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In 2020, the murder of George Floyd and the growing Black Lives Matter movement put a spotlight on diversity and equity issuesincluding in the business world. Suddenly investors were clamoring to support Black entrepreneurs, a way of course correcting a history of underinvestment and unfair barriers. In the year after Floyds murder, a given investor was 36% more likely to invest in a Black-founded startup, and the overall share of venture capital dollars going to Black entrepreneurs grew by 43%. Major investments made headlines. SoftBank, for example, announced a $100 million fund that would invest only in companies led by founders of color. Andreessen Horowitz launched a $2 million fund focused on founders from underserved communities, and VC firm Revolution set aside $2 million to support Black entrepreneurs. But that surge didnt last, according to new research out of Cornell University. Within two years, investment in Black-founded startups reverted back to previous levels. (Some studies say just half a percent of all VC funding goes to Black founders; separate Cornell research found that just 3.5% of founders seeking funding from VC firms are Black.) For this new study, Cornell researchers analyzed data from PitchBook, which shows venture capital funding into companies. They used software (and then manually checked it) to classify images of about 150,000 founders by race, and then looked at investments from 2020 to 2023. The researchers also looked at who was driving the temporary surge in investments, digging into about 30,000 investors. They found that the main increase in funding was primarily among venture capitalists who had never previously invested in a single Black entrepreneur. The researchers found that Black entrepreneurs with stronger business track records were less likely to take investments from those newcomer investors in that period after Floyds death. In other words, the strongest Black entrepreneurs paired up with investors who had more of a track record of investing in founders of color, says study coauthor Matt Marx, a Cornell professor who focuses on entrepreneurship and innovation. To Marx, that shows how investing is a two-way relationship: The investor has to get the entrepreneur to agree to take their money, and so it becomes a little trickier to right past wrongs or address the issue if the very people you were ignoring have to agree to go along with you. Most VCs who did invest in Black entrepreneurs also did so in a less engaged way, the researchers found. These investors were about 15% less likely to take a board seat in the startup they supported, for example. So where does this leave Black founders now? Marx has other research that points to some opportunities. In a 2024 study, his team found that the racial funding gap for startups is smaller when a business comes out of an acceleratorlike Y Combinator or Techstarsthan just through VC funding. With traditional VC investing, founders may need a personal connection to get referred, which can leave certain people out. If venture capital funds adopted a more open application process like accelerators do, that could help close the funding gap, Marx says. And though that surge of investments to founders of color petered out, overall the number of Black founders is growingalbeit slowly. Although Black entrepreneurs represented just 3.5% of all founders seeking VC funding over the past 20 years, in the past five years that number has grown to 4.5%. That means Black founders are still underrepresented, Marx says. But maybe [that growth] gives someone the thought that, Hey, I can play this game too. Im going to take an entrepreneurship course or apply to an accelerator. Thats my hope.
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E-Commerce
Ive been through so many of these calls that theyre more predictable than a Tyler Perry plot. They start with small talk about the weather or exciting weekend activities. I recite my 90-second mini bio, walk through my resumé bullets, and listen for red flags (see: vague roles, use of the word family that doesnt describe actual kinfolk). Just when it seems were wrapping up, the part I dread most shows up: the money question. Before you go, can you share your salary expectations? I love Boots on the Ground, but I hate this dance. Younger me used to get tripped up, blurting out a safe number thats low enough to stay in the running, but far below what I knew I deserved. It took me years to learn the jig: Some companies know exactly what theyre willing to pay but still ask you to guess, like youre appraising a speedboat on The Price Is Right. Go too low, you undercut yourself. Go too high, you risk a polite rejection email. After my most-recent two-step, I decided Im done playing that game. If a job posting doesnt advertise the positions salary range, I dont apply. Part of that confidence comes from a rules shift. Wage transparency laws are still new. California started in 2018 by banning invasive salary-history questions that often reinforced pay gaps. Colorado raised the stakes in 2021 by forcing employers to post pay ranges to protect applicants from lowballing. My native Washington State and a handful of others followed. Thats progressbut only if the job youre applying for is in one of those states. For me, these rules make local interviews less shady. But when pursuing remote gigs that lack these protections, Im taken right back to those early career missteps where I failed to keep my playing cards concealed. Even with pay transparency becoming the norm, some suspect companies list absurd ranges. They claim theyre considering a wide range of experience levels, but it looks hella sketchy to me. I saw one posting that claimed the salary could be anywhere between $60,000 and $180,000. Thank you for that incredibly helpful insight. Hard pass. Job hunting is a full-time hustle: scrolling boards; tweaking your résumé for the 12th time; rushing to apply before the avalanche of résumés roll in; surviving the gauntlet of Zoom, phone, and panel interviews. Thats hours of unpaid labor. The least an employer can do is tell me upfront what the hell a competitive salary is. If were keeping it real, employers that withhold this information while pressing candidates to reveal their expectations are keeping the deck stacked against marginalized applicants. For Black folks like myself, wage and information gaps can make under-asking more likely, which keeps the cycle going. This brings me back to that awkward moment with the hiring manager. Asked what I wanted to make, I answered honestly: I needed more details about the day-to-day before I could price my talents. The manager said the budget wasnt settled yet, and we moved on. I hung up proud that I didnt fold, but also convinced Im done vying for jobs that treat salary like a guessing game. If you want a mystery solved, call Scooby-Doo and the gang. I just came here to work.
Category:
E-Commerce
A decade ago, we argued about which app would win your attention. Now the fight is over who mediates it. AI assistants are becoming the front door to everything we do with computers: search, shopping, media, work. Whoever wins that interface will decide what you see first, which option you consider default, much of the narrative surrounding it, and how much of your life is routed through their business model. In that context, three strategies are colliding. Apple, following its instincts about privacy, is baking a privacy-forward layer called Apple Intelligence straight into the devices people already use, with on-device models and a new Private Cloud Compute for heavier requests. Amazon is pushing Alexa+, a paid, more agentic assistant that does real tasks end-to-end. And Meta is pitching a world of personal superintelligence, custom, high-context AIs tuned to you, not just to a platform. Each vision is coherent, each has a different set of incentives, and each points to a bifurcated future where a small group enjoys bespoke, high-performance own AIs while most people live inside a commercial, one-size-fits-most assistant. At WWDC25, Apple unveiled new Apple Intelligence features coming to iPhone, iPad, Mac, Apple Watch, and Apple Vision Pro. [Photo: Apple] Apples move is the most predictable and, in its way, the most radical: keep as much as possible on device, and when you must go to the cloud, do it inside a verifiable privacy envelope. Thats the promise behind Private Cloud Compute, which Apple describes as extending device-grade security into the data center; it underpins the new Siri and the system-wide writing, image, and notification tools announced at WWDC25. For developers, Apple also opened access to the on-device foundation model so apps can call into this layer without shipping your life to a third party. Its the Apple playbook applied to AI: integrate, de-risk, and turn trust into lock-in. Amazon is taking a different tack: make the assistant an agent that does multistep work, charge for it, and leverage the Prime bundle to drive adoption. Alexa+ is a re-architecture, not a skin: more natural language, third-party actions, and a subscription price of $19.99/month (free with Prime). Early access began in spring and rollouts are continuing to Echo devices with screens. The economics matter: paying for the assistant reframes it from a loss-leader to a product with its own P&L, and makes it easier to justify the heavy compute behind agentic tasks. [Photo: Amazon] Googles late push outlines whats at stake: Gemini for Home will start replacing Google Assistant on Nest speakers and displays, with free and paid tiers and a more conversational, context-aware experience. When the wake word changes less than the behavior, you know the platform is serious: this isnt a novelty feature, its a replacement strategy. Then theres Meta: Mark Zuckerberg has started using the phrase personal superintelligence, framing the goal less as an assistant that lives in an app and more as an AI that lives in your context, across devices, services, and knowledge. Its not hard to see the architecture: open-weights models (from Llama 3.1 onward) tuned with your corpus, running locally where possible and on rented compute when necessary. The spend signals the seriousnessand the desperation. Two Classes What does all of this mean for real people? In practice, two AI classes are forming: Class A is a minority with the time, money, or institutional support to build their own superpowered assistant: a personal knowledge base, continuous memory, custom tools, controlled privacy, and the ability to inject domain data (papers, contracts, code, archives) into every answer. The pieces already exist in the wild: open-weights models you can run yourself, Home Assistant integrations that give a local LLM real control over your environment, and maturing desktop stacks that make a DIY second brain less of a weekend hack and more of a workflow. Its still geeky, but much less exotic than a year ago. Class B is the majority, living with a default assistant from one of the big platformsbecause it ships with the phone, or its bundled with Prime, or it replaced the thing on your kitchen speaker. Those assistants will be competent, even delightful. But they will also be shaped by the platforms incentives: a shopping giant will optimize for commerce and partners; an ads company for discovery inside its ecosystem; a hardware company for brand and retention. Thats not a conspiracy; its the point of having an assistant at all. With ad-funded assistants from Meta or Google, the core incentive is still advertising: even as both companies insist they dont sell your data, the ad-tech plumbing runs on real-time bidding that broadcasts behavioral and device signals to a long tail of intermediaries during each auction, a practice regulators and researchers have criticized for pervasive data leakage. These assistants will keep turning your life into auctionable signals, and most people will accept the trade because convenience masks the true cost of lost privacy. The market sorting itself Notice the new paywalls around capability: Apple bakes intelligence into devices you already bought, Amazon sells a tier with heavier agentic skills, Google is teeing up free and paid versions of Gemini for Home. This is how the market sorts itself: casual users get a competent baseline, power users pay for more autonomy, and enterprises build their own stacks. In the middle, a long tail of enthusiasts is stitching together local models, personal data, and automations that feel like a bespoke service . . . because in a sense, it is. If the business model is ads, expect the assistant to optimize for monetizable moments, and for the data it gathers about you to keep feeding a market that regulators are still trying to corral, from cookie deprecation U-turns to ad-tech antitrust trials. As usual in ad-funded models, your attention (and the exhaust of your behavior) is the product on the shelf: They wont sell your data as a CSV, but theyll sell access to your profile, again and again, at auction speed. This split matters for power and for culture. If your assistant knows your files, your history, your tastes, and your constraints, it stops being a chat box and starts being a context engine. It will presort the world for you. That can be liberating: less friction, fewer tabs, more time. It can also be dangerously normative: a single vendors defaults become your defaults, not because you chose them, but because they came baked into the thing that listens when you talk. The more decisions we outsource to agents, the more we should care about whose agent it is. What to watch for What should we watch for next? Three simple questions cut through the marketing: Who pays? If you dont, youre the product. If you do, what does the meter measure: tokens, tasks, or time? Alexa+ makes that trade explicit, while Googles reversal on cookie deprecation shows how hard ad-funded ecosystems let go of the data firehose. Where does your context live? Apples pitch is verifiable privacy with PCC; Metas is open weights and portability; Googles is Gemini everywhere. The detailsnot the slogansdecide if your assistant remembers for you or about you. Can you take it with you? The early signs of a DIY tier, like Home Assistant, local LLMs, or second brain workflows, suggest a path where your assistant is an artifact you own, not a subscription you rent. Theres also the culture of default. Most people wont assemble a personal superintelligence: theyll use whatever came with the phone, the speaker, or the social app, and those assistants belong to companies whose revenues depend on profiling and targeting. Europes courts and regulators keep pushing back on that model, and in the U.S., the ad stack still sprays data through auctions at internet scale. The convenience is realso is the privacy bill. The majority will live inside assistants that treat privacy as collateral, not because theyre malicious, but because they underestimate what its worth. How to choose My bet: well end up with both worlds at once. A small but growing cohort will assemble their own personal superintelligence (RAG on steroids, tuned to their corpus and values), while most people will know no better than to blindly rely on the assistants that ship with their devices or their memberships. If you want the first path, start by owning your context: keep your notes, documents, and history in portable formats; experiment with local models; and avoid locking critical workflows into any one vendors UI. If you prefer the second (or you were not able to follow this article), at least be intentional about which platforms incentives youre adopting, because that assistant will optimize something. Make sure its optimizing for you.
Category:
E-Commerce
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