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We can create the future of science right now
Paul Litvak is the founder and Executive Director of the Robyn Dawes Institute and a Visiting Scholar at UC Berkeley. He has a PhD in Behavioral Decision Research from Carnegie Mellon where he studied emotions and the sunk cost bias. Over 15 years in industry, he solved a wide range of challenging technical problems. At Meta he created models optimizing the ad review process. At Google he ran experiments to measure social influence. At Airbnb, he was a product manager leading machine learning teams optimizing search ranking and price suggestions. He also co-founded and led product at Rhythmic Health, a venture-backed biosensing startup, creating an accurate low cost system using a color changing strip and a smart phone to measure salivary lactate. The bottleneck At this point, it is uncontroversial to say that science needs to stop using the PDF article as the unit of knowledge and currency. The unbearable slowness of scientific publishing, the profit motive and margins: I’m not saying anything new. The PDF also sucks because it’s hard to extract structured information, which makes it hard to do evidence synthesis. As a result, we do much less evidence synthesis than is needed. And evidence synthesis ultimately undergirds most policy and medical decision-making. I can see second-by-second real-time odds for any sporting or newsworthy event, but a school board can’t see the best evidence on whether their 8th graders should be taught algebra. As a society, we don’t treat this as an important problem. Again, not controversial. Not only is the problem well understood, but the solution has already been laid out. What we need is AI-assisted living evidence synthesis - (1) an open knowledge graph of atomic claims, (2) claims linked to evidence, (3) assessment and synthesis of each piece of evidence, and (4) continuous updating with new data. What few realize (yet) is that the technical capacity to build this vision for a significant portion of science already exists. Not only that, scientists and startup teams are already building many of these components. I know this because I’ve been surveying the space and talking to many of the builders. There are some missing pieces: for example, evaluations of how well some of the components work. But at this point, most of what’s missing is a fully end-to-end working integration of all of these parts. In the rest of this essay, I’m going to lay out all the parts of a working living evidence layer for science and who is working on them, and propose concrete next steps for building this system. What’s now possible The diagram above outlines the components of a living evidence synthesis platform, including some of the teams working on each component1. Scientific PDFs are processed into claims with associated evidence. The evidence is subjected to a forensic audit, methodological evaluation and robustness and reproducibility checks. Finally it’s given a weight in a continuously updating synthesis. What follows is a description and status of each component and a few of the teams working on them. Document understanding The first thing you need to be able to do is turn an article into structured data. Mostly that means parsing PDFs. There are often multicolumn layouts that confuse non-specialized PDF to text processing libraries. For scientific papers, there is the added complexity of parsing formulas and tables and figures. This is a really hot area - there are startups offering APIs, and it seems like a new open source package gets posted to Github every few weeks. What follows isn’t exhaustive. A package called GROBID was the state of the art for a while, they didn’t update their package for nearly two years until very recently. In the meantime reducto.ai released an AI powered PDF extraction API, PaddleOCR became popular, IBM released a model called Docling, and both Mistral and Gemini created models and libraries. I also know of at least one other well-funded psychology research group working on a paper parser. By contrast, there are few open evals in this space, with no extensive evals for complex table comprehension in particular. Nonetheless, I’m confident this will be a solved problem soon, given the combination of LLM advances and developer interest. Hypothesis level extraction There has been increasing interest in comprehending the extracted text of papers and linking information to evidence for each hypothesis. A lot of work has already been done. Trialstreamer (Marshall et al. 2020) and RobotReviewer LIVE (Marshall et al. 2023) demonstrated automated extraction of trial population, intervention, and outcome at scale on clinical RCTs. PaperQA2 (Skarlinski et al. 2024) and Ai2 ScholarQA (2024) extended this to retrieval-augmented question answering with citation grounding. Elicit, Consensus, and SciSpace operationalized claim-level extraction for end users. OpenEval (Booeshaghi et al. 2026) is the most recent and most ambitious: 1.96 million atomic claims extracted from 16,087 eLife manuscripts using Claude Sonnet 4.5, grouped into ~299,000 results, with LLM evaluations showing 81% agreement with human peer review on a 2,487-paper subset. None of these solutions link claims to test-statistics, as you would need to evaluate randomized controlled trials. This is why I built the evidence.guide API - to extract hypotheses and associated test statistics from behavioral science papers. The best public eval of this kind of extraction I’m aware of comes from the recent SCORE project - they had humans code thousands of psychology papers to extract their claims by hand. It would be extremely helpful to the world if all scientific PDFs were available as structured open data. I’ve been working to make this happen, both directly at Berkeley and through coordination with large entities I can’t yet speak of; as hard as it is to do, I think it’s possible2. Forensic audit A lot of work has been done on forensic audit, but some gaps remain. Of course, for biology papers that rely on images for evidence, there are a variety of tools (notably Proofig and ImageTwin) to spot anomalies. These are still well short of what sleuths like Elizabeth Bik can do on her own, but these tools are constant companions among fraud analysts. There’s someone working on auditing Excel files for anomalies, and a number of teams are automating numerical checks like GRIM and SPRITE, including Scrutiny project, the INSPECT-SR team as well as statcheck. The regcheck team is building a way to use AI to compare preregistrations to analyses in papers, to ensure there aren’t significant deviations. Nonetheless, there are many other kinds of anomalies to screen for, both public and less publicly known. And there are no formal evals for anomaly detection that I’m aware of. Still, there’s a lot to draw from in this space and I’m pretty certain we will be able to scan papers for most kinds of obvious anomalies in the near future. Methodological review This area has been white hot, though I fear for many of the startups in this space, because this capability may become commoditized. There are at least six different AI peer review companies, including Refine.ink, Reviewer3, ReviewerZero.ai, Q.E.D. Science, Paper Wizard, and Isitcredible. Coarse (a pun on refine) was also recently created as an open source alternative. These systems provide qualitative feedback on the content of papers, spotting methodological weaknesses and mathematical errors. They seem to work pretty well, and many academics report bitterly that they exceed the average quality of typical peer reviewers. But there are few evals here either. What evals exist so far involve using LLM-as-judge (circularity problems abound) or comparing against human reviews of questionable quality. What you’d ideally want is an eval that measures capturing known errors in papers3. Reproducibility and robustness Another active area has been using AI agents to automate computational reproducibility4 and robustness5 checks in papers that report numerical results. For more recent papers where data and code are available, AI agents can see whether they can re-run the analyses and produce the numbers reported in the published paper. In addition to a handful of individual academics who have been experimenting using Claude Code for this, the Institute for Replication is a leading group working on building an end to end system. The evals related to this problem are the most mature, with CORE-Bench (Siegel, Kapoor, Narayanan 2024) and PaperBench (OpenAI 2025) available to benchmark agents on this task. There is also work on getting AI agents to test alternative ways of analyzing the data to ensure the results are robust to small analytic design choices. Synthesis This is the most underdeveloped area where significant investment is required. Although some automated evidence synthesis systems exist — for example, otto-sr is building an AI agent to write systematic reviews — none of these incorporate the full range of paper level signals to weight evidence appropriately. Nor is there anything like an eval or a gold standard for a good systematic review. Arguably Cochrane reviews are the closest we have to gold standard human systematic reviews, though I’ve heard academics in the know complain about their uneven quality. A key question for a synthesis platform is how to weight anomalies and methodological issues in assessing the quality of a piece of evidence. This is an unsolved problem and one I’m very keen to work on. Continuous updating There are many pieces of basic infrastructure available for monitoring for new research and initiating updates. OpenAlex is the current open citation graph. Retraction Watch integrated into Crossref in October 2023. Scite tracks how citations support, contrast, or mention prior claims. The Living Evidence Network demonstrated continuous-update workflows in clinical guidelines. Engineering this is a relatively straightforward task. When you look over this technical architecture and all the progress being made, it’s hard not to be optimistic that a living guide to scientific evidence will be built. The stakeholders are ready The social infrastructure for this is starting to coalesce — it’s not just a pie in the sky academic exercise to imagine this coming into existence. Institutions like the Center for Open Science, the Institute for Replication, the INSPECT-SR, the Living Evidence Network and more are all working on scaling work to improve research quality. Funders are also aligned. The Sloan Foundation has funded living evidence work through COS. Coefficient Giving supports the Institute for Replication and COS. The Astera Institute and the Institute for Progress have shown interest in this space. NIH has established an Office for Replication and Reproducibility. Although there are (very unfortunately) serious headwinds in science funding generally, there is an active group of funders interested in metascience. A brief word about what I’ve been doing at RDI. First, as a Visiting Scholar at Berkeley I’ve been actively figuring out how a non-profit and a public university can conduct and make public the results of large-scale academic article data mining. With some of the money I raised from donors, I commissioned a legal analysis of recent case law and publisher text data mining (TDM) agreements in order to understand whether a massive open data mining of academic articles is possible (with caveats, it is). I’ve also been working to bring together stakeholders in this space, and identify gaps. I’ve also been doing some software development in this space, with more to come. A pilot proposal The assumption undergirding all of this is that an AI, given all this information, would make the right judgment about a scientific claim with lots of conflicting evidence, weighing all the factors appropriately. That’s the hypothesis we need to test. Randomized control trial research is the best place to focus on first. RCTs are used to make many of the important decisions in society - from medical trials to public policy changes. And they use a relatively uniform set of inferential statistics with lots of known and available diagnostics. Behavioral science experiments, within the broader realm of RCTs, should be first used as a testbed whose results can be generalized. Because behavioral science is at the vanguard of open science practices, replications abound (there are thousands of them) to serve as ground truth training data. Key Hypothesis Therefore the pilot would test, in behavioral RCTs that have been replicated, whether the quality of evidence for a claim can be used to accurately predict whether that claim will replicate. Secondary Hypotheses Compared to claims that replicated, non-replicated claims demonstrate a greater share of forensic anomalies in their source literatures. Hypothesis level claim and statistic extraction is accurate enough to scale living evidence without onerous human review costs. Replication prediction is more accurate than prediction markets6 or journal prestige. If all the different quality signals we gather do accurately predict which studies will replicate, then we can use that model to score evidence to power the living evidence layer. Why this is informative regardless of outcome If the pilot succeeds, the architecture extends to medical RCTs (where Living Evidence already operates and integration is mostly about claim representation), then to slices of basic biology with stable replication structure. If it fails, the field learns which quality signals are load-bearing and which ones metascience has oversold. Either result is a contribution to knowing what the literature supports. Implications for funders Because this burgeoning ecosystem of builders already exists, a well-informed philanthropic or government funder could play a crucial catalyzing role in bringing this future about. They could play at least three roles: creating open structured datasets, publishing open benchmarks and incentivizing the solving of key technical challenges. First, open archives of papers that the government maintains, like PubMed, could be turned into structured data amenable to large scale metascience and claim aggregation. I know the US government already has an interest in doing this, though some key open questions remain unanswered. How do you determine the best models and systems for accurately extracting information from papers? How can you establish a robust way to allow researchers to flag errors and correct them? And finally, how do you create a legal regime in working with publishers to maximize the scope of available papers? For the latter, a university or a private philanthropy may be better positioned to make structured data publicly available under journal subscription terms or fair use. Philanthropists or government funders could also coordinate to create or commission benchmarks that evaluate whether important problems have been solved. For example, an open benchmark for claim extraction from a range of different scientific article types would be extremely helpful. Ensuring the underlying data are accurate is vital for creating these evaluations. I’ve discussed opportunistically using various human-created datasets for this purpose, but a consistent problem is that errors in human data make it difficult for them to serve as a gold standard. Finally, with structured data and benchmarks available, the government or private philanthropy could use them to incentivize groups to develop machine learning systems that meet these benchmarks. Prizes are one potentially valuable tool for this. For example, you could establish a prize for a system that accurately updates a living meta-analysis for a small set of claims. Prizes are particularly useful as signals of problem importance, and can help create vibrant ecosystems of public and private research—see, for example the role the government played in kickstarting the current work on nuclear fusion. Conclusion The drawbacks of the current scientific publishing system are known. Scientists agree, metascientists agree, philanthropists agree: the published PDF plus citation graph isn’t the right substrate for maintaining a representation of the evidence base in science. The pieces needed to build the alternative either already exist or are rapidly taking shape. The community is forming around exactly this problem, with concrete partnerships and shared infrastructure. A pilot should start on behavioral science RCTs because that’s the slice of empirical science most amenable to legibility, where replication ground truth is richest, and where the failure modes are best documented. What’s been missing is the galvanizing mission to assemble these pieces into something that works. That’s what I’m proposing to build. *** 1 I have a broader field map that I’ll release publicly soon. This is me, building in public! 2 If this is something that you are excited about, please reach out and talk to me. 3 More on this very soon too! 4 This tests whether, given the code and the data, you can get the same statistics as reported in the published paper. 5 This tests whether the results are the same as a paper’s given alternative analytical decisions in conducting the analysis (like outlier omission). Closely related is the idea of a “multiverse” where you come up with many different ways of answering the same underlying research question with the same data, and test whether the results hold in all those alternative methods. There’s been work on the latter as well. 6 Some of the replications, e.g. those from the SCORE project, had paired the experiments with forecasts from prediction markets. So we get to look at this for free.
WHO urges targeted action to strengthen emergency workforce capacities for shared health risks across the Asia-Pacific region
Kuala Lumpur, Malaysia, 11 June 2026 – At a time when the world is more interconnected than ever, the World Health Organization (WHO) is disseminating a comprehensive regional analysis of shared health risks across Asia and the Pacific, urging Member States to invest in enhancing workforce capacities regionwide to mitigate these risks and ensure health systems are better prepared to anticipate, tackle and withstand a range of crises and shocks. Over 100 delegates from 49 countries and areas in the Asia-Pacific region reviewed results from the regional risk analysis during the WHO-led Asia Pacific Health Security Action Framework (APSHAF) Stakeholders Meeting in Malaysia. Synthesizing insights from 21 strategic risk assessments and more than 800 multisectoral experts, the regional analysis highlighted an urgent imperative: countries must proactively invest in emergency workforce preparedness to navigate a landscape where climate hazards, disease outbreaks and geophysical events increasingly overlap. These complex converging crises create devastating, compounding impacts on health systems and communities - reiterating the need for investing in a fit-for-purpose regional health workforce and multisectoral action. Key findings: an evolving risk landscape More than half of the participating countries ranked flooding, cyclones, dengue and pandemic-potential respiratory pathogens as being of high or very high risk, while antimicrobial resistance, landslides and gastroenteritis remain widespread concerns as well. “Given that 51% - over half - of these assessed hazards are deeply interconnected, isolated health interventions are no longer sufficient,” said Dr Saia Ma’u Piukala, WHO Regional Director for the Western Pacific. “Extreme weather events like cyclones and floods directly trigger subsequent disease outbreaks, while environmental degradation - driven by sea-level rise, coastal erosion and saltwater intrusion - fuels health risks across the Pacific. Mitigating these cascading risks requires unified, cross-sectoral action linking environmental, agricultural and public health policies to build true regional resilience.” Priority recommendations for action To address critical vulnerabilities, WHO calls on Member States in the Asia-Pacific region to urgently prioritize the following strategic actions: Bridge workforce capacity gaps. Embed and standardize emergency preparedness within academia to promote regional workforce interoperability. Foster multisectoral coordination. Address ad-hoc taskforce responses and fragmented surge support through formalized contingency plans and standard operating procedures that are frequently updated and tested. Secure sustainable financing for emergencies. Overcome systemic barriers to access dedicated funding for both immediate anticipatory actions and long-term emergency preparedness initiatives. Enable emergency-ready primary health care. Embed emergency management into public health workforce readiness planning. Ensure that emergency planning is inclusive. Establish clear pathways for inclusive emergency planning that actively involves communities most at risk and other vulnerable groups. Optimize communication. Update risk communication and community engagement strategies, aligning them with seasonal risk calendars. Elevating workforce readiness to meet regional health risks While the gaps identified are significant, the Asia-Pacific region possesses solid foundational pillars, including dedicated clinicians, laboratory staff, rapid response teams, emergency medical teams and extensive community networks. However, to adequately address the compounding nature of modern crises, the various components of the emergency workforce must collaborate cohesively. True resilience depends entirely on transforming this currently fragmented workforce into a highly trained, interconnected and rapidly deployable asset. To achieve this, WHO is actively advancing regional workforce capacities through the Global Health Emergency Corps initiative. This initiative - supported by the Gates Foundation and Institute of Philanthropy - seeks to build a seamless network of emergency workforce across borders and sectors, ensuring that when a crisis strikes, a standardized, well-coordinated and trained workforce is immediately ready to respond. “Scaling up emergency workforce readiness is no longer optional,” said Dr Gina Samaan, Regional Emergency Director for WHO in the Western Pacific. “It is the definitive factor that will determine how effectively the Asia-Pacific region safeguards health and protects lives in an increasingly unpredictable future.” For more information, or to arrange media interviews, contact: Ara Johannes, WHO Western Pacific Health Emergencies Division: johannesa@who.int WHO Western Pacific Communications: wprocom@who.int
Do New Leadership Hires Clarify WELL Health Technologies' (TSX:WELL) Public-Sector Digital Health Strategy?
WELL Health Technologies has appointed Dr. Andrew Bond as Chief Health Officer and Head of Public Sector, and Derek Clark as Chief Operating Officer, adding experienced leadership to clinical governance, government engagement, and platform-wide operations. These hires deepen WELL’s bench in both health system policy and complex multi-entity execution, reinforcing its push to work more closely with public health systems while scaling its national digital and clinic network. Next, we’ll...
The small business making big investments to deliver advanced defense technologies
Employee-owned Physical Sciences Inc. has been scaling small business government R&D awards to solve some of the thorniest national security challenges.
Philly’s Naturaz is scaling science-backed care for textured hair
Founder Mumbi Dunjwa used her chemistry and healthtech background to build a vegan product line available in more than 60 retailers across the US.
Precision Medicine’s Next Hurdle Isn’t Science
A new UPMC report found that while most health systems now offer precision medicine programs, many still face challenges scaling them due to reimbursement, data integration and patient engagement issues. Health system leaders say the field’s future depends on embedding genomics into routine care, as well as proving its clinical and financial value.
Health care investment firms GHO and CBC to merge
Scaling for suvival.
Out of this Prairieville family's hardest moment, a $1M company was born
What began as a deeply personal challenge for Jordanna and Louis Coleman has grown into a fast-scaling logistics business—one built not only on strategy and resilience, but on faith. LC Transport Services, founded in February 2022 and headquartered in Prairieville, started with a single truck, a home office and a determination to create stability for […]
Virtual health is helping hospitals deliver care anywhere
Virtual health is reshaping care delivery - expanding access, cutting costs and supporting universal health coverage. Scaling it needs coordinated action.
Derek Candelore Introduces The Service Blueprint Program to Support Home Service Business Growth
PITTSBURGH, PA / ACCESS Newswire / May 16, 2026 /Derek Candelore has announced the launch of The Service Blueprint, a structured business development program designed to address common operational and scaling challenges faced by home service companies. ...
AMD promises to bring improved, hardware-backed FSR 4 upscaling to older Radeon GPUs
FSR 4.1 running on RDNA3 or RDNA2 GPUs may take a bigger performance hit.
AMD’s FSR 4.1 upscaling tech is coming to older graphics cards
A performance boost for your GPU.
AMD FSR Upscaling 4.1 officially coming to Radeon RX 7000 GPUs in July, RX 6000 in 2027
Breaking, AMD confirms FSR 4.1 for Radeon RX 7000 series After months and often years of waiting, AMD is finally confirming support for FSR4 on RDNA3 and
Read the internal memo from LinkedIn's CEO on layoffs
LinkedIn is laying off employees and "scaling back" other investments, an internal memo shows.
A different kind of leader: Scaling impact in geospatial science
David Page, head of the Geographic Data Science Section at Oak Ridge National Laboratory, leads with a focus on judgment, collaboration and empowering teams. Drawing on experience in academia, entrepreneurship and national security science, he has helped advance faster, scalable geospatial analysis while building resilient, forward-looking research teams.
2026 CEO Study: 5 plays for AI-first transformation
AI-first CEOs are executing on strategy more successfully and scaling more AI initiatives enterprise wide. Download the 2026 IBM IBV CEO Study to find out how.
A different kind of leader: Scaling impact in geospatial science
David Page, head of the Geographic Data Science Section at Oak Ridge National Laboratory, leads with a focus on judgment, collaboration and empowering teams.
Forza Horizon 6 Will Target 4K 30 FPS on Xbox Series X—60 FPS Calls for Upscaling
With the official launch of Forza Horizon 6 around the corner, Microsoft and Playground Games have just divulged a little more information about the upcoming driving sim, specifically its world, car line-up, and official console performance modes and expected frame rates. Firstly, and perhaps most i...
Asha Sharma Comments On AI Use As Upscaling Tech Rolls Out To ROG Xbox Ally
"We are refocusing our AI efforts..."
Collaborative Coding, Better Scaling, Health Tracking: HAI Awards $2.17M to Innovative Research
Seed grants will fund 29 research teams pursuing novel research ideas across disciplines.
How Some Founders Are Redefining Fashion’s Idea of Success
As the pipeline to scaling a fashion business grows more precarious, some designers are ditching the pursuit of global prestige in favour of a slower approach to growth they say is more sustainable — and more fulfilling.
Scaling the digital future: Why AI and skills investments matter for business and society
It is important to bridge the gap between cutting-edge technology and the people who will shape our future. Learn more about how AI combined with skills investments matters for business and society.
MAXDropshipping Launches Enhanced Fulfillment Solution for Scaling E-commerce Brands
NEW YORK, April 25, 2026 (GLOBE NEWSWIRE) -- MAXDropshipping, a global dropshipping fulfillment partner, has announced the enhancement of its fu...
SPONSORED How JPMorganChase is scaling support for small businesses
New efforts from JPMorganChase aim to support 271,000 small businesses in San Francisco.
Canada’s biotech paradox: strong science, weak scaling
Canada produces a steady stream of biotech startups built on credible academic research. Universities and hospital research centers in Toronto, Montreal, and Vancouver generate discoveries that move quickly into company formation. Early-stage activity is not the constraint. The difficulty tends to surface later, when those companies try to grow.
CBL Scholars Program Opens Pathways to Business Law Careers - UC Law San Francisco (Formerly UC Hastings)
First-year law students and 2026 CBL Scholars Deniz Masjedi, Alvaro Flores, and Tavia Pappaly gain mentorship connections with leading industry professionals as they prepare for careers in business law. Created by the UC Center for Business Law San Francisco (CBL), the CBL Scholars Program aims to develop the next generation of business law leaders, supported by contributions from individuals and corporate sponsors. The highly competitive program provides funding along with academic and professional mentorship to students from disadvantaged backgrounds. Scholars gain exposure to the Bay Area’s technology and venture capital ecosystems and access to UC Law SF’s broad network of senior legal professionals. Now in its sixth year, the CBL Scholars Program provides a scholarship and academic and professional mentorship to students entering the business law community from disadvantaged educational, economic, social, or physical backgrounds. The program is generously sponsored by the law firms Gunderson Dettmer and Goodwin Procter. Meet this year’s CBL Scholars: Deniz Masjedi, Alvaro Flores, and Tavia Pappaly. All three have secured summer associate positions at prestigious law firms. Deniz Masjedi: A Passion for Helping Founders Grow Companies Deniz Masjedi, who was born in Iran and moved to Southern California as a teenager, traces her interest in law to her family’s immigration journey and her desire to advocate for herself and her family. She earned a bachelor’s degree at UC Berkeley, where she studied political science and sociology. At UC Law SF, she serves as a 1L representative for the Iranian Law Students Association and is active in the Business Law Society and Technology & Law Society. Next fall, she will serve as director of mentorship for the Technology & Law Society and director of professional development for the Business Law Society. She will join Sidley Austin this year as a 1L/2L summer associate. Q: What draws you to business law? A: Prior to law school, I worked in Cooley LLP’s Private Companies practice group as a corporate paralegal for three years, supporting over 100 venture-backed startups and venture capital funds. I found purpose in helping founders transform their vision into successful companies. It was incredibly rewarding being part of their journeys—from company formation and initial financing to major transactions such as M&A deals. What excites me most about this field is the potential for a small, innovative company to become the next Google or Zoom, shaping how people communicate and access information. I hope to continue supporting entrepreneurs and investors as a corporate attorney. Q: What does it mean to you to have been awarded this scholarship? A: As the first person in my family to attend law school in the United States, receiving this scholarship is a tremendous honor. My parents and I left everything behind so that I could enjoy the freedom to practice my religion without persecution and pursue a career in law as a woman. This award is an acknowledgment of my dedication to business law and motivates me to continue pursuing excellence in this field. It also recognizes years of hard work—learning a new language, navigating the college and law school application process, and ultimately building a career in the legal profession. Q: How will this scholarship help support your future goals? A: This scholarship will provide access to mentorship from leaders in the field, enabling me to develop the skills necessary to offer practical advice and effective representation to future clients. It also eases the financial burden of law school, letting me focus on my coursework and deepen my commitment to corporate law. As an immigrant, breaking into the legal profession has not always been easy, and the time that mentors have invested in me has been instrumental to my progress. This scholarship will allow me to continue building those relationships while positioning me to one day mentor others pursuing similar paths. Alvaro Flores: Inspired by Entrepreneurs The son of Mexican immigrants raised in San Antonio, Texas, Alvaro Flores says he has long been interested in how policy, markets, and culture shape society. He studied international relations and global economy at the University of Southern California, with minors in business finance and legal studies. His experiences span local political organizing, venture capital and startup work, and teaching entrepreneurship. At UC Law SF, he is involved in the Business Law Society, Technology & Law Society, and the Latinx Law Student Association. He will join Perkins Coie as a 1L/2L summer associate with the firm’s Emerging Companies & Venture Capital practice. Q: What draws you to business law? A: I’m drawn to stories of entrepreneurs whose simple ideas become companies that shape the economy and society. Spending time in venture capital and working directly with founders has shown me that many important decisions in the scaling process are ultimately legal ones, particularly how companies are structured and how capital is raised. Business law sits at the intersection of innovation and equity. It offers a way to stay close to the pace of technological change while also helping expand who gets to participate in it. There is something special about being part of that process here in the Bay Area, where the American Dream feels like it is reimagined in real time. Q: What does it mean to you to have been awarded this scholarship? A: I am truly grateful for the support and belief this scholarship represents. I see it not just as recognition, but also as alignment. The CBL Scholars Program’s focus on developing individuals who engage deeply with the business and technology ecosystem reflects the path I have been intentionally building. More than anything, it feels like an investment in that journey. I have been thoughtful about each next step–whether teaching, working in venture capital, or building multidisciplinary experiences–and this scholarship reinforces that those efforts come together in a meaningful way. Q: How will this scholarship help support your future goals? A: It allows me to focus more on developing as a business lawyer, without the distraction of financial constraints. Beyond that, the mentorship and access to the Bay Area network are just as valuable. I’m especially interested in working with early-stage companies and investors, so the opportunity to learn directly from people operating at a high level in that space is huge. Tavia Pappaly: Working in the Wine Industry Sparked Interest in Business and Law Tavia Pappaly spent five years in the wine industry before deciding to pursue law school with a goal of becoming a corporate transactions attorney. Pappaly says she wants to use her legal skills to help business grow and thrive. A first-generation law student from Fairfield, California, she is a member of the Legal Education Opportunity Program (LEOP) and will serve as vice president of social planning for the UC Law SF Sports Law Society in her 2L year. She plans to work as a summer associate at Gibson Dunn in San Francisco after her 2L year. Q: What draws you to business law? A: Business law brings together many of my interests and personal experiences. As the daughter of immigrants who grew up around a family-owned small business, I saw firsthand both the opportunities and challenges of building something from the ground up. That perspective, combined with my experience in the wine industry, solidified my interest in helping businesses of all sizes tackle evolving legal challenges. I’m excited to grow within the corporate law space and develop the skills and experience to support businesses at every stage. I’m deeply motivated to give back to immigrant communities and small business owners, whose experiences often intersect. By building a strong foundation in corporate law, I hope to support those working to create generational stability and to contribute in a meaningful way to the communities that have shaped me. Q: What does it mean to you to have been awarded this scholarship? A: Receiving this scholarship is incredibly meaningful to me. I take great pride in my background as a first-generation student and a woman of color, so being recognized for my potential in a field that has historically underrepresented those communities is both affirming and empowering. This program will help connect me to the resources and committed mentors that will help me succeed. Beyond that, it is no secret that law school is a significant financial commitment. This scholarship helps ease both the financial burden and the stress that comes with it, allowing me to focus more on my education and long-term goals. I am incredibly grateful! Q: How will this scholarship help support your future goals? A: This scholarship has already connected me with an incredible network of faculty, business law professionals, and like-minded students. The community fostered by the Center for Business Law is deeply supportive, and I’m excited to grow alongside my cohort. I truly believe this network will play an important role in helping me thrive in law school and position me for success in business law. As I have seen firsthand how impactful both financial scholarship and mentorship are, I hope to give back in this same way later in my career.
'Unsexy' side of robotaxis: The AV industry wants to grow up
Ride AI, an autonomous vehicle hype conference, showed an industry that wants to move away from the hype and focus on scaling.
SVG New Sponsor Spotlight: 4Wall Entertainment’s Dave Caulwell on Scaling Live Event Production for Sports
As live sports production continues to expand across linear, digital, and in-venue platforms, the demand for high-end production infrastructure — from lighting and video to audio and rigging — has...
How Dangerous Is Anthropic’s New AI Model? Its Chief Science Officer Explains.
Just how fast is the future coming at us, and how worried should we be? Last week, Anthropic announced Claude Mythos, the latest in what has become the leading family of AI models. In a very short time, AI models from Anthropic and other companies have become shockingly powerful at coding. But Mythos stands out because it is not just able to write code that can hack into critical systems, like those that run financial companies, but also to analyze systems and find and string together vulnerabilities in them, allowing Mythos to evade even sophisticated defenses. Anthropic chose to release Mythos to only about 40 leading corporations, so they could analyze the model’s capabilities and prepare countermeasures, in an effort it calls Project Glasswing. The release of Mythos—and the prospect of similarly powerful AIs to follow—has caused tremors at the top levels of Washington and corporate America. Jared Kaplan, co-founder and chief science officer at Anthropic, is at the center of this breakthrough. We sat down with Kaplan so he could walk us through just how powerful Mythos is, what the implications are for issues like our personal privacy, and what comes next. This conversation has been edited for length and clarity. Sean Fischer: For the layman, how is Claude Mythos different—more powerful, more dangerous—from previous models released by Anthropic? Jared Kaplan: I think most people have experienced the fact that our computers get faster and faster over time and over many decades. There’s this very smooth trend where every 18 months, maybe, computer processing power doubles. I think AI is on a trend in which it’s improving maybe 10 times faster than that. A lot of the earliest work that I did in AI was around identifying this scaling trend in AI. Claude Mythos is the latest model from Anthropic, but it’s not different because it’s qualitatively different. It’s really just kind of the culmination of the trend of AI models getting smarter and smarter at all kinds of general capabilities, from reasoning to software engineering to scientific research to knowledge work. For us, it was the first model to demonstrate very elite-level cybersecurity capabilities. It wasn’t because we trained Claude Mythos to focus on cybersecurity, but it was because, as a byproduct of its general intelligence and its general ability with software, it’s particularly good at identifying vulnerabilities in software and how to exploit them.
How Latino business owners are navigating growth, AI and inflation
Latino-owned businesses in the U.S. continue to overcome funding challenges to pursue expansion and innovation—through strategies such as scaling internationally, acquisitions, and investing in artificial ...
“It Would Be A Nightmare”: A PlayStation 6 “Lite” is Not Happening Due To Development and Hardware Reasons, Leaker Says
Leaker KeplerL2 debunks PlayStation 6 "Lite" rumors, citing technical "nightmares" for devs and 4K upscaling hurdles.
YouTube’s copyright system strikes again.
Nvidia’s own DLSS 5 trailer was taken down from YouTube after an Italian TV channel — which reportedly used the footage in a segment about the upscaling tech — issued copyright takedown requests for videos using the same clips. The takedown requests impacted some creators using the footage as well. [Image: https://platform.theverge.com/wp-content/uploads/sites/2/2026/04/nvidia-copyright-takedown.png?quality=90&strip=all]
Why Smart Founders Build Big Business Infrastructure Before They Scale
Scaling a company takes more than grit and growth. Founders who invest early in institutional-grade benefits can reduce risk, attract top talent, and build a culture that performs under pressure.
Gemma 4 and what makes an open model succeed
Having written a lot of model release blog posts, there’s something much harder about reviewing open models when they drop relative to closed models, especially in 2026. In recent years, there were so few open models, so when Llama 3 was released most people were still doing research on Llama 2 and super happy to get an update. When Qwen 3 was released, the Llama 4 fiasco had just gone down, and a whole research community was emerging to study RL on Qwen 2.5 — it was a no brainer to upgrade. Today, when an open model releases, it’s competing with Qwen 3.5, Kimi K2.5, GLM 5, MiniMax M2.5, GPT-OSS, Arcee Large, Nemotron 3, Olmo 3, and others. The space is populated, but still feels full of hidden opportunity. The potential of open models feels like a dark matter, a potential we know is huge, but few clear recipes and examples for how to unlock it are out there. Agentic AI, OpenClaw, and everything brewing in that space is going to spur mass experimentation in open models to complement the likes of Claude and Codex, not replace them. Especially with open models, the benchmarks at release are an extremely incomplete story. In some ways this is exciting, as new open models have a much higher variance and ability to surprise, but it also points at some structural reasons that make building businesses and great AI experiences around open models harder than the closed alternatives. When a new Claude Opus or GPT drops, spending a few hours with them in my agentic workflows is genuinely a good vibe test. For open models, putting them through this test is a category error. Something else to be said about open models in the era of agents is that they get out of the debate of integration, harnesses, and tools and let us see close to the ground on what exactly is the ability of just a model. Of course, we can’t test some things like search abilities without some tool, but being able to measure exactly the pace of progress of the model alone is a welcome simplification to a systematically opaque AI space. Share The list of factors I’d use to assess a new open-weight model I’m considering investing in includes: Model performance (and size) — how this model performs on benchmarks I care about and how it compares to other models of a similar size. Country of origin — some businesses care deeply about provenance, and if a model was built in China or not. Model license — if a model needs legal approval for use, uptake will be slower at mid-sized and large companies. Tooling at release — many models release with half-broken, or at least substantially slower, implementations in popular software like vLLM, Transformers, SGLANG, etc due to pushing the envelope of architectures or tools. Model fine-tunability — how easy or hard it is to modify the given model to your use-case when you actually try and use it. The core problem is that some of these are immediately available at release, e.g. general performance, license, origin, etc. but others such as tooling take day(s) to week(s) to stabilize, and others are open research questions — with no group systematically monitoring fine-tunability. In the early era of open models, the days of Llama 2 or 3 and Qwen pre v3.5, the architectures were fairly simple and the models tended to work out of the box. Some of this was due to the extremely hard work of the Llama, Qwen, Mistral, etc. developer teams. Some is due to the new models being genuinely harder to work with. When it comes to something like Qwen 3.5 or Nemotron 3, with hybrid models (either gated delta net or mamba layers), the tooling is very rough at release. Things you would expect to “just work” often don’t. I’ve been following this area closely since we released Olmo Hybrid with a similar architecture, and Qwen 3.5 is just starting to work well in the various open-source tools that need to all play nice together for RL research. That’s 1.5 months after the release date! This is just to start really investing more into understanding the behavior of the models. Of course, others started working on these models sooner by investing more engineering resources or relying on partially closed software. The fully open and distributed ecosystem takes a long time to get going on some new models. All of this is lead-in for the most important question for open models — how easy is it to adapt to specific use-cases? This is a different problem for different model sizes. Large MoE open-weight models may be used by entities like Cursor who need complex capabilities in their domain, e.g. Composer 2 trained on Kimi K2.5. Other applications can be built on much smaller models, such as Chroma’s Context-1 model for agentic search, built on GPT-OSS 20B. The question of “which models are fine-tunable” is largely background knowledge known by engineers across the industry. There should be a thriving research area here to support the open ecosystem model. The first step is to understand characteristics of different base and post-trained models to understand what they look like. The second step is to tune pretraining recipes for open models so they’re more flexible. Interconnects AI is a reader-supported publication. Consider becoming a subscriber. Subscribe For The ATOM Project and other Interconnects endeavors, we’ve put in substantial effort to measuring adoption trends in the open ecosystem. Everything takes a long time to unfold after a model is first publicly available — and adaptability is why. What we know for sure now, when Qwen has been going from strength to strength with its releases, is that technical staff across the industry has gotten comfortable working with Qwen models. Countless research methods and datasets were made to work with Qwen. It’ll take patience for any other model family to get to this point — a patience I’m not sure many open model builders have. This takes us to Gemma 4, Google’s latest open models. Gemma 3 was released more than a year ago, in March of 2025, and is a bit underrated. Gemma 4 comes in 4 sizes for now, with a bigger, MoE model of over 100B total parameters rumored but not released yet. The models we have today come in sizes of ~5B dense, 8B dense, 26B total 4B active MoE, and 31B dense. I’m most excited that they’re finally adopting a standard Apache 2.0 open source license. This’ll massively boost adoption. The standard of better licenses for strong open-weight LLMs was set by mostly Chinese open model labs in the last 1-2 years, and now U.S. companies are following suit. I will personally be so happy if the horrible Llama licenses and Gemma terms of service were an ~18-month transient dynamic of the industry being nervous about releasing strong open models. The Gemma 4 scores look very solid, the small models have incredible benchmark scores (especially in general domains like LMArena) and the 31B model rivals the recent Qwen 3.5 27B, which is the leading member of that class. The ~30B size range is an important one, as it’s accessible both to researchers and to enterprises looking to deploy the model in real use-cases. Where the 7B model scale is the default for tinkering and research, a 30B model is the default for seeing if an open model can unlock substantial value in your specific workflow — a good mix of intelligence, low price, tractability for downstream training, etc. This takes us back to the above adoption criteria I mentioned for open models and the bigger question — do I think Gemma 4 will be an overwhelming success? Previous Gemma models have been plagued by tooling issues and poorer performance when being finetuned. Gemma 4’s success is going to be entirely determined by ease of use, to a point where a 5-10% swing on benchmarks wouldn’t matter at all. It’s strong enough, small enough, with the right license, and from the U.S., so many companies are going to slot it in. I’m cautiously optimistic that Gemma 4 is going to work better here. Winds are shifting for open models built in America. We saw GPT-OSS go through a bumpy launch to become an overwhelming success. There’s a collective energy around the likes of Reflection, Arcee, Nemotron, Gemma, Olmo, and peers that show substantial demand for building new stacks around open models. There’s capital to be spent on AI stacks across the economy by those who want more ownership of everything, including the model. After launching The ATOM Project 240 days ago, the conversation is shifting into the next stage. Summer of 2025 was a crisis moment where the U.S. AI scene realized it can’t wait and figure out open models after building AGI. The two markets will capture different areas and proceed in parallel. Now that more companies in the U.S. are releasing strong models, we need to improve the ecosystem so that these models are easy to use, understand, and build value around. It’s the hard work to build another inflection point in these adoption plots I’ve been updating consistently, but that’s the work to be done. Join me in it. More data coming soon! Here’s a sneak peek:
Tyler Sloat Joins Hinge Health Board As Growth And Discipline Focus
Hinge Health (NYSE:HNGE) appointed Tyler Sloat to its board of directors, adding a leader with experience in high growth technology companies. Sloat has held senior roles at companies that have gone through rapid expansion and IPOs. The appointment signals a focus on scaling Hinge Health's business and strengthening governance as the company matures. Hinge Health focuses on digital musculoskeletal care, a segment that sits at the intersection of healthcare and technology. As employers and...
Claude's popularity is forcing it to hit the brakes on users
"I know this was frustrating," an Anthropic official wrote in an announcement of the changes. "We're continuing to invest in scaling efficiently.
World TB Day: Scaling workplace healthcare interventions
Four lessons from the End Workplace TB initiative could help other private sector-led campaign coalitions to scale their workplace healthcare programmes.
The DLSS 5 Backlash Is Still Swirling
Almost a week on, the controversy and backlash over NVIDIA’s DLSS 5 announcement are still swirling, following some mixed messaging from the company about how the tech works. A reveal trailer on Tuesday showcased multiple games like “Resident Evil Requiem,” “Starfield”, and “Hogwarts Legacy” using the new technology, which goes beyond simple upscaling and leads […]
Why Small Process Changes Often Deliver the Biggest Business Results
Founders often overlook small operational frictions that drain productivity. Here is how small process improvements can unlock boost scaling.
Microsoft says it'll make Windows 11 a calmer OS with fewer upsells or ads, as it tries to win back users
Microsoft has now confirmed it’s scaling back “upsells” (or ads/recommendations) in Windows 11, as it tries to win back users.
Gamers Hate Nvidia's DLSS 5. Developers Aren’t Crazy About It Either
Nvidia’s new AI upscaling gaming technology struck gamers as uncanny and off-putting. Developers don't seem to like it either, but it could be “the default” in a few years.
Prairieville’s Sarah Taylor is scaling a health network for industrial growth
As Ascension Parish’s industrial boom continues to draw contractors and skilled workers to the region, the demand for occupational health services is rising alongside it. NuREADY Health Network is expanding its footprint across the Gulf South to help companies manage workforce testing, injury care and compliance across multiple job sites. Major industrial construction and maintenance […]
Nvidia CEO says critics are "completely wrong" about DLSS 5 technology
Following concerns that upscaling feature detracts from creative expression, Jensen Huang says it "doesn't change artistic control"
Gamers react with overwhelming disgust to DLSS 5's generative AI glow-ups
Nvidia's next frame-gen tech goes way beyond upscaling, and not in a good way...
Judge blocks RFK Jr. from scaling back childhood vaccine recommendations
A federal judge on Monday temporarily blocked federal health officials from cutting the number of vaccines recommended for every child, and said U.S. Health Secretary Robert F. Kennedy Jr. likely violated federal procedures in revamping a key vaccine advisory committee.
Sony's enhanced PSSR upscaling arrives on PS5 Pro today
Sony's upgraded PlayStation Spectral Super Resolution (PSSR) tech is rolling out as part of the PlayStation 5 Pro system update that's available today.
NVIDIA claims DLSS 5 will deliver 'photoreal' image quality with AI this fall
Just months after announcing DLSS 4.5 at CES, NVIDIA has unveiled its next major upscaling technology, DLSS 5.
Sony’s AI graphics upscaling for PS5 Pro games is getting a big update tonight
Less flickering and shimmering this time around.
Linux Finally Catches Up to Windows with a Game-Changing Performance Feature
For years, the Linux kernel’s scheduler has been world‑class at balancing loads, yet it missed a crucial, cache‑aware instinct. In modern multi‑core systems, that gap can turn into measurable latency, especially when threads bounce between cores that don’t share the same cache. A new upstream feature, often called Cache Aware Scheduling, is now set to change that equation. A cache‑savvy scheduler arrives At the heart of any OS, the scheduler decides which thread runs where and for how long. In modern CPUs, small private caches (L1 and L2) sit beside each core, while a larger Last Level Cache (LLC, typically L3) is shared among groups of cores. When a task migrates across LLC boundaries, its warm data may vanish from the cache, forcing slower trips to main memory. Cache Aware Scheduling keeps related tasks close to their shared LLC, reducing destructive migrations. By respecting cache topology, the kernel minimizes cold‑start penalties and preserves locality that many workloads desperately need. The result is less thrashing, fewer memory stalls, and more consistent throughput. "Keep tasks close to their data, and the system will keep performance close to its peak." Why this narrows the Windows gap Windows has long leaned on topology‑aware, cache‑sensitive heuristics, especially since the Windows 10 era. That advantage helped Microsoft handle hybrid designs with P‑cores and E‑cores, plus complex cluster and NUMA layouts. With cache awareness integrated upstream, Linux brings parity to this vital dimension, without sacrificing its trademark flexibility. The Linux approach remains deeply configurable, reflecting the ecosystem’s breadth across servers, desktops, and embedded devices. It layers atop existing NUMA‑balancing and energy‑aware logic, refining placement rather than reinventing the wheel. Crucially, it aligns scheduling with the hardware’s real shape, not just with abstract CPU counts. Real‑world gains and who benefits Early tests on Intel Sapphire Rapids platforms point to gains in the 30–45% range for select workloads. Those wins appear in cache‑sensitive tasks like in‑memory analytics, high‑thread compilation, and microservices with tight working‑sets. Games and latency‑bound engines can also feel steadier frame‑times, especially when threads share hot assets. The benefits extend to AMD’s 3D V‑Cache parts, where keeping threads near enlarged cache slices prevents needless misses. Hybrid x86 designs with performance and efficiency cores further profit when cache and core roles are scheduled in concert. Even handhelds running SteamOS can squeeze more out of limited power, translating cache locality into smoother play. Key advantages include: Lower end‑to‑end latency Fewer expensive misses to RAM Better multi‑socket and NUMA behavior Improved energy efficiency More predictable QoS under mixed loads Stronger scaling on dense servers Caveats, tuning, and rollout No scheduler change is purely free, and trade‑offs still exist. Favoring locality can reduce cross‑cluster balance if limits are set too tight. Likewise, fairness and utilization must remain healthy, especially under heterogeneous, bursty workloads. Linux’s implementation is designed to be measured and tunable, not a blunt instrument. It cooperates with energy‑aware scheduling for mobile efficiency, and with NUMA‑balancers on big iron. Expect architecture‑specific refinements as vendors surface richer topology hints and cache‑sharing maps. Upstream integration has begun, with broad distribution adoption likely over the next cycles. Many users will encounter the change through standard kernel updates in 2025–2026, depending on distro cadence. Server‑class kernels may enable or tune it sooner for targeted fleets where cache locality is money in the bank. How to think about performance impact Cache Aware Scheduling amplifies gains when your workload is both CPU‑bound and cache‑sensitive. Think tight inner loops, hot code paths, and datasets that fit snugly in shared LLC. It’s less dramatic for I/O‑bound services or memory‑hungry tasks that blow past cache capacity. Still, even modest locality improvements often translate into smoother tail‑latency, which is where user experience and SLAs typically break. Developers can further help by pinning related threads, batching work, and aligning data to reduce cross‑cluster chatter. The scheduler’s new awareness works best when applications expose good hints. The bottom line By understanding and honoring cache topology, Linux removes a subtle yet costly bottleneck. The kernel’s smarter placement keeps hot data hot and work near where it belongs. That narrows a long‑standing gap with Windows, while preserving the openness and tunability Linux champions. For gamers, creators, and operators, the payoff is practical: more consistent frames, faster builds, and snappier services. For the ecosystem, it’s another step toward hardware‑savvy scheduling that turns transistor complexity into real‑world speed. And for Linux itself, it’s a timely upgrade that matches today’s silicon with tomorrow’s expectations.
A free Xbox Ally X update could deliver up to 30% smoother performance for your handheld games — "Automatic Super Resolution" preview starts soon
Xbox is bringing Automatic Super Resolution to the Xbox Ally X in April. The AI upscaling feature aims to improve performance while keeping games sharp.
PopSockets founder David Barnett talks about building a viral business
David Barnett, founder of PopSocket, stops by Equity to talk about scaling the business and what he's learned as a leader.
Smaller raise? Higher health insurance costs may be to blame
Many employers responded to rising health insurance costs by scaling back pay increases, the New York Fed found.
Smaller raise? Higher health insurance costs may be to blame
Many employers responded to rising health insurance costs by scaling back pay increases, the New York Fed found.
Smaller raise? Higher health insurance costs may be to blame
Many employers responded to rising health insurance costs by scaling back pay increases, the New York Fed found.
How one small business is making tourniquets easier to use
AutoTQ, an automatic tourniquet, helps bystanders stop bleeding fast. Golden Hour Medical is scaling trauma care through workplace safety adoption.
Teladoc Health Q4 Earnings Call Highlights
Teladoc Health (NYSE:TDOC) reported fourth-quarter and full-year 2025 results that management said were generally in line with expectations, while outlining a 2026 plan centered on product innovation in integrated care, scaling BetterHelp’s insurance offering, and continued operational efficiency ef
The PS5 Pro is getting upgraded upscaling tech in March
The PS5 Pro will be updated with a new version of its PSSR upscaling tech based on AMD FSR 4.
The Strong Leader's Guide to Scaling Without Breaking Your Company
A practical framework for founders and business leaders to scale smarter by combining freemium access, marketplace participation and partner ecosystems into a cohesive growth model.
DLSS upscaling is coming to Fallout 4 on Nintendo Switch 2 in the future
Bethesda has confirmed that the Fallout 4: Anniversary Edition which launched yesterday on the Nintendo Switch 2 will eventually get DLSS. DLSS is a form of upscaling which should come in handy for…
Anthropic is dropping its signature safety pledge amid a heated AI race
Anthropic said it will no longer "pause the scaling and/or delay the deployment of new models" when the updates would outpace its own safety measures.
Quantum computer breakthrough tracks qubit fluctuations in real time
Qubits, the heart of quantum computers, can change performance in fractions of a second — but until now, scientists couldn’t see it happening. Researchers at NBI have built a real-time monitoring system that tracks these rapid fluctuations about 100 times faster than previous methods. Using fast FPGA-based control hardware, they can instantly identify when a qubit shifts from “good” to “bad.” The discovery opens a new path toward stabilizing and scaling future quantum processors.
Meta shifts Horizon Worlds focus from VR to mobile platforms
Meta's Horizon Worlds pivots to mobile focus, scaling back VR efforts. This strategic shift positions it against platforms like Roblox and Fortnite.
Google DeepMind Partnerships in India: scaling AI in science and education
Google DeepMind announces new AI partnerships in India to advance scientific research, empower students with interactive Gemini-powered learning, and support national goals in agriculture and renewable energy.
Earth Science Tech, Inc. (ETST) Reports Third Fiscal Quarter 2026 Results
Revenue of $8.4 million, up 14.1% year-over-yearGross profit margin of 76.3%, up 71 bps year-over-yearNet income of $910,000, up 341% year-over-yearGenerated positive cash from operations of $1.2 million fiscal year-to-datePeaks, the company’s telemedicine platform, surpasses $2.0 million in revenue in less than a year, scaling from $248,000 into a high‑margin growth platformReduced outstanding shares of common stock by 3.6% year over year through share repurchasesGeographic expansion underway w
Scaling social science research
GABRIEL is a new open-source toolkit from OpenAI that uses GPT to turn qualitative text and images into quantitative data, helping social scientists analyze research at scale.
Apple Tweaks Ecosystem Strategy Across Payments Car AI And Health
Apple (NasdaqGS:AAPL) is shifting the Apple Card issuing partnership from Goldman Sachs to JPMorgan Chase, affecting more than 12 million existing cardholders. The company is opening CarPlay to third party AI chatbots, allowing outside developers to integrate conversational tools into the in car experience. Apple is scaling back its standalone AI powered health coach project, instead choosing to roll selected features into current health and fitness apps. For you as an investor, these moves...
Apple is scaling back plans for new AI-based health coach service
The initiative, code-named Mulberry, was wound down in recent weeks, according to the people, who asked not to be identified because the move wasn’t public.
Apple Is Scaling Back Plans for New AI-Based Health Coach Service
Apple Inc. is scaling back plans for a virtual health coach, according to people with knowledge of the matter, part of an effort to rethink how the company approaches the burgeoning market for wellness services.
Intesa Sanpaolo presents its 2026–2029 Business Plan: scaling a proven model with sustainable profitability and strong capital returns
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Scaling innovations in public health systems: guidance and toolkit
This guidance is a strategic instrument to position governments as drivers of health systems transformation. By embracing the roles, processes and strategies outlined herein, public sector leaders can move beyond short-term initiatives and isolated successes toward lasting, large-scale improvements in health outcomes.
Scientists say quantum tech has reached its transistor moment
Quantum technology has reached a turning point, echoing the early days of modern computing. Researchers say functional quantum systems now exist, but scaling them into truly powerful machines will require major advances in engineering and manufacturing. By comparing different quantum platforms, the study reveals both impressive progress and steep challenges ahead. History suggests the payoff could be enormous—but not immediate.
Introducing BU's New Online MS in Artificial Intelligence in Business
BU Questrom School of Business has launched a new Online MS in Artificial Intelligence in Business, designed for working professionals responsible for improving performance, scaling change, and making artificial intelligence work in real organizations.
Jerry J. Bobb Unveils Bold Strategy for Business Pivot: Scaling Back to Move Forward
Facing staffing shortages and supply disruptions, Jerry J. Bobb narrowed operations rather than expanding them—a move he says helped stabilize a...
NVIDIA rolls out DLSS 4.5 to all RTX GPUs
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Scaling virtual health: developing a roadmap for success
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How business AI can return humanity’s most precious resource
Business AI will ultimately free up time. Organizations now deploy AI for end-to-end work and in operations allowing scaling without expanding headcounts.
NVIDIA announces DLSS 4.5 at CES 2026
Just like last year, NVIDIA has used some of its time on the CES stage to introduce an upgraded version of its real-time image-upscaling technology.
Business Owners Can Now Calculate Estimated Tax Savings in Seconds Using Gelt’s AI-Powered Calculator
Gelt, an AI-native tax firm serving high-net-worth individuals and scaling businesses, has launched the Gelt Calculators for Smarter Tax Savings hub.
China's DeepSeek kicked off 2026 with a new AI training method that analysts say is a 'breakthrough' for scaling
DeepSeek has released a new AI training method that analysts say is a "breakthrough" for scaling large language models.
LG announces new UltraGear evo gaming monitors with AI upscaling
Even your gaming monitor isn’t safe from the buzz of AI.
Tesla is recruiting factory workers and sales staff to operate its 'Robotaxi' service
Posters that appeared at Tesla's California factories and a review of LinkedIn profiles show how the automaker is scaling its ride-hailing service.
Ford is switching gears from large EVs: 'It was really the customer changing their decision'
Ford is scaling back its large electric vehicle production and prioritizing hybrid vehicles, a move that is expected to cost nearly $20 billion.
AMD’s “FSR Redstone” upscaling claims to close the gap with Nvidia’s DLSS
A mix of new and returning tech to improve speed, image quality on new AMD GPUs.
Google DeepMind CEO Demis Hassabis says AI scaling 'must be pushed to the maximum'
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Vaulted Deep Named "Best in Business" By Inc. Magazine for Scaling Critical Waste Infrastructure while Delivering Carbon Removal
Vaulted Deep has been named to Inc.'s 2025 Best in Business list for scaling the first commercial infrastructure for simultaneously managing organic waste while delivering permanent carbon removal. The recognition in the Best Startups category follows a year of growth across Vaulted's operations, infrastructure, and commercial traction. In 2025, Vaulted expanded its operating capacity, delivered more than 23,000 tonnes of verified CO₂ removal, and secured long-term agreements with Microsoft and
From momentum to endurance: Scaling structured literacy with implementation science
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