By the time 2025 ended, the illusion of stability was gone. Not because of one single collapse, but because nearly every system we interact with began behaving more honestly, if not more recklessly. Technology, finance, culture, and governance stopped pretending they were aligned with the public interest and started acting in ways that exposed their real incentives.
This was the year when the gap between how things are marketed and how they actually function became impossible to ignore.
Across every category Laterstack covers, the same pattern repeated. Speed over safety. Growth over trust. Automation over accountability. And a public that is slowly realizing it has been participating in systems it no longer understands or controls.
What follows is not a highlight reel. It is a map.
Technology Stopped Feeling Neutral
In 2025, technology finally lost its last claim to neutrality. AI tools moved from novelty to infrastructure. They quietly embedded themselves into hiring systems, creative pipelines, customer service, surveillance tools, and financial decision making.
The problem was not that AI existed. It was that it became untraceable. Companies stopped clearly disclosing when it was used. Awards bodies struggled to define what counted as acceptable use. Developers admitted that AI tools were already baked into workflows long before public conversations caught up.
The result was confusion and mistrust. Not because people rejected technology, but because they were no longer sure who was making decisions. When a system fails and no human is clearly responsible, accountability evaporates.
This year showed that convenience scales faster than ethics.
Cybersecurity Became Personal
Data breaches in 2025 were no longer abstract. They were intimate. Search histories. Viewing habits. Location data. Internal employee communications. Entire lives reduced to databases and then passed around as leverage.
What stood out was not just the volume of breaches, but the normalization of them. Companies issued statements. Regulators promised reviews. Users were advised to reset passwords and move on.
At the same time, governments expanded surveillance quietly. Employee monitoring increased. Border technologies became permanent. Drones, analytics platforms, and internal tracking tools moved from pilot programs into standard operations.
The line between protection and observation blurred. Many people did not notice it happening. That was the point.
Startups Learned Capital Has a Shorter Memory Than Hype
2025 was brutal for startups that required massive infrastructure, long timelines, or regulatory patience. Battery swapping. Autonomous logistics. Climate hardware. Ambitious platforms that once raised hundreds of millions quietly filed for bankruptcy.
The lesson was not that innovation failed. It was that venture capital rewards narrative far longer than viability. Once market conditions tightened and incentives shifted, many companies were left without a path forward.
Meanwhile, smaller and less visible startups thrived. Tools that solved narrow problems. Services that operated in legal gray areas. Platforms that scaled first and dealt with consequences later.
It became clear that the future belongs less to vision and more to adaptability.
Finance and Gambling Drifted Into the Same Space
Prediction markets, crypto casinos, and financialized gaming expanded rapidly in 2025. Often faster than regulators could respond. Often faster than users understood the risks.
These platforms did not advertise themselves as gambling. They framed participation as insight, forecasting, or strategy. But the mechanics were familiar. Risk was abstracted. Losses were individualized. Profits were centralized.
What made this year different was the confidence. Companies no longer acted like they were pushing boundaries. They acted like boundaries no longer mattered.
This was not deregulation. It was enforcement lag. And it created a new digital frontier where speed determined legitimacy.
Culture Fragmented, Then Hardened
Online culture in 2025 did not just fracture. It calcified. Algorithms rewarded outrage, certainty, and repetition. Nuance became expensive. Long form thinking felt foreign.
At the same time, distrust of institutions deepened. Media. Tech companies. Governments. Even creators. Every entity was assumed to have an agenda, usually financial.
Yet people still searched for meaning. That tension defined the year. A desire for clarity paired with systems designed to obscure it.
This is why subtlety matters now more than ever. People resist being told what to think. But they are still capable of noticing patterns when space is created for them to connect the dots themselves.
What This Meant for Everyday People
For most people, 2025 felt exhausting rather than explosive. Systems did not collapse overnight. They eroded quietly.
Jobs became more automated but less secure. Privacy became conditional. Entertainment blurred with monetization. Participation increasingly meant exposure.
The common thread was that choice remained, but clarity did not. And without clarity, consent becomes performative.
Recognizing that is the first step toward reclaiming agency.
Where Laterstack Fits Into 2026
Laterstack exists to slow the scroll. To connect stories that are usually siloed. To treat readers like adults who can hold competing ideas without needing a conclusion handed to them.
If 2025 showed us anything, it is that understanding the world now requires synthesis, not speed.
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Resolve AI, a startup building an autonomous site reliability engineer, has reached a headline valuation of $1 billion in a Series A led by Lightspeed Venture Partners, according to people familiar with the deal.
The funding marks one of the most aggressive bets yet on AI systems designed to maintain and repair software infrastructure without human intervention. Resolve AI’s product automatically identifies, diagnoses, and resolves production issues in real time, a role traditionally handled by highly trained site reliability engineers.
Sources said the round used a multi tranched structure that has become common among in demand AI startups. In this setup, part of the investment was priced at a $1 billion valuation, while the remainder was purchased at a lower price, resulting in a blended valuation below the headline figure.
Resolve AI’s annual recurring revenue is approximately $4 million, according to two people familiar with the company’s finances. The total size of the funding round was not disclosed. Resolve AI and Lightspeed declined to comment.
The company was founded less than two years ago by Spiros Xanthos, a former Splunk executive, and Mayank Agarwal, Splunk’s former chief architect for observability. The two met more than two decades ago while studying at the University of Illinois Urbana Champaign and previously co founded Omnition, which Splunk acquired in 2019.
Their new startup tackles a growing pain point across the tech industry. As software systems spread across increasingly complex cloud environments, companies struggle to hire enough experienced reliability engineers to keep systems running. Outages are costly, reputation damaging, and difficult to predict.
Resolve AI’s pitch is simple but ambitious. Replace reactive human troubleshooting with automated systems that can detect problems early and fix them instantly. By reducing downtime and cutting operational overhead, companies can shift engineering talent away from constant firefighting and toward building new products.
The startup raised a $35 million seed round last October led by Greylock, with participation from World Labs founder Fei Fei Li and Google DeepMind scientist Jeff Dean. It now competes with Traversal, another AI powered SRE startup that raised a $48 million Series A led by Kleiner Perkins with backing from Sequoia.
Investor appetite suggests that autonomous infrastructure management is becoming a core layer of the modern software stack, not a niche tool.
What This Means for Everyday People
For everyday users, autonomous reliability tools could quietly change how digital services behave. Fewer outages, faster fixes, and more stable apps mean less frustration when banking apps crash, healthcare portals fail, or workplace software freezes mid task. At the same time, these systems reduce the need for human oversight, raising questions about accountability when automated tools control the backbone of daily digital life.
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Cursor Buys Graphite as AI Coding Arms Race Accelerates
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Cursor, the fast growing AI coding assistant company valued at $29 billion, has acquired Graphite, a startup focused on AI powered code review and debugging. The deal continues Cursor’s aggressive acquisition strategy as competition intensifies in developer tooling.
Financial terms were not disclosed, but Axios reported that Cursor paid significantly more than Graphite’s last valuation of $290 million. That figure was set earlier this year when the five year old startup raised a $52 million Series B.
The acquisition reflects a growing reality inside engineering teams. Code generated by AI often ships with errors, forcing developers to spend hours reviewing and fixing what machines produce. While Cursor already offers AI powered code review through its Bugbot product, Graphite brings specialized tooling designed for modern development workflows.
One of Graphite’s standout features is its stacked pull request system. This allows developers to work on multiple dependent changes at the same time without waiting for approvals, reducing bottlenecks and speeding up deployment cycles.
By combining AI code generation with AI driven review and debugging, Cursor is positioning itself as a full stack platform that takes engineers from first draft to production faster than traditional tools.
The deal also highlights how crowded the AI code review space has become. Competitors include CodeRabbit, which reached a $550 million valuation in September, and Greptile, a smaller player that raised a $25 million Series A this fall.
Cursor CEO and co founder Michael Truell has longstanding ties to Graphite’s founding team. He first met co founders Merrill Lutsky, Greg Foster, and Tomas Reimers through Neo Scholar, a selective program run by Ali Partovi’s venture firm Neo. Neo backed Graphite at the seed stage.
Both companies also share major investors, including Accel and Andreessen Horowitz, reinforcing how tightly connected the AI developer ecosystem has become.
This acquisition follows a series of recent moves by Cursor. In November, the company bought Growth by Design, a firm focused on technical recruiting strategy. Earlier this year, it absorbed talent from AI powered CRM startup Koala in a deal that valued Koala at $129 million, according to PitchBook.
Together, these deals suggest Cursor is not just building a product, but assembling an end to end system around how software gets built, reviewed, staffed, and shipped.
What This Means for Everyday People
For everyday users, this shift matters even if they never write a line of code. As AI tools increasingly build the software behind banking apps, healthcare portals, and workplace systems, the pressure to ship faster can clash with the need for reliability. Consolidation like this aims to reduce errors, but it also concentrates power over how digital infrastructure is created. The tools shaping the modern internet are being decided by fewer companies, faster timelines, and higher stakes.
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Celeste Amadon and Asher Allen did not set out to rebuild dating. They were originally working on a tool that used artificial intelligence to book restaurants for dates. Somewhere along the way, they noticed something more revealing than availability calendars or cuisine preferences. People wanted to talk, and when they did, they revealed far more about themselves than any profile ever could.
That observation became Known, a San Francisco based dating startup built around voice AI instead of swipes. The app replaces forms and bios with long, open-ended conversations that feel closer to an interview than onboarding. Early users spend an average of twenty six minutes talking to the system. One user stayed engaged for over an hour and a half.
For Amadon, that time spent talking is the product. She believes it allows the platform to understand users well enough to suggest dates that actually make sense, while cutting down on rejection, endless chatting, and ghosting.
Known’s early testing appears to support that theory. In its San Francisco beta, the company says eighty percent of introductions resulted in in-person dates. That performance helped the startup raise $9.7 million from investors including Forerunner, NFX, Pear VC, and Coelius Capital. It also marked Forerunner’s first investment in a dating app.
Forerunner partner Eurie Kim says the appeal lies in Known’s understanding of a demographic that dating apps often miss. She describes Amadon as deeply attuned to the unspoken needs of young women, many of which never make it into profiles but emerge naturally in conversation. In the past, extracting that kind of nuance required an expensive human matchmaker. Known is attempting to do it with software.
The app’s flow is deliberately constrained. After onboarding, users receive suggested matches and can ask AI agents questions about those profiles. If both parties express interest, they have twenty four hours to accept the introduction and another twenty four hours to commit to a date. The goal is momentum, not endless messaging.
Known still incorporates its original restaurant concept. The app suggests venues based on user preferences and integrates with calendars to help coordinate schedules. During beta, the company charged thirty dollars per successful date, though Amadon says pricing is still experimental.
Behind the scenes, Known remains a small operation. The team includes three full-time engineers, a go-to-market group, and several contractors. Both founders dropped out of Stanford to build the company. With new funding, they plan to expand cautiously.
The timing is not accidental. Amadon openly frames Known as a response to what many researchers describe as a loneliness crisis, especially among younger adults. Dating apps promised connection but often delivered something closer to gamified isolation. Known positions itself as a correction rather than an upgrade.
Competition is heating up. New dating startups are leaning on AI to mimic bespoke matchmaking services that once cost thousands of dollars. Established players like Tinder, Bumble, and Hinge are also rolling out AI features. Amadon welcomes it. She sees competition as confirmation that swipe-based dating has reached its limits.
Known is currently testing in San Francisco and plans a broader launch early next year.
What This Means for Everyday People
Known reflects a broader shift in how technology is being reconsidered. For years, efficiency meant faster, shallower interactions. This startup is betting that slowing users down and asking them to speak instead of perform may lead to better outcomes. For everyday people, it suggests a future where technology does not just optimize behavior, but encourages presence, commitment, and real-world connection.
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In the scrubby desert of western Nevada, nothing on the surface suggests the energy lying beneath. Utah-based startup Zanskar Geothermal & Minerals recently announced it discovered a 250-degree Fahrenheit geothermal reservoir using artificial intelligence. The company named the site Big Blind, reflecting its status as a “blind” system, which shows no surface signs of geothermal activity and has not been explored before.
Big Blind is the first blind site discovered by the industry in over 30 years. Carl Hoiland, co-founder and CEO of Zanskar, explained that the common belief that geothermal resources are depleted is inaccurate. He expects many more hidden sites across the western United States. Geothermal energy is a near-limitless and constant source of power. Unlike solar or wind, it does not depend on the weather and produces almost no carbon emissions. The challenge has been finding and scaling these underground reservoirs.
Geothermal systems require specific underground conditions, including porous rocks and reservoirs of hot water or steam that can be brought to the surface to generate power. In the 1970s and 1980s, oil and gas companies invested heavily in drilling for these systems. By the mid-1980s, many abandoned the effort due to high costs and low success rates.
Joel Edwards, co-founder and CTO of Zanskar, describes the search for these systems as a classic needle-in-a-haystack problem. There is no single indicator for a blind geothermal system. The startup’s AI models analyze decades of data from known sites and integrate information such as rock composition, heat flow, and magnetic fields to identify potential reservoirs. Drilling over the summer confirmed that Big Blind contains hot, porous rock suitable for commercial energy production.
Zanskar estimates that electricity from Big Blind could be generated within three to five years, pending permitting and grid connection. Experts see the discovery as significant. James Faulds, professor of geosciences at the Nevada Bureau of Mines and Geology, points out that three-quarters of US geothermal resources are blind, suggesting a vast untapped potential in the western states alone.
Government studies from 2008 estimated undiscovered geothermal resources in the United States could provide around 30 gigawatts of power. Zanskar believes the real figure could be at least ten times higher. Over the past three years, the startup has identified numerous additional hotspots with similar characteristics to Big Blind.
Beyond its energy potential, geothermal is gaining political support. Unlike solar and wind, it has largely avoided regulatory scrutiny and is expected to grow rapidly. Zanskar’s approach uses conventional geothermal techniques combined with modern data analysis to uncover sites that have been hidden for decades.
This discovery is part of what some experts are calling a geothermal renaissance. While next-generation geothermal methods, including enhanced drilling techniques, are gaining attention, Zanskar demonstrates that traditional methods still have untapped potential. Success at Big Blind may serve as a model for the industry and encourage further investment.
For everyday people, these developments mean that more of the energy powering homes, businesses, and even data centers could come from a clean, constant, and domestic source. Geothermal energy could reduce reliance on fossil fuels, stabilize electricity costs, and contribute to climate goals without major lifestyle changes.
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GRAND JUNCTION, CO – Sunday Robotics, a robotics startup founded in April 2024, has unveiled Memo, an autonomous home robot capable of completing everyday household tasks like clearing the dinner table, loading the dishwasher, and even operating an espresso machine.
Unlike most robotics startups that rely on teleoperation or synthetic data simulations, Sunday Robotics developed a proprietary glove system to train Memo. Human operators wear gloves that mimic the robot’s Lego-like hands to perform tasks, generating real-world data for Memo to learn from.
Efficient and Cost-Effective Training
CEO Tony Zhao explained that the glove-based method provides “two orders of magnitude higher capital efficiency compared to teleoperation ($200 vs $20,000).” The approach allows for large-scale data collection without physically moving Memo, making the training process highly scalable. Currently, the startup employs more than 500 human data collectors across the United States to improve Memo’s dexterity and AI performance.
Breaking New Ground in Dexterity
Training robots to handle delicate objects has been a longstanding challenge in the robotics field. During live demonstrations, Memo successfully lifted fragile wine glasses, folded socks, and loaded the espresso machine without breaking any items. Zhao highlighted that replicating human hand capabilities, which involve thousands of touch receptors, is a major engineering feat.
“Today, we present a step-change in robotic AI,” Zhao said. “Memo demonstrates that autonomous home robots can perform complex tasks efficiently and safely.”
Alternative to Traditional Training Methods
Most robotics companies train AI through teleoperation, where humans control robots via joysticks, or by using simulated data. Sunday Robotics’ glove system bypasses these methods, providing more accurate and context-rich training data.
Michael Cheng, co-founder of Sunday Robotics, noted that “relying solely on teleoperation could take decades to gather sufficient training data for a robot like Memo.” By contrast, the glove method allows for widespread, distributed training.
Looking Ahead
The startup plans to continue refining Memo’s abilities, aiming to expand its applications in home automation. With breakthroughs in dexterity and autonomous AI learning, Sunday Robotics positions itself as a leader in the next generation of consumer robotics.
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solar startup Africa is quietly building the future the rest of the world keeps talking about
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While the world argues about AI, another transformation is unfolding far from the usual tech centers. solar startup Africa is expanding rapidly as local founders build energy systems designed for people who have spent decades waiting for reliable power. This shift is not theory. It is visible in homes, clinics, markets, and schools that once went dark every evening.
The growth of solar startup Africa comes from simple forces. The cost of panels has dropped. Mobile payments work everywhere. Community trust accelerates adoption. Most importantly, the builders grew up with the problems they are solving. They remember outages that lasted days. They know what a broken grid means for a small shop or a rural clinic.
A ground level Solarpunk reality
This is Solarpunk without the aesthetic filter. It is not a design trend or a social media fantasy. It is real infrastructure for communities that cannot wait for national grids to catch up.
Shops can stay open at night. Clinics can preserve vaccines. Farmers can cool crops instead of watching them spoil. Students can study without burning candles. And people can work remotely because their power no longer cuts out every afternoon.
solar startup Africa creates opportunity by making electricity stable. That one change shifts everything else.
Why this acceleration is happening now
A few key shifts pushed this movement forward.
Affordable solar panels made small scale grids possible.
Mobile money created a simple pay as you go system for families and businesses.
Local founders understood exactly where the grid fails and why diesel generators drain income.
Communities saw results quickly, which gave the model legitimacy.
The result is a wave of village scale and neighborhood scale solar grids that operate independently from national systems that move far slower.
The next phase of energy growth
The future of solar startup Africa is already visible in early projects.
Smart community grids allow operators to track usage remotely.
Solar bundles pair panels with fridges, pumps, or tools to raise income.
Energy payments evolve into financial identities that help residents access credit.
Local assembly plants begin to keep more of the value inside the region.
AI supports maintenance and prediction quietly in the background instead of becoming the primary story.
These developments point toward a world where reliable power is not something communities hope for. It is something they build and own.
Why the rest of the world should pay attention
The rise of solar startup Africa offers a model for regions with weak infrastructure. It demonstrates that large grids are not the only path to progress. It shows that communities can build upward using energy systems designed for their own needs rather than imported assumptions.
This is progress without permission. It is change without waiting for someone else to deliver it.
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The cybersecurity world just got another wake-up call. Deepwatch, a security company known for its AI-powered detection and response platform, has laid off roughly 80 employees out of its 250-person team. The company says the move is about “accelerating investments in AI and automation.”
For many in the field, it’s a sign of a bigger shift happening across the cybersecurity industry — one where traditional analyst-heavy teams are giving way to AI-driven platforms that promise faster detection and fewer human bottlenecks.
The story behind the layoffs
In an email to TechCrunch, Deepwatch CEO John DiLullo explained that the company is “aligning our organization to accelerate our significant investments in AI and automation.”
Employees inside the company painted a more complicated picture. One current staffer told TechCrunch that the layoffs “feel like an AI reshuffle more than a clear product plan.” The move comes as Deepwatch develops new “agentic AI” features that supposedly make its security platform more autonomous though insiders say details are still unclear.
Eight former employees have already announced their departures on LinkedIn. The total reduction, between 60 and 80 workers, represents a major downsizing for a company that had been growing steadily over the last few years.
A broader cybersecurity reset
Deepwatch isn’t alone. 2025 has seen a string of cybersecurity layoffs, even as demand for threat defense keeps rising.
Earlier this year, CrowdStrike cut about 5% of its workforce, despite posting record revenue and cash flow. Other firms, including Deep Instinct, Otorio, ActiveFence, SkyBox Security, and Sophos, have also reduced headcount.
At first glance, that might sound like an industry contraction. But the deeper story is a realignment. Security platforms are increasingly being rebuilt with AI automation at the core, aiming to do what used to require entire SOC teams.
The pitch is simple: AI models can flag anomalies faster, handle repetitive triage work, and even automate response playbooks. That means fewer humans, more software, and a new kind of efficiency that’s starting to define the next phase of cybersecurity.
What it means for the future of cybersecurity work
The shift comes with big questions for cybersecurity professionals. What happens when AI becomes the analyst?
For now, most companies still rely on human oversight, but the ratio is changing. Fewer people are being hired to monitor dashboards. More are being asked to train AI models, review outputs, and handle only the complex or ambiguous cases.
In the near future, the cybersecurity job market may look less like traditional operations centers and more like data science and model management teams. The people who stay will be those who understand both domains: security and machine learning.
This mirrors what’s happening in other tech sectors, where AI adoption doesn’t eliminate roles immediately it reshapes them.
The bigger picture
If this sounds familiar, it’s because this same pattern has played out before, when cloud automation replaced on-prem infrastructure jobs, and when DevOps merged development and operations.
The cybersecurity industry is now undergoing its own version of that shift. Deepwatch’s layoffs aren’t an isolated event; they’re a preview of what happens when security meets automation at scale.
Over the next few years, expect more platforms that market themselves as “AI-native,” fewer manual SOC workflows, and a growing divide between traditional analysts and the next generation of cyber-AI engineers.
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Quantum computing has quietly stepped up again. IBM followed through on promises made back in June, debuting two new quantum processors that mark a leap toward error-corrected systems. Meanwhile, IonQ and Quantum Art made their own announcements that signal how quickly quantum innovation is moving from the lab into something that looks more like the next computing industry.
IBM’s new Quantum Loon and Nighthawk chips
IBM confirmed the large-scale manufacturing of its Quantum Loon processor, the company’s new architecture for logical qubits. Loon represents a major shift in IBM’s hardware strategy, moving to a square grid layout that connects each qubit to four neighbors instead of the older hex pattern. This setup allows for faster operations and a smoother path toward error-corrected quantum computing.
The second chip, Nighthawk, uses the same grid design but without Loon’s long-distance connections. It’s focused on improving error rates so researchers can begin testing algorithms for quantum advantage, cases where quantum systems outperform classical ones.
IBM also launched a public GitHub repository that lets researchers share performance data across classical and quantum algorithms. It’s part of a broader effort to track where quantum hardware actually provides a measurable edge.
IonQ’s record-breaking error rate
While IBM was refining architecture, IonQ made headlines with a new record-low error rate for two-qubit gates achieving over 99.99 percent fidelity. That number may sound small, but in quantum computing it’s enormous. Each percentage point gained means fewer hardware qubits are needed to stabilize a system, which brings practical quantum hardware closer to reality.
IonQ’s method builds on technology acquired from Oxford Ionics, which used electromagnetic fields instead of lasers to handle qubit operations. The breakthrough reduces the time required for cooling ions between operations, allowing the entire machine to run faster while maintaining stability.
Quantum Art and Nvidia team up
The final announcement came from Quantum Art, which revealed a partnership with Nvidia to create a more efficient compiler for its trapped-ion systems. While Nvidia isn’t directly building quantum chips, it’s using GPUs to model, optimize, and support the computations quantum hardware needs.
Quantum Art’s design is unusual instead of performing operations on one or two qubits at a time, its system groups large clusters of ions into what it calls quantum cores. Each core operates on many qubits at once, using laser “pins” to isolate and move groups efficiently. The result could be multicore quantum computing, a concept that mirrors classical chip architecture.
FULL ARTICLE HERE
This move also reflects a growing trend, traditional computing giants like Nvidia are embedding themselves deeper into the quantum ecosystem, preparing for a hybrid future where quantum and GPU systems coexist.
The next stage of the race
Taken together, these updates show that 2025 is shaping up to be a year of practical momentum for quantum hardware. IBM is building the foundation for scalable, error-corrected machines. IonQ is proving the physics can handle real-world precision. And Quantum Art is testing entirely new models for how quantum cores might work in the future.
If these companies keep their current pace, the next few years might finally deliver what the field has promised for decades: quantum computing that actually does something classical machines cannot.
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– John Timmer Senior Science Editor –
In one of the more bizarre startup moments of the year, Inference CEO Sam Hogan accidentally gave his company’s newest project the same name as a famous OnlyFans creator and Substack researcher.
The initiative was called Project AELLA, part of an open science push designed to make research more accessible using structured summaries generated by large language models. Within hours of the launch, Hogan learned that “Aella” wasn’t just a cool Greek name, it was also the online identity of a high-profile figure in tech and adult content circles.
The mixup that went viral
After the announcement, Aella herself noticed and responded with a simple post: “Lmfao.”
Hogan quickly replied that he “didn’t know who you were until today,” before changing the project’s name to Project OSSAS. The exchange went viral on X, drawing attention from investors, tech founders, and fans of Aella’s work alike.
Aella, who once ranked in the top 0.04 percent of OnlyFans creators, now focuses most of her time on data-driven research about relationships and behavior through her Substack, Knowingless. Her work has gained recognition from tech figures such as Marc Andreessen, who publicly called one of her theories “fantastic.”
When brand naming meets the internet
The situation highlighted how fragile modern branding can be in an era where every name has an online footprint. A single Google search oversight turned into a viral moment connecting the worlds of adult content, tech startups, and venture capital.
To Hogan’s credit, he handled it with humor and transparency. After learning about Aella’s reputation, he even proposed potential collaboration, suggesting data visualizations for her research. Aella responded enthusiastically.
The two exchanged messages that ended on a friendly note and a possible idea for a joint project.
Inference official website
Aella’s Substack “Knowingless”
What it means for founders
The incident became an instant case study in startup brand due diligence. In a world where personal brands often overlap with company names, founders are realizing they need to check not just trademark databases but also social media ecosystems.
It also revealed how online culture now blends effortlessly with venture-backed innovation. Aella and Hogan, despite their very different backgrounds, share a fascination with human data and digital systems.
Hogan’s startup, Inference, recently closed an 11.8 million dollar seed round led by Multicoin Capital and Andreessen Horowitz, describing itself as “the world’s largest GPU cluster for model inference.”
What started as an embarrassing name mix-up could end as a collaboration between two of the most unexpected voices in tech.
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