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Amaya Matos was 19 years old when she was diagnosed with acute myeloid leukemia. Shed just graduated high school, but instead of heading off to college or hanging out with friends, she spent much of her time sitting in a hospital bed, scrolling on her phone. Getting treatment for cancer can be extremely isolating: physically, especially when youre inpatient and at constant risk of infection; and mentally, because no one else in your life fully understands what youre going through. Now 25, Matos is approaching five years of remission, but shes still managing complications. A stem cell transplant triggered graft versus host disease, a condition in which donated stem cells attack the patients own body. Chronic graft versus host disease is rare, and navigating it can be just as lonely. But today, Matos has a way of connecting with others going through the same thing: an app called CancerBuddy. It connects patients, survivors, and caregivers through an array of filters like diagnosis, age, and gender identity. Users can swipe through CancerBuddy like a dating app to match and talk with others, and the app also has topic-based groupsthere’s one for young adults, one around nutrition, and even one for graft versus host disease. Without this, I dont know where I would be in my survivorship journey, because I dont know who I would have leaned on that would have really gotten it, Matos says. Before doctors appointments, she often checks the group to see what others have shared. It allows me to feel like I can advocate for myself, she says. Im allowed to ask questions. Im allowed to ask for more elaboration. ‘Why not use this to connect patients?’ The app, developed by the Bone Marrow & Cancer Foundation, launched in beta in 2022 and has recently begun rolling out to hospitals. Christina Merrill, founder and CEO of the Bone Marrow & Cancer Foundation, created CancerBuddy after decades of working directly with cancer patients. Ive always been a big proponent of connecting patient to patient, survivor to survivor, caregiver to caregiver, she says. Yet hospitals and advocacy groups often struggle to make those connections. For years, the foundation facilitated introductions manually through its website. That approach was hard to scale and complicated by HIPAA (Health Insurance Portability and Accountability Act) rules, since it required patient consent before sharing any information about diagnoses. (CancerBuddy is private and doesnt share information; its also free.) Some hospitals have peer programs and support groups too, but then the COVID-19 pandemic prevented people from gathering in person. Sometimes people also travel far for care, so they cant easily come back to a facility for such a group. Hospital staff like nurses or social workers (which Merrill was for years) are often too overworked to connect patients. Inspiration struck as Merrill returned to the dating world after a divorce. She began swiping on dating apps, and thought, This technology is incredible. Why not use this to connect patients? She worked with design agency Frog to build the CancerBuddy app, equipping it with all these different filters so patients could find, really, the perfect match. Connecting cancer patients by their commonalities is crucial, Merrill says. A patient sitting in a hospital with colorectal cancer who is 20 years old, its very hard for them to relate to somebody that has colorectal cancer whos in their 60s or 70s. They want to meet somebody else that is their age and where they are in their life, she says. The same applies to diagnosis; someone with breast cancer undergoes different treatments than someone with leukemia. They cant feel the support they would if they meet someone with the exact same diagnosis, Merrill says. Cancer survivors, too, face their own unique experiences, including a lot of uncertainty. Are they going to relapse? What can they do or not do in terms of activities post treatment? Merrill says. Cancer survivors commonly experience anxiety and depression. They’re supposed to be relieved that they’re cancer free. But instead, they’re worried about the future, so they can relate to others that are going through that, and really get that support that they need. ‘You feel less lonely’ Then there are the caregivers. Matoss mom filled that role, driving her to treatments and even donating stem cells. When she got on the app as a caregiver, she [had been] down the rabbit hole of, why is this happening to my kid? It doesnt even run in our family, Matos said. On CancerBuddy, she found other caregivers asking the same questions and wrestling with the same emotions. It just makes you take a step back and think, were all in this together. We can continue to be in it together and share highs and lows. Research consistently shows that social support boosts cancer patients emotional wellbeing and overall quality of life. Peer connection reduces loneliness, improves mental health, and can even affect survival: one study found that cancer survivors who reported higher levels of loneliness were 67% more likely to die than those with stronger social ties. And the need for connection is only growing. In the coming year, experts project that new first-time cases of cancer in the U.S. will surpass 2 million. Thats nearly 5,500 new diagnoses every day. Some patients and survivors turn to public Facebook groups, but those are open to anyone, arent run by cancer organizations, and lack moderation, Merrill notes. CancerBuddy, by contrast, is moderated by her team. In one case, a teenager newly diagnosed with lymphoma posted in the adolescents and young adult group, saying he was questioning his life. Merrill was quickly alerted and able to check in. Within minutes, others in the group also began messaging him. Hes still a member today. CancerBuddy remains small, with about 6,000 patients and survivors currently using it, but Merrill says the numbers grow every week. For many, the impact is already clear. As Matos puts it: You feel less lonely. You really do. And I think that that’s a huge thing when you’re going through something.
Category:
E-Commerce
Over the last decade, design challenges have become a controversial tool in hiring designers. Intended to evaluate how candidates think, behave, communicate, solve problems, and brainstorm ideas, these exerciseswhen well-facilitatedcan offer valuable insight into a designers character, their story and capabilities.However, a troubling trend has emerged: When the design challenge is poorly facilitated and poorly contextualized it defaults to placing candidates in unfamiliar business domains that overemphasize solution delivery. These flawed challenges not only misrepresent what good design entails but also risk excluding the very designers we seek to attractthose who prioritize inquiry over assumption based execution and context over conjecture. The Problem: Poorly Facilitated Design Challenges Foster Bias Toward Solution Accuracy At the core of this issue lies facilitation. A challenge, no matter how well-intentioned, becomes a biased and misleading evaluation tool when it bypasses critical elements of the design processdiscovery, user empathy, context-building, and problem framing. When a candidate is asked to “solve” for a hypothetical business challenge they have no familiarity with, the assessment begins with presumption, not understanding. This turns a designer into a guessersomeone making assumptions in a vacuum, rather than an investigator uncovering real human behaviors and systemic constraints. As a result, facilitators are now evaluating how well someone can perform under artificial constraints, which rarely correlates to success in real-world product environments. In the absence of context, behavioral data, access to stakeholders, or a room for discovery, the challenge becomes a proxy not for good designbut for rapid conceptual execution. Jeff Appel, Lead Product Designer at Salesforce, shared insights on LinkedIn regarding the pitfalls of design challenges; highlighting concerns about the absence of a clear evaluation methodology and criticized the flawed “lone designer” premise often associated with many of these poorly facilitated challenges. The article “The hidden cost of design tasks” written by Jane Austin emphasizes that design challenges often overlook crucial aspects such as collaboration with teams, access to stakeholders, and actual constraints of project work. This can result in tasks that favor those adept at test-taking rather than those skilled in holistic, strategic design. Echoing industry leaders like Mike Monteiro and Jared Spool, the article warns against reducing design skills to shallow deliverables, advocating instead for assessments rooted in authentic professional scenarios. The Impact: Disadvantaging Depth in Favor of Surface Level Thinking This model disproportionately favors designers who are confident improvisers rather than those who are methodical problem framers. It elevates speed over strategy, polish over process, and solutioneering over systems thinking. In doing so, it filters out designers who may take a more rigorous, research-first approachthose who excel in cross-functional collaboration and ongoing discovery, which are far more indicative of real-world success. When challenges require deep business familiarity without offering foundational context, we are no longer testing design abilitywe’re testing prior business exposure. The Fallacy: Familiarity as a Proxy for Competence The notion that a strong designer should be able to design for any business problem, on the fly, is not only unrealisticits actually, anti-design and directly contradicts the well known teachings and practices of Don Norman and Dieter Rams. True design leadership lies in asking the right questions, navigating ambiguity, and co-creating with users and stakeholders, by which the human is placed at the heart of the process. Designing a product for an unfamiliar domain without access to the system, people, or environment that define it undermines the very nature of human-centered design. Imagine asking a surgeon to operate based on insufficient data, patient history, situational context and a few vague bullet points. The analogy may sound extreme, but design, when practiced at a strategic level, demands similar rigor. It is not about instant answers or reacting to the all too common request, just show me what it looks likeit is about intentional framing, exploration and continuous discovery. The Supporting Points of View: Other design pros weigh in In reporting this story, I spoke to other design professionals to get their opinions on this topic. Heres what they said: As someone experienced in hiring, Ive shifted away from design challenges in interviews. Instead, I favor a structured process involving cross-functional partners and meaningful conversations to better assess a designers capabilities and fit. “A key part of this process is the portfolio review, which reveals more than speculative exercises can. It shows how candidates tell their stories: Are they tailoring their presentation or using a generic deck? Do they clearly define the problem space, navigate ambiguity, and reflect on both successes and challenges? Ryan Leffel, VP, head of design, Priceline This article calls out what so many designers have experienced but often cant say out loud: speculative design challenges can easily become performative traps. They ask candidates to “solve” abstract problems in unfamiliar industries, without research, context, or real users. Then they assess their “fit” based on polish and speed instead of strategic thinking or collaborative depth.” Brian Rice, former chief design and brand experience officer, 3M, and founder of Rice & CoDesign, LLC “A frequent issue is the bias toward speed. Many candidates race to produce polished work in a short window to impress a hiring panel. The result is often beautifully sketched or polished UI from assumptions rather than carefully reasoned design. “This fosters a dangerous dynamic. We start rewarding presentation shine and clever hacks rather than thoughtful inquiry, stakeholder alignment, or systemic thinking.That means we end up hiring the designers who look fast and clever under pressure rather than the ones who excel at navigating ambiguity, asking the right questions, and collaborating across functions. “It is the design equivalent of hiring a surgeon because they stitched something up quickly, without checking whether they addressed the underlying diagnosis.”Thomas Wilson, customer journey manager and strategist, MedicaI am a proponent of behavioral interviews with a defined rubric supported by a case study presentationrather than a standalone design challenge. I work closely with the hiring panel to ensure each interviewer understands the riteria and has the right area of focus that complements the role. For example, topics may include stakeholders and partnerships, business impact and technical skills, or, for a leadership position, team growth, scale, and the ability to navigate difficult conversations.Jose Coronado, managing director, Digital Impulsum The Solution: Redesigning the Challenge around Discovery, And Critical Thinking; Not Just Solution Creation Instead of setting up a design challenge based off a speculative hypothesis, what if we invited candidates into our real-world context? Let them probe and evaluate actual existing artifacts. Ask what questions they would explore. Evaluate how they frame problems, define a research strategy, navigate trade-offs, and identify behavioral insights. This doesn’t mean handing them sensitive databut it does mean shifting from output-focused to process-focused evaluation. Give candidates room to express how they would approach ambiguitynot just what they would build. Reward those who identify critical gaps, challenge flawed assumptions, navigate difficult relationships, continue discovery using robust, yet flexible design frameworks.A broken challenge leads to broken decision makingand ultimately, a broken hiring model. The solution lies not in throwing out the design challenge, but in rethinking its intent: from proving solutions to revealing process, mindset, character and inquiry. Conclusion: Moving Toward a More Contextual, Human-Centered Hiring Practice As design leaders, we must hold ourselves to the same standards in our hiring practices that we demand in our products and services: contextual, inclusive, research-driven, and iterative. If we continue to rely on speculative challenges that ignore the foundations of good design, we risk building teams that mirror expediency rather than excellence.Jane Austin’s article does acknowledge the persistence of design tasks in some hiring contexts, sometimes as practical tools where portfolios or references are unavailable. Veteran design leaders such as Julie Zhuo and Khoi Vinh recognize that a well facilitated designed challenge, set with the right context, supported by portfolio reviews and a two-way, interactive conversation can reveal a candidates unique approach to ambiguity and cross-functional collaboration scenarios.These global design voices stress the importance of transparency, respect for candidate time, and integrating multiple touch-points throughout the hiring process. Ultimately, the consensus across the industry leans towards more holistic, equitable practices that value creativity and ethical evaluation rather than relying solely on solution creation and standardized tasks.
Category:
E-Commerce
When OpenAI launched its new GPT-5 model in August, the company bragged loud and hard about how GPT-5 is its smartest, fastest, most useful model yet and how interacting with it was like chatting with a helpful friend with PhDlevel intelligence. When it comes to creative tasks like writing, GPT-5 immediately felt like a major step backward. But as Ive tested the model more extensively, Ive seen that it does excel at many pragmatic tasks like writing code and analyzing data. That got me thinking, How would it do as a stock picker? If GPT-5 is great at processing massive sets of complex dataand its supposed to be widely useful and a legitimate PhD-level expert in everythingwhy not have the model put its money where its mouth is and perform the widely useful task of making me fabulously wealthy? To that end, I gave GPT-5 (via ChatGPT) $500 of real money to invest however it wanted, with the stated goal of earning me as much as possible over the next six months. I expected generic investment advice. Instead, its picks truly surprised me. Not my first AI rodeo Before we go further, let me be clear that nothing in this article should be considered financial advice, and you certainly shouldnt trade based on anything I share here. Im a journalist conducting a crazy experiment. You should get your financial advice from professionals, not chatbots. Also, this isnt my first rodeo. I tried a version of this experiment before in the very earliest days of the generative AI boom, so I have at least a vague idea of what Im doing. Back in 2022, I served as a beta tester for OpenAIs GPT-3 model. This was months before ChatGPT was released and the company blew up into the headline-winning, job-devouring behemoth it is today. Back then, it still operated as a wonky research lab, making its tools available to journalists and researchers for free. Without a proper chat interface, testers like me had to submit our requests to the AI via what was basically a web-based version of a classic computer command line. Still, I was able to cajole GPT-3 into picking a stock portfolio, a process that I documented at the time on a now long-forgotten blog. Its choices were, lets say, rudimentary. It essentially took a momentum-based approach, recommending stocks like Ralph Lauren and Wynn Resorts that had already done well that year. To those picks, it added Microsoft, Apple, and Amazon on the basis of the fact that theyre tech giants. In 2022, it was extremely cool just to see a computer write out a narrative of any kind. But its analysis wasnt exactly groundbreaking. Any idiot can tell you Buy Microsoft and stand a pretty good chance of making you money. Finding nuance and opportunity that others have missed is much harder. Still, GPT-3s early picks proved to be solid ones. As I write this, its portfolio is up 82.15% since I ran my first experiment back in 2022. The S&P 500 gained about 67% over the same period. Seeing that OpenAIs modelseven in their early infancycould outperform the market gave me confidence. Still, basically everything gained value since 2022; the investing landscape back then was much less murky than it is today, and grabbing any handful of individual stocks was likely to make you good money. Also, nearly three years is a long time to wait for what still amounts to fairly modest gains. The model did well, but it didnt even manage to double its money in that time. I wanted otherworldly riches, not mild alpha. And I wanted them now. Never mind safety It was with that mindset that I turned to GPT-5 and asked it to make me a portfolio of stocks fit for the Dadaesque, tariff-laced, AI-besieged world we inhabit here in 2025. Specifically, I told ChatGPT with GPT-5s Thinking model selected that I would give it $500 to invest however it saw fit. I wanted it to maximize my returns over the next six months by picking five public-market stocks. Here was my prompt: I will give you $500 to invest in the stock market. You may choose up to 5 stocks. Make your picks, explain why, and I will buy them and we will see how they do. To be honest, I didnt expect much. OpenAIs models have gotten more powerful since 2022, but theyve also gotten far more squeamish. When I served as a beta tester, only nerds like me were using the companys products. We basically had free rein to ask them anything we wanted. With billions of people now using the companys models, OpenAI has understandably tightened the leash quite a bit. In a blog post around GPT-5s release, the company explained its new safe completions framework, an extremely robust approach to elegantly weaseling out of answering potentially damaging questions. I thought GPT-5 would answer my stock-picking question safely, with either a cop-out (Talk to a professional adviser) or a wussy response (Invest for the long term in a low-cost S&P 500 index fund, ya putz!). Instead, it spent eight minutes mulling over my query before returning what it called a Diversified High-Growth Portfolio. Its picks werent wussy or generic at allthey were clever and highly aggressive. Show me the stocks GPT-5 recommended that I spread my $500 evenly over five companies: Palantir (PLTR), AppLovin (APP), Agios Pharmaceuticals (AGIO), Hut 8 Corp. (HUT) and MicroStrategy Inc. (MSTR). Thats very different from saying Buy Microsoft and calling it a day. Id never heard of half the companies on GPT-5s list. And even the ones Id heard of, like Palantir, werent companies Id ever considered investing in. These picks certainly felt like they had the potential to be under-the-radar winners. But how the heck had GPT-5 chosen them? Unlike with OpenAIs earlier model, GPT-5 didnt make me guess as to its investment thesis; it laid out the details of its choices clearly, sharing that it had read 98 articles and websites in order to make them. Palantir, the model said, was driven by its AI/data platform and was gaining traction in comercial and government sectors. Based on investor enthusiasm for its AI-driven growth, GPT-5 expected the stock to keep achieving big gains. GPT-5 liked AppLovin for much the same reason, citing its proprietary AI engine. But the model also looked at its fundamentals, pointing out: Analysts note that even after strong gains, shares trade only ~8% below peak levels, suggesting room if growth continues. Agios made the cut for a totally different reason. GPT-5 said that Agios is awaiting an FDA [Food and Drug Administration] decision . . . on expanding its lead drug Pyrukynd to treat thalassemia, a large unmet need. If approved, Pyrukynd would be the first therapy for all thalassemia subtypes. A positive FDA outcome or even renewed optimism could spark a significant rally. Basically, GPT-5 seemed to be placing a risky bet on the company achieving FDA approval for a potentially lucrative druga piece of upcoming news that could easily spike or tank its price. Finally, GPT-5 recommended Hut 8 and MicroStrategy essentially because it wanted exposure to cryptocurrencies. The model noted that MicroStrategy holds almost $71 billion worth of Bitcoin, making it a highly leveraged Bitcoin play, while Hut 8 has transformed from a pure crypto miner into an energy-infrastructure platform for both Bitcoin mining and AI/HPC data centers. The model concluded: Overall, the portfolio aims for explosive upside rather than stability. Basically, it had thrown safety to the wind and taken the approach of picking the riskiest, trendiest things it could find (AI, crypto, early-stage pharma) and throwing all the money at them. Going boldly Again, I was impressed that GPT-5 didnt simply chicken out and tell me not to risk losing my money. But beyond that, I was impressed by how well it had followed my prompt. I hadnt asked the model for safe or sane bets. I had asked it to take an unreasonably short investment timeframe and make me as much money as possible. Its portfolio reflects that perfectly. Its picks are bold, get-rich-or-die-trying options. Either Agios will get a positive decision from the FDA and flourish, or its trials will go poorly and it will suffer. Bitcoin will either keep climbing or reveal its signature volatility, potentially tanking the models last two picks. Palantir is indeed on a roll right nowthat could continue, or the stock could fall, Icarus-like, back to earth and take my money with it. Id essentially asked the model to roll the dice, and it had done that splendidly. Its advice isnt good exactly, in the sense that its picks are incredibly risky. But theyre true to my intent. That reflects another facet of the new modelthe highly accurate “instruction following that OpenAI promised in GPT-5s release notes. GPT-5 may not be Shakespeare, but its very good at determining what its users want and delivering that as accurately as possible. GPT-5 also appears to have gotten the details in its response (stock prices, previous gains, adviser notes) largely correct. That fits with OpenAIs assertion that GPT-5 hallucinates far less than previous models. With my new AI portfolio in hand, the only thing left to do was fire up the Robinhood app, transfer $500 from my bank account, and buy the stocks ChatGPT had chosen. So, I did exactly that. As I write this about two weeks later, GPT-5s stocks are already up about 10%. Thats the kind of rapid early growth I was seeking. So, will I end this experiment with Lambo money, or will GPT-5s portfolio crash and take a car payments worth of my cash down with it? Is throwing hundreds of dollars at a silicon-bound pseudo-intelligence a good idea or financial folly? Ask me in six months.
Category:
E-Commerce
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