companies in the US. The policy change is aimed at supporting American jobs, strengthening US manufacturing, and benefiting American taxpayers. The Department of Commerce is still finalizing the details of the policy.