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Robinhood was under fire after the GameStop controversy in 2021. But last year, it posted its strongest results ever. FC Explains how Robinhood rebuilt trust, launched powerful tools, and made a major comeback.
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E-Commerce
Public servants today face a double burden: Theyre simultaneously charged with running our most important community functionslike disaster preparedness and administering electionswhile the technology at their disposal is outdated and ill-fitted to the job. The rise of AI has upended how private companies operate, but public servants across agencies lack AI tools designed specifically with government work in mind. In a perfect world, public servants could trust mass-market AI. But provisioning critical services requires a high bar. Quickly deploying technology prone to generating inaccuracies is an unacceptable tradeoff for those solving societys hardest problems. Few of us would be happy to get our mail a day earlier if it meant 10% of our mail never came. The tradeoff is more acute when public servants are working to end homelessness or reinvigorating economic development. AI is more accurate and useful for government employees when it’s built atop core data assets like documents and emails and the contextual metadata around those assetsinformation like who shared documents, when they were shared, and the conversations that surrounded them. Public sector AI requires context The massive opportunity to empower public servants and improve government operations with functional, reliable AI tools comes not from training larger, smarter models, but ensuring AI has context. Context is everything because government operations depend upon the local partners, procedures, history, and regulations of a community of practitioners. For AI to work for public servants, it must understand the context well enough to generate accurate information. Todays AI tools fall short because they lack contextual metadata. Without this information, AI is not fit for purpose. Public servants cannot sacrifice accuracy for speed. Siloed technology destroys context Government work is inherently collaborative. Cybersecurity officials work with state and federal counterparts, and homelessness coordinators work with public health departments. But there is a fundamental mismatch between the collaborative nature of government work and the silos of most technology. Todays AI tools generally serve single organizations, lacking functionality to enable cross-agency collaboration. When FEMA responds to disasters, utilities, hospitals, shelters, and community organizations all play key roles. Public servants coordinate these nongovernment partners, but isolated AI systems can only access information within their own agenciesmissing the context that lives across organizations. And the work doesn’t happen in siloed agency folders. It happens in email threads, texts, unshared working documents, and view-only, versioned, and immediately outdated shared documents. These disconnected digital workspaces destroy context. But this is a technology problemwhat does a context-rich technology look like? The government operations tech stack Effective government AI must be attentive to the different technology layers that underpin the work of public servants. We can visualize the government operations tech stack in four layers: Layer 1: SystemsThe first, foundational, layer comprises the file storage systems: OneDrive, SharePoint, local folders, Outlook, and other repositories. While this is where key information often lives, it is rarely well-organized or accessible to outside partners. Layer 2: ResourcesThis refers to the resources themselves. Think individual files like memos, spreadsheets, SOPs, and more. While enterprise AI systems can access one organizations documents, they miss the critical context of how and why these resources were shared, who created them, and what discussions they generated. Layer 3: Coordination The coordination layer encompasses emails, texts, events, direct messages, and video communications. This is where cross-organization collaboration happens and where ongoing discussions shape decisions. It contains the three sentence email from the 30-year department veteran, who succinctly explained where an internal policy originated, why it was created, and which parts no longer apply. This is institutional knowledge shared in real-time. AI tools without access to the coordination layer are set up for failure. Layer 4: InterfaceThe interface layer is where public servants make use of the data across layers. And this is where purpose-built AI can make an impact. Government officials should be able to get immediate answers without needing to recall whether information lives in a shared drive, email, video call, or calendar event. And the interface layer doesnt end with a search it should enable the next step, whether thats drafting a policy, connecting with a subject matter expert, or reaching out to partners. Atop digital layers are public servants making decisions and taking action. This is where policy meets practice, where coordination becomes execution, and where community needs are met. Only context-rich AI can reliably scale public impact An AI interface with the full contextual metadata of government operationsthe systems, resources, and coordination layersbecomes transformative. An elections official searching for polling center volunteers finds not just the sign-up sheet in their drive, but also the follow-up email from a facilities manager identifying the correct entrance, the text from a sick volunteer needing replacement, and the recent listserv discussion correcting the record about the polling location entrance. AI with this context provides a complete operational picture, not isolated documents that become outdated as soon as theyre created. During emergency response, an AI with contextual access can connect FEMA policies with real-time partner communications, community feedback, and operational updates. Instead of just knowing what documents exist, the AI understands who shared critical information, when situations changed, and why certain decisions were made, enabling more effective coordination and faster response times. This contextual AI doesn’t just provide informationit provides traceable, auditable insights that public servants can trust and act upon. It connects users not only to the right documents but to the right people and the right conversations, embedded within their specific community and operational context. The vision is clear: AI that lives where government work happens, with access to the full collaborative environment across organizations. When deployed with complete contextual metadata, AI can empower public servants to make a bigger impact while maintaining the accuracy and accountability needed. Government operations are fundamentally about coordination and context, and AI must reflect this reality to succeed in the public sector. Madeleine Smith is cofounder and CEO of Civic Roundtable.
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E-Commerce
The healthcare industry has many ills. The payer-provider disconnect creates confusion, limits access, and exacerbates inefficiencies. Doctor and nurse burnout has led to widespread staffing shortages. This is compounded by aging infrastructure, outdated regulation, fragmented care delivery, and overly-complicated legacy systems. The list goes on. But there is a particular cancer that we could eliminate tomorrow: big consulting firms. Every year, American healthcare systems spend hundreds of millions of dollars on consulting firms that deliver PDFs instead of solutions. While patients suffer and clinicians burn out, these legacy firms collect their checks and move onleaving implementation challenges to healthcare institutions they’ve diagnosed but failed to treat. Proponents of the Big Five consulting model would argue that sclerotic institutions need an untainted outsider to parachute in. Someone who isn’t married to the nuances of an existing system. But theorizing is easy to do when you’re not tethered to the results. In healthcare, it’s not just financial results the consultants are off the hook for, it’s people’s lives. What do providers get with a legacy consulting firm? Well, a sizable stack of documents. With massive fees to match. While a 300-page PDF may impress at first blush, it won’t lead to actionable, sustainable solutions. It certainly won’t allow the health system to rapidly test and refine new models of care delivery, proving which ideas do and dont work in practice. Time pressure and fitting solutions in boxes At Cactus, the design firm I cofounded, one of our clients was let go from a major health system as part of a big consulting cost-cutting round. He had been working to reduce staffing shortages and developed a novel method for care teams to work together more efficiently, freeing up precious time for overworked nurses. Despite having proven results, he was let go because the consultants couldnt fit his already-implemented, already-proven solution into their model. He has now founded a business to sell that same method to health systems as a SaaS business. Traditional consulting firms also worsen a key challenge in healthcare: time pressure. Most health systems plan year-to-year based on government reimbursements. With revenue cycles already complex, consultants often default to short-term cost cuttingan easier sell than long-term change. The result? Innovation stalls, patient experience suffers, and the cycle repeats. If it’s so difficult, why not just leave healthcare alone? Because clearly, patients want better services. And by and large, those closest to the patient aren’t the problem. There is an abundance of clinical excellence in the United States but this doesn’t always translate to the best outcomes or patient experiences. The disconnect lies in how we approach system-level change. While working with a leading cancer center, my team found a big pain point for doctors: low compliancein other words patients werent following instructions. Research revealed that while treatment plans (housed in large binders) were technically sound, they werent tailored to patients. As a result, patients would underperform. We found that clearer communication, organized around actionable items and digestible content, could drive meaningful improvement. This user experiencefirst investigation, which centered on the needs of both doctors (frustrated by low compliance) and patients (overwhelmed by information), is rarely prioritized in traditional consulting models but is core to a more modern, design-led approach. The house renovation problem Imagine you’re renovating a Victorian mansion with a funky layout. The big consulting approach would be to tell the construction team to spend the allotted budget on turning the largest bedroom into two bedrooms, thereby increasing the home value. Maybe they’d advise a fresh coat of light grey paint, chosen to be least likely to offend potential buyers. But what if the person buying the home doesn’t have people to fill those bedrooms? What if they would be happier with a bolder color? What if in the process of splitting the bedroom, the floorboards are found to have mold? Well, the suit-clad consultants are already gone. You’re on your own, kid. Now imagine a design-led approach. In this scenario, the firm leading strategy is also the one implementing the changes. They spend the budget shifting the plumbing, dealing with mold issues as they arise. They don’t add another bedroom because it’s not needed, even if it would theoretically increase value. They try out a few paint swatches and see market appetite for bolder colors. Buyers are happierthey’re willing to pay more! Everyone wins. From slide decks to solutions Apply this rubric to healthcare. A design-led approach can balance strategic and business goals with the realities of user experience and complexities of implementation. Consultants that also build can pilot innovations faster, see results faster, reducing overall risk and cost. This type of team can rapidly experiment and improve in a virtuous cycle like the best startups do. Design-led consulting firms that implement their own changes also have a higher stake in outcomes. They create working prototypes before prescribing final solutions. They iterate based on real-world feedback. This way mistakes are found fast and plans are adjusted before scaling, saving cost and allowing faster and more thorough implementation. A call to healthcare leaders To healthcare leaders, I ask: What could you ship in the next 90 days with a design-led approach? What might still be sitting in a binder three years from now with traditional consulting? If the answer is something that could change people’s lives, and I suspect it is, it might be time to ditch the big guys. Design-led firms offer a fundamentally different relationship: partners who share your risk, commit to real-world results, and aren’t afraid to get their hands dirty implementing solutions. They bring technical talent alongside strategic thinking. They work in weeks, not quarters. Most importantly, they are judged by what they build, not what they recommend. The question isn’t whether you can afford this approach. The question is whether you can afford not to try it. Because while big consulting firms continue collecting their checks for delivering their slide decks, your patients and workforce are waiting for something better. They deserve it. And with the right partners, you can finally deliver it. Noah Waxman is CEO and cofounder at Cactus.
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E-Commerce
America’s behavioral health workforce is in crisis. Burnout is accelerating, waitlists are expanding, and clinicians are transitioning away from the field at alarming ratesnot because their passion has diminished, but because the administrative burden has become overwhelming. These departures represent one of the most significant labor challenges in our industry with its impact felt across communities nationwide. Every week, I talk to leaders running certified behavioral health agencies across the country, and their message is consistent: Leaders aren’t seeking technological disruption. They’re desperate for breathing room to retain staff, serve more clients, and maintain compliance without drowning in documentationa fundamental workforce sustainability issue. When artificial intelligence enters the conversation, many approach it with justified skepticism. This caution stems from two critical concerns: First, behavioral health has endured its share of overhyped technology that promised transformation but delivered frustration; second, and equally important, is the critical issue of regulatory compliance, privacy, and data security in this intensely regulated field. Documentation: The critical foundation for reimbursement and compliance In behavioral health, documentation forms the foundation for intake, clinical interactions, reimbursement, compliance, and broader care accountability. Providers rightfully question whether AI-generated notes (where technology assists in creating clinical documentation) will withstand audits or satisfy complex regulatory requirements. AI technology not only facilitates the creation of clinically relevant documentation but also enhances oversight capabilities and ensures full auditability of all generated content. The daily reality for most providers involves hours of note-taking, repetitive scheduling, and navigating documentation systems never designed for behavioral health’s unique compliance demands. We call this “administrative burden,” but the term fails to capture the resulting exhaustion that drives talented clinicians from the field. According to a recent study by Google Cloud and The Harris Poll, U.S. clinicians spend nearly 28 hours weekly on administrative tasksover half of their work week, with 82% reporting burnout. In behavioral health’s already resource-constrained environment, this burden often becomes the breaking point for dedicated professionals, contributing to a sector-wide labor shortage. Find solutions in thoughtful AI implementation With clinicians overwhelmed with documentation that causes burnout, behavioral health professionals need solutions that work for them. This is where carefully designed AI tools show great promise. Early AI implementations focused on documentation and administrative workflows are demonstrating measurable benefits when developed with clinician input and compliance requirements at the forefront. Organizations deploying these tools thoughtfully see tangible impact: In our current deployments, clinicians are reporting up to 80% reduction in clinical note-taking time as technology listens and drafts notes in the background. Smart assistants help staff locate resources without interrupting care. Intake workflows become more efficient, shortening the gap between a client’s first call and first session. These tools don’t put an end to compliance complexitythey help manage it while creating space for providers to be present. Care gets better when clinicians are supported I remember hearing directly from a patient that during his initial session, his provider never once made eye contact with him. Instead, they were focused on their computer, furiously typing notes and immersed in updating his electronic health record (EHR). Fortunately, thats where we repeatedly see that AI can improve care delivery. By removing needless administrative work, behavioral health clinicians have more time to form connections and spend more time with their clients, achieving better outcomes. According to our customers, 50% say they feel more connected to their clients without increasing hours and 60% say they feel more connected to their patients now that theyre not documenting during sessions. I also hear from clinicians that they see greater consistency in their care plans with fewer missed handoffs. These aren’t merely efficiency gains, but restoration of human capacity essential for building trust, ensuring continuity, and addressing complex behavioral health needsall critical factors in workforce retention. AI won’t fix everything To be clear, AI isn’t a silver bullet. It won’t resolve funding challenges, address workforce shortages, rebuild trust between providers and policymakers, or guarantee that alternative payment models work effectively for behavioral health. What it can do is create breathing room and greater capacity by providing clinicians with the margin needed to practice what they are trained for. In a field where every additional hour with a client, every prevented resignation, and every accelerated intake process can transform a life, that support matters profoundly. 3 critical elements for success For AI to deliver on its promise in behavioral health, its important to focus on three critical elements: clinical expertise driving development, seamless integration into existing workflows, and evaluation against regulatory standards. The most effective AI tools are designed with clinicians informing their development, refined with their feedback, and tested against compliance standards and regulations during audits. Behavioral health needs meaningful innovationtools that honor the work, alleviate friction, and strengthen the relationship between clinician and client. This will help preserve our essential mental health workforce to our social infrastructure Josh Schoeller is CEO of Qualifacts.
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E-Commerce
This morning, the worlds largest telescope revealed its first-ever images of spaceand theyre pretty jaw-dropping. The images come courtesy of the NSFDOE Vera C. Rubin Observatory, a scientific facility funded by the U.S. National Science Foundation and the U.S. Department of Energy’s Office of Science. Located at the summit of Cerro Pachón in Chile, the facility is the product of more than 20 years of work. Its space cameraembedded in the hulking Large Synoptic Survey Telescope (LSST)is about the size of a small car and includes a sensor array of 3 billion pixels, the most sensors ever used in a telescope camera. According to a press release from the Rubin Observatory, its expected to generate an ultrawide, ultra-high-definition time-lapse record of the universe. It will bring the sky to life with a treasure trove of billions of scientific discoveries, the release reads. The images will reveal asteroids and comets, pulsating stars, supernova explosions, far-off galaxies, and perhaps cosmic phenomena that no one has seen before. The most efficient Solar System discovery machine ever built In just its first 10-hour test observation, unveiled today, the LSST managed to capture images that include millions of galaxies and Milky Way stars, as well as more than 2,000 never-before-seen asteroids within our solar system. Taken together, the photos illustrate a technicolor view of space at a mind-boggling scalebut the 10 million galaxies photographed by the LSST represent only 0.05% of the roughly 20 billion galaxies that the camera is expected to record within the next decade. The primary goal of the LSST is to complete a 10-year survey of the Southern Hemisphere sky, capturing hundreds of images and around 20 terabytes of data per night throughout that period. Per the Rubin Observatory, this massive influx of data will make the LSST the most efficient and effective solar system discovery machine ever built. All of the captured data will be made available online, allowing astronomers across the globe to access countless new findings without physical access to the telescope. The LSST is designed to advance four main areas of study: understanding the nature of dark matter and dark energy; creating an inventory of the solar system; mapping the Milky Way; and exploring the transient optical sky (studying objects that move or change in brightness). Experts predict that, given its capacity to identify millions of unseen asteroids, comets, and interstellar objects, the camera could even help protect the planet by spotting objects on a trajectory toward the Earth or moon. NSFDOE Rubin Observatory will capture more information about our universe than all optical telescopes throughout history combined, Brian Stone, chief of staff at the National Science Foundation, said in a press release. Through this remarkable scientific facility, we will explore many cosmic mysteries, including the dark matter and dark energy that permeate the universe.
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E-Commerce
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