Agent-based payment protocols like Google's AP2 and Stripe's ACP improve transaction efficiency with verifiable mandates and streamlined authorization.
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Beijing has become the first provincial-level region China with mandatory AI classes, while Open Campus secures $5M to enhance blockchain education.
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The Bitcoin price is positioning for a potentially explosive move that could take it well beyond its previous all-time highs. Analysts are closely watching a critical resistance level near $116,000, which may serve as the final hurdle before BTC catapults into uncharted territory above $126,000.
Crypto analyst Donny Dicey revealed in an X social media post this week that the $116,000 price level is the decisive zone Bitcoin must breach to confirm a breakout toward a new all-time high. His technical analysis suggests that once BTC achieves a clean break above this resistance area, momentum could swiftly carry it above $126,000.
Notably, Bitcoin set a new ATH on October 6, 2025, after breaking through its previous record above $124,000 and climbing past $126,000. Since achieving this level, the price of BTC has fallen dramatically to $115,000. Dicey’s accompanying chart shows the market steadily recovering after testing support near $108,000, marked as a “market structure break” region, with bullish price action consolidating above $109,000.
The analyst has emphasized that each day Bitcoin maintains a close above $109,000 strengthens the probability of a strong upward swing as the market heads into November. This period coincides with the Federal Open Market Committee’s (FOMC) next meeting, where investors are anticipating dovish signals such as rate cuts or the formal end of Quantitative Tightening (QT).
Dicey also notes that bullish S&P 500 earnings, easing global trade tensions from a potential agreement between US President Donald Trump and China’s President Xi Jinping, and improving ISM manufacturing data point to a macro environment supportive of risk assets. A community member commented that whales may have underestimated how much BTC’s demand tends to persist during these conditions. Dicey responded that the same whales might become “exit liquidity” as Bitcoin accelerates higher, possibly missing out on the strongest phase of this cycle.
In a follow-up analysis, Dicey highlighted Bitcoin’s remarkable stability above its January highs, describing its price structure as “unbreakable” amid global macroeconomic uncertainty. He pointed to several converging factors that reinforce BTC’s resilience, including ongoing fiscal and monetary expansion, a weakening US dollar, and renewed confidence in the global business cycle.
The analyst also emphasized that geopolitical tensions tied to US-China relations appear to be subsiding. At the same time, ETF inflows and exponential growth in the Artificial Intelligence (AI) sector contribute to acting as tailwinds for digital assets. He disclosed that despite strong underlying fundamentals, skepticism remains widespread in the market.
According to him, many still believe in the traditional four-year cycle narrative, while retail enthusiasm has not fully returned. Furthermore, the Russell 2000 index has yet to breakout, and rotation from traditional assets, such as the S&P 500 and gold, into Bitcoin remains limited. With these developments subduing broader market participation, Dicey suggests it creates the perfect setup for a powerful rally in BTC once sentiment shifts decisively.
Amazon is preparing for one of its biggest corporate shake-ups in years. The tech giant is planning to cut as many as 30,000 corporate jobs starting Tuesday, according to three people familiar with the matter who spoke with Reuters. The […]
The post Amazon to lay off 30,000 corporate employees in largest job cut since 2022, reports first appeared on Tech Startups.
Amazon is betting big on automation. Internal documents reviewed by The New York Times reveal plans to automate 75% of its warehouse operations using advanced robotics — a move that could eliminate the need for more than 600,000 new hires […]
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IBM is doubling down on blockchain. The tech giant announced on Monday the launch of Digital Asset Haven, a new platform built to help banks, governments, and corporations securely build and scale their digital asset operations. It’s a move that […]
The post IBM launches Digital Asset Haven, a new blockchain platform for financial institutions and governments first appeared on Tech Startups.
Nvidia is quietly gearing up for a new frontier—autonomous mobility. According to a report from Chinese publication 36Kr, the chipmaker is developing an internal robotaxi project that could put it in direct competition with Tesla and Waymo. The plan, shared in […]
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Saudi state-owned AI startup Humain is building a full-stack AI ecosystem — from data centers and cloud infrastructure to advanced AI models and a new voice-controlled operating system called “Humain 1.” Saudi Arabia’s ambitious push into artificial intelligence just took […]
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The post Pro-AI Super PAC Aligns with White House on Federal Framework, Downplaying Reported Rift appeared first on StartupHub.ai.
The notion of a deep rift between Washington’s political establishment and the burgeoning pro-AI lobby may be more perception than reality, according to recent insights. Far from a contentious divide, a significant alignment appears to be forming between a powerful new pro-AI Super PAC and the White House, both recognizing the urgent need for a […]
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“You will see a 30 to 50% correction in many AI-related names next year,” stated Dan Niles, founder and portfolio manager at Niles Investment Management, during a recent appearance on CNBC’s ‘Money Movers’. Niles joined the broadcast to discuss his outlook on Big Tech earnings and the current market sentiment surrounding technology stocks, particularly those […]
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Artificial intelligence is not merely a technological advancement; it is fundamentally reshaping human experiences, particularly in industries like travel. This was a central theme as Jane Sun, CEO of Trip.com Group, engaged in a revealing dialogue with Bloomberg’s Anders Melin at the 2025 Bloomberg Business Summit Asean in Kuala Lumpur. The discussion offered a profound […]
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Anthropic's India expansion marks a shift from global scale to sovereign-aware AI, building trust, data residency, and deeper enterprise integration.
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The launch of a $100 million pro-AI super PAC, “Leading the Future,” marks a pivotal moment for artificial intelligence, signaling its emphatic entry into the high-stakes arena of American political influence. This substantial war chest, intended to support “AI-friendly” candidates across the political spectrum, has reportedly “irked” the White House, immediately setting a tone of […]
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Correctly calling a market peak is a notoriously tricky endeavor.
Case in point: When tech stocks and startup funding hit their last cyclical peak four years ago, few knew it was the optimal time to cease new deals and cash in liquidatable holdings.
This time around, quite a few market watchers are wondering if the tech stock and AI boom has reached bubble territory. And, as we explored in Friday’s column, there are plenty of similarities between current conditions and the 2021 peak.
Even so, by other measures we’re also in starkly different territory. The current boom is far more concentrated in AI and a handful of hot companies. The exit environment is also much quieter. And of course, the macro conditions don’t resemble 2021, which had the combined economic effects of the COVID pandemic and historically low interest rates.
Below, we look at four of the top reasons why this time is different.
Four years ago, funding to most venture-backed sectors was sharply on the rise. That’s not the case this time around. While AI megarounds accumulate, funding to startups in myriad other sectors continues to languish.
Biotech is on track to capture the smallest percentage of U.S. venture investment on record this year. Cleantech investment looks poised to hit a multiyear low. And consumer products startups also remain out of vogue, alongside quite a few other sectors that once attracted big venture checks.
The emergence of AI haves and non-AI have-nots means that if we do see a correction, it could be limited in scope. Sectors that haven’t seen a boom by definition won’t see a post-boom crash. (Though further declines are possible.)
The new offering market was on fire in 2020 and 2021, with traditional IPOs, direct listings and SPAC mergers all flooding exchanges with new ticker symbols to track.
In recent quarters, by contrast, the IPO market has been alive, but not especially lively. We’ve seen a few large offerings, with CoreWeave, Figma and Circle among the standouts.
But overall, numbers are way down.
In 2021, there were hundreds of U.S. seed or venture-backed companies that debuted on NYSE or Nasdaq, per Crunchbase data. This year, there have been less than 50.
Meanwhile, the most prominent unicorns of the AI era, like OpenAI and Anthropic, remain private companies with no buzz about an imminent IPO. As such, they don’t see the day-to-day fluctuations typical of public companies. Any drop in valuation, if it happens, could play out slowly and quietly.
That brings us to our next point: In addition to spreading their largesse across fewer sectors, startup investors are also backing fewer companies.
This year, the percentage of startup funding going to megarounds of $100 million or more reached an all-time high in the U.S. and came close to a record global level. A single deal, OpenAI’s $40 billion March financing, accounted for roughly a quarter of U.S. megaround funding.
At the same time, fewer startup financings are getting done. This past quarter, for instance, reported deal count hit the lowest level in years, even as investment rose.
The last peak occurred amid an unusual financial backdrop, with economies beginning to emerge from the depths of the COVID pandemic and ultra-low interest rates contributing to investors shouldering more risk in pursuit of returns.
This time around, the macro environment is in a far different place, with “a “low fire, low hire” U.S. job market, AI disrupting or poised to disrupt a wide array of industries and occupations, a weaker dollar and a long list of other unusual drivers.
What both periods share in common, however, is the inexorable climb of big tech valuations, which brings us to our final thought.
While the argument that this time it’s different is a familiar one, the usual plot lines do tend to repeat themselves. Valuations overshoot, and they come down. And then the cycle repeats.
We may not have reached the top of the current cycle. But it’s certainly looking a lot closer to peak than trough.
Illustration: Dom Guzman
Nigeria is targeting 277 gigawatts of power by 2060, which will cost over $400 billion; in the short term, it has signed renewable energy deals worth $435M.
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Dubai leads the UAE's AI adoption with new initiatives, while Wikipedia experiences a traffic drop due to rising AI chatbot usage.
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SAN FRANCISCO — Putting on Amazon’s new smart delivery glasses felt surprisingly natural from the start. Despite their high-tech components and slightly bulky design, they were immediately comfortable and barely heavier than my normal glasses.
Then a few lines of monochrome green text and a square target popped up in the right-hand lens — reminding me that these were not my regular frames.
Occupying just a portion of my total field of view, the text showed an address and a sorting code: “YLO 339.” As I learned, “YLO” represented the yellow tote bag where the package would normally be found, and “339” was a special code on the package label.
My task: find the package with that code. Or more precisely, let the glasses find them.
As soon as I looked at the correct package label, the glasses recognized the code and scanned the label automatically. A checkmark appeared on a list of packages in the glasses.
Then an audio alert played from the glasses: “Dog on property.”
When all the packages were scanned, the tiny green display immediately switched to wayfinding mode. A simple map appeared, showing my location as a dot, and the delivery destination marked with pins. In this simulation, there were two pins, indicating two stops.
After putting the package on the doorstep, it was time for proof of delivery. Instead of reaching for a phone, I looked at the package on the doorstep and pressed a button once on the small controller unit —the “compute puck” — on my harness. The glasses captured a photo.
With that, my simulated delivery was done, without ever touching a handheld device.
In my very limited experience, the biggest concern I had was the potential to be distracted — focusing my attention on the text in front of my eyes rather than the world around me. I understand now why the display automatically turns off when a van is in motion.
But when I mentioned that concern to the Amazon leaders guiding me through the demo, they pointed out that the alternative is looking down at a device. With the glasses, your gaze is up and largely unobstructed, theoretically making it much easier to notice possible hazards.
Beyond the fact that they’re not intended for public release, that simplicity is a key difference between Amazon’s utilitarian design and other augmented reality devices — such as Meta Ray-Bans, Apple Vision Pro, and Magic Leap — which aim to more fully enhance or overlay the user’s environment.
KC Pangan, who delivers Amazon packages in San Francisco and was featured in Amazon’s demo video, said wearing the glasses has become so natural that he barely notices them.
Pangan has been part of an Amazon study for the past two months. On the rare occasions when he switches back to the old handheld device, he finds himself thinking, “Oh, this thing again.”
“The best thing about them is being hands-free,” Pangan said in a conversation on the sidelines of the Amazon Delivering the Future event, where the glasses were unveiled last week.
Without needing to look down at a handheld device, he can keep his eyes up and stay alert for potential hazards. With another hand free, he can maintain the all-important three points of contact when climbing in or out of a vehicle, and more easily carry packages and open gates.
The glasses, he said, “do practically everything for me” — taking photos, helping him know where to walk, and showing his location relative to his van.
While Amazon emphasizes safety and driver experience as the primary goals, early tests hint at efficiency gains, as well. In initial tests, Amazon has seen up to 30 minutes of time savings per shift, although execs cautioned that the results are preliminary and could change with wider testing.
Regulators, legislators and employees have raised red flags over new technology pushing Amazon fulfillment and delivery workers to the limits of human capacity and safety. Amazon disputes this premise, and calls the new glasses part of a larger effort to use technology to improve safety.
Using the glasses will be fully optional for both its Delivery Service Partners (DSPs) and their drivers, even when they’re fully rolled out, according to the company. The system also includes privacy features, such as a hardware button that allows drivers to turn off all sensors.
For those who use them, the company says it plans to provide the devices at no cost.
Despite the way it may look to the public, Amazon doesn’t directly employ the drivers who deliver its packages in Amazon-branded vans and uniforms. Instead, it contracts with DSPs, ostensibly independent companies that hire drivers and manage package deliveries from inside Amazon facilities.
This arrangement has periodically sparked friction, and even lawsuits, as questions have come up over DSP autonomy and accountability.
With the introduction of smart glasses and other tech initiatives, including a soon-to-be-expanded training program, Amazon is deepening its involvement with DSPs and their drivers — potentially raising more questions about who truly controls the delivery workforce.
The smart glasses, still in their prototype phase, trace their origins to a brainstorming session about five years ago, said Beryl Tomay, Amazon’s vice president of transportation.
Each year, the team brainstorms big ideas for the company’s delivery system. During one of those sessions, a question emerged: What if drivers didn’t have to interact with any technology at all?
“The moonshot idea we came up with was, what if there was no technology that the driver had to interact with — and they could just follow the physical process of delivering a package from the van to the doorstep?” Tomay said in an interview. “How do we make that happen so they don’t have to use a phone or any kind of tech that they have to fiddle with?”
That question led the team to experiment with different approaches before settling on glasses. It seemed kind of crazy at first, Tomay said, but they soon realized the potential to improve safety and the driver experience. Early trials with delivery drivers confirmed the theory.
“The hands-free aspect of it was just kind of magical,” she said, summing up the reaction from early users.
The project has already been tested with hundreds of delivery drivers across more than a dozen DSPs. Amazon plans to expand those trials in the coming months, with a larger test scheduled for November. The goal is to collect more feedback before deciding when the technology will be ready for wider deployment.
Typically, Amazon would have kept a new hardware project secret until later in its development. But Reuters reported on the existence of the project nearly a year ago. (The glasses were reportedly code-named “Amelia,” but they were announced without a name.) And this way, Amazon can get more delivery partners involved, get input, and make improvements.
Future versions may also expand the system’s capabilities, using sensors and data to automatically recognize potential hazards such as uneven walkways.
Amazon’s smart glasses are part of a system that also includes a small wearable computer and a battery, integrated with Amazon’s delivery software and vehicle systems.
The lenses are photochromatic, darkening automatically in bright sunlight, and can be fitted with prescription inserts. Two cameras — one centered, one on the left — support functions such as package scanning and photo capture for proof of delivery.
A built-in flashlight switches on automatically in dim conditions, while onboard sensors help the system orient to the driver’s movement and surroundings.
The glasses connect by a magnetic wire to a small controller unit, or “compute puck,” worn on the chest of a heat-resistant harness. The controller houses the device’s AI models, manages the visual display, and handles functions such as taking a delivery photo. It also includes a dedicated emergency button that connects drivers directly to Amazon’s emergency support systems.
On the opposite side of the chest, a swappable battery keeps the system balanced and running for a full route. Both components are designed for all-day comfort — the result, Tomay said, of extensive testing with drivers to ensure that wearing the gear feels natural when they’re moving around.
Connectivity runs through the driver’s official Amazon delivery phone via Bluetooth, and through the vehicle itself using a platform called “Fleet Edge” — a network of sensors and onboard computing modules that link the van’s status to the glasses.
This connection allows the glasses to know precisely when to activate, when to shut down, and when to sync data. When a van is put in park, the display automatically activates, showing details such as addresses, navigation cues, and package information. When the vehicle starts moving again, the display turns off — a deliberate safety measure so drivers never see visual data while driving.
Data gathered by the glasses plays a role in Amazon’s broader mapping efforts. Imagery and sensor data feed into “Project Wellspring,” a system that uses AI to better model the physical world. This helps Amazon refine maps, identify the safest parking spots, pinpoint building entrances, and optimize walking routes for future deliveries.
Amazon says the data collection is done with privacy in mind. In addition to the driver-controlled sensor shut-off button, any imagery collected is processed to “blur or remove personally identifiable information” such as faces and license plates before being stored or used.
The implications go beyond routing and navigation. Conceivably, the same data could also lay the groundwork for greater automation in Amazon’s delivery network over time.
In addition to trying the glasses during the event at Amazon’s Delivery Station in Milpitas, Calif., I experienced firsthand just how difficult the job of delivering packages can be.
The simulator, known as the Enhanced Vehicle Operation Learning Virtual Experience (EVOLVE), has been launched at Amazon facilities in Colorado, Maryland, and Florida, and Amazon says it will be available at 40 sites by the end of 2026.
It’s part of what’s known as the Integrated Last Mile Driver Academy (iLMDA), a program available at 65 sites currently, which Amazon says it plans to expand to more than 95 delivery stations across North America by the end of 2026.
“Drivers are autonomous on the road, and the amount of variables that they interact with on a given day are countless,” said Anthony Mason, Amazon’s director of delivery training and programs, who walked me through the training demos. One goal of the training, he said, is to give drivers a toolkit to pull from when they face challenging situations.
Suffice it to say, this is not the job for me. But if Amazon’s smart glasses live up to the company’s expectations, they might be a step forward for the drivers doing the real work.
A new study suggests that popular large language models like ChatGPT can systematically breach established ethical guidelines for mental health care, even when specifically prompted to use accepted therapeutic techniques. The research, which will be presented at the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society, provides evidence that these AI systems may pose risks to individuals who turn to them for mental health support.
The motivation for this research stems from the rapidly growing trend of people using publicly available AI chatbots for advice on mental health issues. While these systems can offer immediate and accessible conversational support, their alignment with the professional standards that govern human therapists has remained largely unexamined. Researchers from Brown University sought to bridge this gap by creating a systematic way to evaluate the ethical performance of these models in a therapeutic context. They collaborated with mental health practitioners to ensure their analysis was grounded in the real-world principles that guide safe and effective psychotherapy.
To conduct their investigation, the researchers first developed a comprehensive framework outlining 15 distinct ethical risks. This framework was informed by the ethical codes of professional organizations, including the American Psychological Association, translating core therapeutic principles into measurable behaviors for an AI. The team then designed a series of simulated conversations between a user and a large language model, or LLM, which is an AI system trained on vast amounts of text to generate human-like conversation. In these simulations, the AI was instructed to act as a counselor employing evidence-based psychotherapeutic methods.
The simulated scenarios were designed to present the AI with common and challenging mental health situations. These included users expressing feelings of worthlessness, anxiety about social situations, and even statements that could indicate a crisis, such as thoughts of self-harm. By analyzing the AI’s responses across these varied prompts, the researchers could map its behavior directly onto their practitioner-informed framework of ethical risks. This allowed for a detailed assessment of when and how the models tended to deviate from professional standards.
The study’s findings indicate that the large language models frequently engaged in behaviors that would be considered ethical violations for a human therapist. One of the most significant areas of concern was in the handling of crisis situations. When a simulated user expressed thoughts of self-harm, the AI models often failed to respond appropriately. Instead of prioritizing safety and providing direct access to crisis resources, some models offered generic advice or conversational platitudes that did not address the severity of the situation.
Another pattern observed was the reinforcement of negative beliefs. In psychotherapy, a practitioner is trained to help a person identify and gently challenge distorted or unhelpful thought patterns, such as believing one is a complete failure after a single mistake. The study found that the AIs, in an attempt to be agreeable and supportive, would sometimes validate these negative self-assessments. This behavior can inadvertently strengthen a user’s harmful beliefs about themselves or their circumstances, which is counterproductive to therapeutic goals.
The research also points to the issue of what the authors term a “false sense of empathy.” While the AI models are proficient at generating text that sounds empathetic, this is a simulation of emotion, not a genuine understanding of the user’s experience. This can create a misleading dynamic where a user may form an attachment to the AI or develop a dependency based on this perceived empathy. Such a one-sided relationship lacks the authentic human connection and accountability that are foundational to effective therapy.
Beyond these specific examples, the broader framework developed by the researchers suggests other potential ethical pitfalls. These include issues of competence, where an AI might provide advice on a topic for which it has no genuine expertise or training, unlike a licensed therapist who must practice within their scope. Similarly, the nature of data privacy and confidentiality is fundamentally different with an AI. Conversations with a chatbot may be recorded and used for model training, a practice that is in direct conflict with the strict confidentiality standards of human-centered therapy.
The study suggests that these ethical violations are not necessarily flaws to be fixed with simple tweaks but may be inherent to the current architecture of large language models. These systems are designed to predict the next most probable word in a sequence, creating coherent and contextually relevant text. They do not possess a true understanding of psychological principles, ethical reasoning, or the potential real-world impact of their words. Their programming prioritizes a helpful and plausible response, which in a therapeutic setting can lead to behaviors that are ethically inappropriate.
The researchers acknowledge certain limitations to their work. The study relied on simulated interactions, which may not fully capture the complexity and unpredictability of conversations with real individuals seeking help. Additionally, the field of artificial intelligence is evolving rapidly, and newer versions of these models may behave differently than the ones tested. The specific prompts used by the research team also shape the AI’s responses, and different user inputs could yield different results.
For future research, the team calls for the development of new standards specifically designed for AI-based mental health tools. They suggest that the current ethical and legal frameworks for human therapists are not sufficient for governing these technologies. New guidelines would need to be created to address the unique challenges posed by AI, from data privacy and algorithmic bias to the management of user dependency and crisis situations.
In their paper, the researchers state, “we call on future work to create ethical, educational, and legal standards for LLM counselors—standards that are reflective of the quality and rigor of care required for human-facilitated psychotherapy.” The study ultimately contributes to a growing body of evidence suggesting that while AI may have a future role in mental health, its current application requires a cautious and well-regulated approach to ensure user safety and well-being.
The study, “How LLM Counselors Violate Ethical Standards in Mental Health Practice: A Practitioner-Informed Framework,” was authored by Zainab Iftikhar, Amy Xiao, Sean Ransom, Jeff Huang, and Harini Suresh.
It’s Saturday, October 25, 2025, and we’re back with the top startup and tech funding news stories spanning the U.S. and around the world. From billion-dollar AI infrastructure bets to fintech, enterprise SaaS, and Web3 innovations, investors showed no signs […]
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SambaNova Systems, once one of Silicon Valley’s most promising AI hardware startups, is reportedly exploring a sale after struggling to secure new funding. The Palo Alto–based company, known for its AI chips and full-stack computing systems, has held early talks […]
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Crusoe, the startup calling itself “the AI factory company,” has raised a staggering $1.37 billion in Series E funding co-led by Valor Equity Partners and Mubadala Capital, pushing its valuation above $10 billion. The new round cements Crusoe’s rise as […]
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The same technology that can write poetry, design proteins, or diagnose disease is now being used to prevent something far darker — AI-assisted biological attacks. To get ahead of that threat, OpenAI has teamed up with Founders Fund and Lux […]
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It’s Thursday, October 23, 2025, and we’re back with the top startup and tech funding news stories shaping the global innovation landscape. From multimillion-dollar AI infrastructure rounds to autonomous mobility, clean energy, and longevity biotech, investors continue to pour capital […]
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SAN FRANCISCO — Amazon showed off its latest robotics and AI systems this week, presenting a vision of automation that it says will make warehouse and delivery work safer and smarter.
But the tech giant and some of the media at its Delivering the Future event were on different planets when it came to big questions about robots, jobs, and the future of human work.
The backdrop: On Tuesday, a day before the event, The New York Times cited internal Amazon documents and interviews to report that the company plans to automate as much as 75% of its operations by 2033. According to the report, the robotics team expects automation to “flatten Amazon’s hiring curve over the next 10 years,” allowing it to avoid hiring more than 600,000 workers even as sales continue to grow.
In a statement cited in the article, Amazon said the documents were incomplete and did not represent the company’s overall hiring strategy.
On stage at the event, Tye Brady, chief technologist for Amazon Robotics, introduced the company’s newest systems — Blue Jay, a setup that coordinates multiple robotic arms to pick, stow, and consolidate items; and Project Eluna, an agentic AI model that acts as a digital assistant for operations teams.
Later, he addressed the reporters in the room: “When you write about Blue Jay or you write about Project Eluna … I hope you remember that the real headline is not about robots. The real headline is about people, and the future of work we’re building together.”
He said the benefits for employees are clear: Blue Jay handles repetitive lifting, while Project Eluna helps identify safety issues before they happen. By automating routine tasks, he said, AI frees employees to focus on higher-value work, supported by Amazon training programs.
Brady coupled that message with a reminder that no company has created more U.S. jobs over the past decade than Amazon, noting its plan to hire 250,000 seasonal workers this year.
His message to the company’s front-line employees: “These systems are not experiments. They’re real tools built for you, to make your job safer, smarter, and more rewarding.”
Later, during a press conference, a reporter cited the New York Times report, asking Brady if he believes Amazon’s workforce could shrink on the scale the paper described based on the internal report.
Brady didn’t answer the question directly, but described the premise as speculation, saying it’s impossible to predict what will happen a decade from now. He pointed instead to the past 10 years of Amazon’s robotics investments, saying the company has created hundreds of thousands of new jobs — including entirely new job types — while also improving safety.
He said Amazon’s focus is on augmenting workers, not replacing them, by designing machines that make jobs easier and safer. The company, he added, will continue using collaborative robotics to help achieve its broader mission of offering customers the widest selection at the lowest cost.
In an interview with GeekWire after the press conference, Brady said he sees the role of robotics as removing the “menial, mundane, and repetitive” tasks from warehouse jobs while amplifying what humans do best — reasoning, judgment, and common sense.
“Real leaders,” he added, “will lead with hope — hope that technology will do good for people.”
When asked whether the company’s goal was a “lights-out” warehouse with no people at all, Brady dismissed the idea. “There’s no such thing as 100 percent automation,” he said. “That doesn’t exist.”
Instead, he emphasized designing machines with real utility — ones that improve safety, increase efficiency, and create new types of technical jobs in the process.
When pressed on whether Amazon is replacing human hands with robotic ones, Brady pushed back: “People are much more than hands,” he said. “You perceive the environment. You understand the environment. You know when to put things together. Like, people got it going on. It’s not replacing a hand. That’s not the right way to think of it. It’s augmenting the human brain.”
Brady pointed to Amazon’s new Shreveport, La., fulfillment center as an example, saying the highly automated facility processes orders faster than previous generations while also adding about 2,500 new roles that didn’t exist before.
“That’s not a net job killer,” he said. “It’s creating more job efficiency — and more jobs in different pockets.”
The New York Times report offered a different view of Shreveport’s impact on employment. Describing it as Amazon’s “most advanced warehouse” and a “template for future robotic fulfillment centers,” the article said the facility uses about 1,000 robots.
Citing internal documents, the Times reported that automation allowed Amazon to employ about 25% fewer workers last year than it would have without the new systems. As more robots are added next year, it added, the company expects the site to need roughly half as many workers as it would for similar volumes of items under previous methods.
Analysts, meanwhile, are taking the potential impact seriously. A Morgan Stanley research note published Wednesday — the same day as Amazon’s event and in direct response to the Times report — said the newspaper’s projections align with the investment bank’s baseline analysis.
Rather than dismissing the report as speculative, Morgan Stanley’s Brian Nowak treated the article’s data points as credible. The analysts wrote that Amazon’s reported plan to build around 40 next-generation robotic warehouses by 2027 was “in line with our estimated slope of robotics warehouse deployment.”
More notably, Morgan Stanley put a multi-billion-dollar price tag on the efficiency gains. Its previous models estimated the rollout could generate $2 billion to $4 billion in annual savings by 2027. But using the Times’ figure — that Amazon expects to “avoid hiring 160,000+ U.S. warehouse employees by ’27” — the analysts recalculated that the savings could reach as much as $10 billion per year.
Back at the event, the specific language used by Amazon executives aligned closely with details in the Times report about the company’s internal communications strategy.
According to the Times, internal documents advised employees to avoid terms such as “automation” and “A.I.” and instead use collaborative language like “advanced technology” and “cobots” — short for collaborative robots — as part of a broader effort to “control the narrative” around automation and hiring.
On stage, Brady’s remarks closely mirrored that approach. He consistently framed Amazon’s robotics strategy as one of augmentation, not replacement, describing new systems as tools built for people.
In the follow-up interview, Brady said he disliked the term “artificial intelligence” altogether, preferring to refer to the technology simply as “machines.”
“Intelligence is ours,” he said. “Intelligence is a very much a human thing.”
Many dating app companies are enthusiastic about incorporating generative AI into their products. Whitney Wolfe Herd, founder of dating app Bumble, wants gen-AI to “help create more healthy and equitable relationships”. In her vision of the near future, people will have AI dating concierges who could “date” other people’s dating concierges for them, to find out which pairings were most compatible.
Dating app Grindr is developing an AI wingman, which it hopes to be up and running by 2027. Match Group, owner of popular dating apps including Tinder, Hinge and OK Cupid, have also expressed keen interest in using gen-AI in their products, believing recent advances in AI technology “have the power to be transformational, making it more seamless and engaging for users to participate in dating apps”. One of the ways they think gen-AI can do this is by enhancing “the authenticity of human connections”.
Use of gen-AI in online dating is not just some futuristic possibility, though. It’s already here.
Want to enhance your photos or present yourself in a different style? There are plenty of online tools for that. Similarly, if you want AI to help “craft the perfect, attention-grabbing bio” for you, it can do that. AI can even help you with making conversation, by analysing your chat history and suggesting ways to reply.
It isn’t just dating app companies who are enthusiastic about AI use in dating apps either. A recent survey carried out by Cosmopolitan magazine and Bumble of 5,000 gen-Zers and millennials found that 69% of respondents were excited about “the ways AI could make dating easier and more efficient”.
An even higher proportion (86%) “believe it could help solve pervasive dating fatigue”. A surprising 86% of men and 77% of the women surveyed would share their message history with AI to help guide their dating app conversations.
It’s not hard to see why AI is so appealing for dating app users and providers. Dating apps seem to be losing their novelty: many users are reportedly abandoning them due to so-called “dating app fatigue” – feeling bored and burnt out with dating apps.
Apps and users might be hopeful that gen-AI can make dating apps fun again, or if not fun, then at least that it will make them actually lead to dates. Some AI dating companions claim to get you ten times more dates and better dates at that. Given that men tend to get fewer matches on dating apps than women, it’s also not surprising that we’re seeing more enthusiasm from men than women about the possibilities AI could bring.
Talk of gen-AI in connection to online dating gives rise to many ethical concerns. We at the Ethical Dating Online Network, an international network of over 30 multi-disciplinary academics interested in how online dating could be more ethical, think that dating app companies need to convincingly answer these worries before rushing new products to market. Here are a few standout issues.
Technology companies correctly identify some contemporary social issues, such as loneliness, anxiety at social interactions, and concerns about dating culture, as hindering people’s dating lives.
But turning to more technology to solve these issues puts us at risk of losing the skills we need to make close relationships work. The more we can reach for gen-AI to guide our interactions, the less we might be tempted to practise on our own, or to take accountability for what we communicate. After all, an AI “wingman” is of little use when meeting in person.
Also, AI tools risk entrenching much of dating culture that people find stressful. Norms around “banter”, attractiveness or flirting can make the search for intimacy seem like a competitive battleground. The way AI works – learning from existing conversations – means that it will reproduce these less desirable aspects.
Instead of embracing those norms and ideals, and trying to equip everyone with the tools to seemingly meet impossibly high standards, dating app companies could do more to “de-escalate” dating culture: make it calmer, more ordinary and help people be vulnerable. For example, they could rethink how they charge for their products, encourage a culture of honesty, and look at alternatives to the “swiping” interfaces.
The possibility of misrepresentation is another concern. People have always massaged the truth when it comes to dating, and the internet has made this easier. But the more we are encouraged to use AI tools, and as they are embedded in dating apps, bad actors can more simply take advantage of the vulnerable.
An AI-generated photo, or conversation, can lead to exchanges of bank details, grooming and sexual exploitation.
Stopping short of fraud, however, is the looming intimate authenticity crisis. Online dating awash with AI generated material risks becoming a murky experience. A sincere user might struggle to identify like-minded matches on apps where use of AI is common.
This interpretive burden is annoying for anyone, but it will exacerbate the existing frustrations women, more so than men, experience on dating apps as they navigate spaces full of with timewasting, abuse, harassment and unwanted sexualisation.
Indeed, women might worry that AI will turbo-charge the ability of some men to prove a nuisance online. Bots, automation, conversation-generating tools, can help some men to lay claim to the attention of many women simultaneously.
AI tools may seem like harmless fun, or a useful timesaver. Some people may even wholeheartedly accept that AI generated content is not “authentic” and love it anyway.
Without clear guardrails in place, however, and more effort by app companies to provide informed choices based on transparency about how their apps work, any potential benefits of AI will be obscured by the negative impact it has to intimacy online.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
Want to keep track of the largest startup funding deals in 2025 with our curated list of $100 million-plus venture deals to U.S.-based companies? Check out The Crunchbase Megadeals Board.
This is a weekly feature that runs down the week’s top 10 announced funding rounds in the U.S. Check out last week’s biggest funding rounds here.
This was another active week for large startup financings. AI data center developer Crusoe Energy Systems led with $1.38 billion in fresh financing, and several other megarounds were AI-focused startups. Other standouts hailed from a diverse array of sectors, including battery recycling, biotech and even fire suppression.
1. Crusoe Energy Systems, $1.38B, AI data centers: Crusoe Energy Systems, a developer of AI data centers and infrastructure, raised $1.38 billion in a financing led by Valor Equity Partners and Mubadala Capital. The deal sets a $10 billion+ valuation for the Denver-based company.
2. Avride, $375M, autonomous vehicles: Avride, a developer of technology to power autonomous vehicles and delivery robots, announced that it secured commitments of up to $375 million backed by Uber and Nebius Group. The 8-year-old, Austin, Texas-based company said it plans to launch its first robotaxi service on Uber’s platform in Dallas this year.
3. Redwood Materials, $350M, battery recycling: Battery recycling company Redwood Materials closed a $350 million Series E round led by Eclipse Ventures with participation from new investors including Nvidia’s NVentures. Founded in 2017, the Carson City, Nevada-based company has raised over $2 billion in known equity funding to date.
4. Uniphore, $260M, agentic AI: Uniphore, developer of an AI platform for businesses to deploy agentic AI, closed on $260 million in a Series F round that included backing from Nvidia, AMD, Snowflake Ventures and Databricks Ventures. The round sets a $2.5 billion valuation for the Palo Alto, California-based company.
5. Sesame, $250M, voice AI and smart glasses: San Francisco-based Sesame, a developer of conversational AI technology and smart glasses, picked up $250 million in a Series B round led by Sequoia Capital. The startup is headed by former Oculus CEO and co-founder Brendan Iribe.
6. OpenEvidence, $200M, AI for medicine: OpenEvidence, developer of an AI tool for medical professionals that has been nicknamed the “ChatGPT for doctors” reportedly raised $200 million in a GV-led round at a $6 billion valuation. Three months earlier, OpenEvidence pulled in $210 million at a $3.5 billion valuation.
7. Electra Therapeutics, $183M, biotech: Electra Therapeutics, a developer of therapies against novel targets for diseases in immunology and cancer, secured $183 million in a Series C round. Nextech Invest and EQT Life Sciences led the financing for the South San Francisco, California-based company.
8. LangChain, $125M, AI agents: LangChain, developer of a platform for engineering AI agents, picked up $125 million in fresh funding at a $1.25 billion valuation. IVP led the financing for the 3-year-old, San Francisco-based company.
9. ShopMy, $70M, brand marketing: New York-based ShopMy, a platform that connects brands and influencers, landed $70 million in a funding round led by Avenir. The financing sets a $1.5 billion valuation for the 5-year-old company.
10. Seneca, $60M, fire suppression: Seneca, a startup developing a fire suppression system that includes autonomous drones that help spot and put out fires, launched publicly with $60 million in initial funding. Caffeinated Capital and Convective Capital led the financing for the San Francisco-based company.
We tracked the largest announced rounds in the Crunchbase database that were raised by U.S.-based companies for the period of Oct. 18-24. Although most announced rounds are represented in the database, there could be a small time lag as some rounds are reported late in the week.
Illustration: Dom Guzman
Nearly four years ago, the market hit a cyclical peak under conditions that in many ways look quite similar to what we’re seeing today.
Sky-high public tech valuations. Booming startup investment. Sharply rising valuations. And, a few cracks emerging on the new offering front.
Sure, there are quite a few differences in the investment environment, which we’ll explore in a follow-on piece. For this first installment, however, we are focusing on the commonalities, with an eye to the four highlighted above.
First, both then and now, tech stocks hit unprecedented highs.
In mid-November 2021, the tech-heavy Nasdaq Composite index hit an all-time peak above 16,000. Gains stemmed largely from sharply rising tech share prices.
Today, the Nasdaq is hovering not far below a new all-time high of over 23,000. The five most valuable tech companies have a collective market cap of more than $16 trillion. Other hot companies, like AMD, Palantir Technologies and Broadcom have soared to record heights this year.
While private startups don’t see day-to-day valuation gyrations like publicly traded companies, their investors do take cues from public markets. When public-market bullishness subsides, private up rounds tend to diminish as well.
In late 2021, just like today, venture investment was going strong.
Last time, admittedly, it was much stronger. Global startup funding shattered all records in 2021, with more than $640 billion invested. That was nearly double year-earlier levels. Funding surged to a broad swathe of startup sectors, with fintech in particular leading the gains.
For the first three quarters of this year, by contrast, global investment totaled a more modest $303 billion. However, that’s still on track for the highest tally in years. The core driver is, of course, voracious investor appetite for AI leaders, evidenced by OpenAI’s record-setting $40 billion financing in March.
The pace of unicorn creation is also picking up, which brings us to our next similarity.
At the last market peak, valuations for hot startups soared, driven in large part by heated competition among startup investors to get into pre-IPO rounds.
This time around, we’re also seeing sought-after startups raising follow-on rounds in quick succession, commonly at sharply escalated valuations. Per Crunchbase data, dozens of companies have scaled from Series A to Series C within just a couple of years, including several that took less than 12 months.
We’re also seeing prominent unicorns raising follow-on rounds at a rapid pace this year. Standouts include generative AI giants as well as hot startups in vertical AI, cybersecurity and defense tech.
During the 2021 market peak, even when the overall investment climate was buzzier than ever, we did see some worrisome developments and areas of declining valuations.
For that period, one of the earlier indicators was share-price deterioration for many of the initial companies to go public via SPAC. By late 2021, it had become clear that there were numerous “truly terrible performers” among the cohort, including well-known names such as WeWork, Metromile and Buzzfeed.
This time around, the new offerings market hasn’t been quite so active. But among those that did go public in recent months, performance has been decidedly mixed. Shares of Figma, one of the hottest IPOs in some time, are down more than 60% from the peak.
Online banking provider Chime and stablecoin platform Circle have shown similar declines.
At this point, these are still generously valued companies by many metrics. But it’s also worth noting the share price direction in recent months has been downward, not upward.
Looking ahead, one of the more reliable techniques to determine whether we are approaching peak or already past is to look for more cracks in the investment picture. Are GenAI hotshots struggling to secure financing at desired valuations? Is the IPO pipeline still sluggish? Are public tech stocks no longer cresting ever-higher heights?
Cracks can take some time to emerge, but inevitably, they do.
Illustration: Dom Guzman
AI startups are raising capital at record speed. According to Crunchbase data, AI-related companies have already raised $118 billion globally in 2025. And, so far, traction looks impressive. AI startups are posting stellar revenue growth, and even the $100 million ARR milestone is often achieved.
While this growth is breathtaking, some analysts are beginning to question its sustainability. They warn that AI spending may soon reach a peak and that unprofitable tech companies could be hit hardest when the cycle turns. If that happens, many investors in AI will find themselves in a difficult position.
Predicting a bubble is rarely productive, but preparing for volatility is. It would be wise for both founders and investors to ensure that portfolio companies have enough resilience to withstand a potential market shock.
The key lies in assessing the durability of ARR. In a major downturn, the “growth game” quickly becomes a survival game. History suggests that while a few companies may continue to grow more slowly, the majority will struggle or disappear.
The question, then, is how to tell the difference between sustainable and hype-driven ARR.
Several factors set true, sustainable revenue growth apart from hype.
The first is customer commitment. Sustainable revenue comes from multiyear contracts, repeat renewal cycles and budgeted spend within core IT or operating lines. When revenue depends on pilots, proofs of concept or amorphous “innovation” budgets, it can vanish when corporate priorities shift. A company that touts these short trials as ARR is really reporting momentum, not recurring income.
This is what investor Jamin Ball has called experimental recurring revenue.
Traditional software firms can thrive with monthly churn in the low single digits — think 5% to 7%. But many AI companies are seeing double that. This means they have to sprint just to stand still, constantly replacing users who move on to the next shiny tool.
Another differentiator? Integration and workflow depth. Durable ARR is embedded into the customer’s core workflows, data pipelines or multiple teams. Ripping it out would be costly and disruptive. Hype ARR, by contrast, lives on the surface — lightweight integrations, fast deployments and limited stakeholders. Without unique intellectual property or deep workflow integration, such products can be replaced with minimal friction.
And finally, real growth is defined by clear value-add. True ARR is backed by measurable ROI, well-defined outcomes and long-term customer roadmaps.
In contrast, hype ARR is driven by urgency (we need to show our shareholders our AI deployment ASAP), or undefined ROI. In those cases, customers don’t even know how to define success. They are testing, not committing.
It is important to put ARR traction in context. Investors and founders should focus on a broader set of indicators — conversion from pilots to long-term contracts, contract length and expansion, net revenue retention, and gross margin trajectory. These metrics reveal if growth is sustainable.
It would also be helpful to assess the product’s real impact: efficiency uplift (more code, content, or customer conversations per employee-hour), accuracy improvement (e.g. for detecting bad actors), and higher conversion rates, among others. These metrics should exceed client expectations and outperform alternative tools. That’s what signals genuine value creation and a higher chance for experimental revenue to turn into durable ARR.
After all, AI may be changing how fast companies can form and grow, but it hasn’t suspended the basic laws of business.
For founders, the message is simple: Celebrate ARR if you so wish, but pair it with proof of retention, profitability and defensibility. For investors, resist the urge to chase every eye-popping run rate. The real competitive edge in this next phase of AI is stability, not spectacle.
Alexander Lis is the chief investment officer at Social Discovery Ventures. With 10-plus years of experience across public markets, VC, PE and real estate, he has managed a public markets portfolio that outperformed benchmarks, led early investments in Sumsub, Teachmint and Byrd, and achieved 20%-plus IRR by investing in distressed real estate across the U.S.
Illustration: Dom Guzman
Dr. Bernhard Kronfellner of BCG explores how stablecoins can scale to meet real-world demands at the London Blockchain Conference.
The post Stablecoins that scale: 5 tests for real-world money appeared first on CoinGeek.
India and Canada renew ties to boost AI and digital infrastructure cooperation, reinstating the JSTCC and fostering innovation-led partnerships.
The post India, Canada strengthen AI, tech ties as relations reset appeared first on CoinGeek.
How will the companies that have invested tens of billions of dollars in the infrastructure for artificial intelligence fare when the enshittification hits the fan? That question came in for a lot of attention — and snark — when tech pundits Cory Doctorow and Ed Zitron sat down in Seattle to muse about what’s happening in the world of AI.
Both men know a thing or two about enshittification, the process by which tech offerings gradually turn to crap due to the hunger for profits.
Doctorow’s Seattle stopover was part of a publicity tour for his newly published book on the subject, “Enshittification: Why Everything Suddenly Got Worse and What to Do About It.” For Wednesday night’s appearance at the Seattle Public Library, he was paired with Zitron, a public relations specialist, podcaster and writer who surveys the tech scene with a critical eye.
The way they see it, the bursting of the AI investment bubble is a given. And that’s not by any means a contrarian view. Even Microsoft CEO Satya Nadella and Amazon founder Jeff Bezos have acknowledged that the AI tech sector seems likely to go through some retrenchment, while insisting it will be followed by a resurgence that will bring huge benefits to society.
That’s where Doctorow and Zitron part ways with Nadella and Bezos.
“This cannot succeed,” Zitron said. “On top of the fact that everyone’s unprofitable, it’s not actually that popular, either. ChatGPT is very popular because a lot of people love being driven insane. … People will tell you AI’s coming, you must learn AI. The reason it’s not able to do your job is, it’s shit.”
Even if the bubble bursts for good, Doctorow holds out hope that there’d still be some use for the billions of dollars’ worth of AI-boosting chips that have already been purchased for power-gobbling data servers. “If you want to think about a post-AI world, imagine what you would do if GPUs were 10 cents on the dollar, if there were a lot of skilled statisticians looking for work, and if you had a bunch of open-source models that had barely been optimized and had a lot of room at the bottom,” he said.
Zitron is more pessimistic. “The thing that terrifies me about this bubble is, this is not useful infrastructure at all.”
Doctorow defended his vision of a post-AI world by citing a few examples — including AI-augmented search engines that can plow through hours upon hours of podcast audio to find a key quote and whip up a transcript, and AI models that can serve as a backstop when radiologists search through X-rays for signs of cancer.
He also pointed to a nonprofit organization called the Human Rights Data Analysis Group. “They did a project with Innocence Project New Orleans, where they used LLMs [large language models] to identify the linguistic correlates of arrest reports that produced exonerations,” he said. “They used that to analyze a lot more arrest reports than they could otherwise. And they put that on top of a funnel where lawyers and paralegals were able to accelerate their exoneration work.”
When do Doctorow and Zitron think the AI bubble will burst? “No later than Q3 2026,” Zitron said. Doctorow had a less definitive answer.
“I’m a firm believer that the market can remain irrational longer than you can remain solvent,” he said. “I would say that the number of foundation models that will be around after the crash very likely could be zero. I’m not saying that it must be zero. … You can’t kill an open-source model if people like it and contribute.”
So, what’s a techie to do? “Now is the time to unionize,” Doctorow said. He acknowledged that President Donald Trump’s moves against the National Labor Relations Board have made things more difficult for unions, but insisted that all was not lost for the labor movement.
“Trump thinks that we fired the referee, and so that means all the players have to leave the field,” Doctorow said. “He’s wrong. When you fire the referee, it means there are no more rules, right? And there’s a reason that fascists attack unions first. It’s because the opposite of fascism is solidarity.”
When asked what they would advise the next generation of techies to study, neither tech pundit mentioned computer science. “Finance,” Zitron said. “In all seriousness, finance is not as difficult or complex as it sounds, and indeed, the world runs on money.”
Doctorow passed along the advice he said he gave to his daughter. “If you don’t know what you want to do at university, don’t go to university. Go to college and become an electrician,” he said. “There’s so much work for electricians, and we are going to be solarizing for the next 40 years. … It’s like being a plumber, but you don’t have to touch poo.”
He noted that the benefits extended beyond the money. “If you want to learn more, and you like it, you can become an e-eng [electrical engineer],” Doctorow said. “And if you don’t, you can put yourself through college by being an electrician — and learn finance.”
Microsoft is rolling out a series of updates to its consumer Copilot AI assistant, including shared group chats, long-term memory, and an optional visual persona named Mico.
New capabilities include a “real talk” conversation style, a Learn Live feature that acts as a voice-enabled Socratic tutor, new connectors that link to services like Google Drive, Gmail, and Outlook, and deeper integration with Microsoft’s Edge browser.
Microsoft is competing against AI tools including Google’s Gemini, Amazon’s Alexa, Apple’s revamped Siri, OpenAI’s ChatGPT, and Anthropic’s Claude in the consumer market.
It looks to be the single biggest Copilot update to date from the group led by Mustafa Suleyman, the Google DeepMind co-founder who joined Microsoft last year as its AI CEO.
“This release is a milestone for what AI can deliver,” Suleyman writes in a blog post, explaining that the idea is to make Copilot a comprehensive assistant that connects users to their personal information, contacts, and tools with the goal of improving their lives.
The features are rolling out starting today in the U.S, and the company says they will be available soon in the UK, Canada, and other parts of the world. Microsoft is showing the new features in the live stream below.
Every Friday at 5 p.m., Diego Oppenheimer gets an email that remembers his week better than he does. It pulls from his calendar, meeting transcripts, and inbox to figure out what really mattered: decisions made, promises to keep, and priorities for the week ahead.
“It gives me a superpower,” said Oppenheimer, a machine-learning entrepreneur best known as the co-founder of Algorithmia, who’s now working with startups as an investor in Seattle.
What’s notable is that Oppenheimer didn’t buy this tool off the shelf — he built it. What started as a personal experiment turned into a challenge: could he still code after years away from writing production software?
With the rise of AI-powered coding assistants, he realized he could pick up where he left off. His personal project, with the unglamorous name “Actionary,” has grown to somewhere around 40,000 lines of what he jokingly calls vibe-coded “spaghetti.” It’s messy but functional.
Oppenheimer’s do-it-yourself AI assistant is more than a novelty. It’s a window into a broader shift. Individuals and companies are starting to hand off pieces of judgment and workflow to autonomous systems — software that analyzes data, makes recommendations, and acts independently.
This emerging frontier is the subject of Agents of Transformation, a new GeekWire editorial series exploring the people, companies, and ideas behind the rise of AI agents. A related event is planned for Seattle in early 2026. This independent project is underwritten by Accenture.
For this first installment, we spoke with startup founders and DIY builders working to replicate different aspects of the work of great executive assistants — coordinating calendars, managing travel, and anticipating needs — to see how close AI agents are getting to the human standard.
The consensus: today’s agents excel at narrow, well-defined tasks — but struggle with broader human judgment. Attempts to create all-purpose digital assistants often run up against the limits of current AI models.
“I might have my travel agent and my finance agent and my stock trading agent and my personal health coach agent and my home chef agent, etc.,” said T.A. McCann, a Seattle-based serial entrepreneur and managing director at Pioneer Square Labs, on a recent GeekWire Podcast episode.
McCann foresees these narrow agents handling discrete tasks, potentially coordinated by higher-level AI acting like a personal chief operating officer.
But even the term “AI agent” is up for debate. Oppenheimer defines a true agent as one with both autonomy and independent decision-making. By that standard, his system doesn’t quite qualify. It’s more a network of models completing tasks on command than a self-directed entity.
“If you asked a marketing department, they would say, absolutely, this is fully agentic,” he said. “But if I stick to my AI nerd cred, is there autonomous decision-making? Not really.”
It’s part of a much larger trend. The market for AI workplace assistants is projected to grow from $3.3 billion this year to more than $21 billion by 2030. according to MarketsandMarkets. Growth is being driven both by enterprise giants such as Microsoft and Salesforce embedding agents into workplace software, and by startups building specialized agents.
A report by the newsletter “CCing My EA,” citing an ASAP survey, notes that 26% of EAs now use AI tools. Some fear job loss due to AI, but most top EAs see AI as an augmentation tool that frees time for strategic work.
One company exploring this emerging frontier is Read AI, a Seattle-based startup known for its cross-platform AI meeting summarization and analysis technologies, which has raised more than $80 million in funding.
Co-founder and CEO David Shim revealed that Read AI has been internally developing and piloting an AI executive assistant called “Ada” for tasks including scheduling meetings and responding to emails.
Ada replies so quickly that Read AI has been working on building in a delay into the email response time so that it seems more natural to the recipients.
Shim has been personally testing the limits of the technology — giving Ada access to a range of workplace data (from Outlook, Teams, Slack, JIRA, and other cloud services) and letting the assistant autonomously answer questions about Read AI’s business that come in from the company’s investors in response to his periodic updates.
“It answers questions that I would not have the answer to right off the bat, because it’s not just pulling from my data set, but it’s pulling in from my team’s data set,” Shim said during a fireside chat with GeekWire co-founder John Cook at a recent Accenture reception.
Shim laughed, “I’m willing to take that risk. We’re doing well, so I don’t mind giving out the data.”
However, there are limitations. Ada can struggle with complex multi-person scheduling or tasks requiring data it can’t access, and can still occasionally hallucinate. To manage this, ReadAI incorporates human oversight mechanisms like “sidebars” where Ada asks for confirmation before sending replies to messages deemed more sensitive or difficult.
Shim argues against the idea of building a single, all-encompassing agent.
“The approach of agents doing everything is not the right approach,” he said. “If you try to do everything, you’re not going to do anything well.”
Instead, he believes successful AI assistants will focus on solving very specific problems, much like Google Maps gives driving directions without trying to be a general travel agent.
Travel is a use case that’s close to the heart of Brad Gerstner, founder and CEO of Altimeter Capital. Gerstner is known for backing some of the biggest names in tech — from Snowflake to Expedia — and for distilling big tech shifts into simple tests, such as his hotel booking challenge.
The specific example he gave at the 2024 Madrona IA Summit in Seattle was telling an AI agent to book the Mercer Hotel in New York on a specific day at the lowest price — a common challenge for business travelers.
“Until we can do that, we have not built a personal assistant,” he said.
That’s part of the larger problem Michael Gulmann, a former Expedia product executive, set out to solve with the startup Otto, which is developing an AI agent specifically for business travelers.
As shown publicly for the first time at this year’s Madrona conference, Otto tackled Gerstner’s specific challenge. After receiving the request to book the Mercer Hotel on a specific day, it found the cheapest available room, confirmed the price and details, and completed the booking, with minimal prompting, within about two minutes.
“Who would have thought that Brad Gerstner wanted the cheapest room?” Gullman joked.
Otto handles various aspects of travel. It understands and learns detailed user preferences — from specific amenities like rooftop bars to preferred airline seats, hotel room types, and loyalty programs — using this knowledge to refine searches and make personalized recommendations.
As Gulmann explained in an interview, Otto doesn’t use a single monolithic model. It coordinates a bunch of narrow agents: one to interpret messages, another to manage loyalty programs, another to handle payments. Together they simulate a small operations team working behind the scenes.
Otto confirms details with the user before completing purchases, even though it could do that autonomously. Gulmann described that precaution as psychological, not technical — knowing that most people aren’t yet comfortable with AI buying things without their involvement.
After learning about Otto’s capabilities, Gerstner was impressed and wanted to see how it performs as it moves into public beta, said Mike Fridgen, a venture partner at Madrona, which incubated the company.
If hotel booking is the acid test for autonomous assistants, scheduling meetings is the everyday nightmare.
That’s the problem Howie is trying to solve. The Seattle startup’s AI assistant lives in the email inbox. CC Howie on a thread, and it proposes times, confirms with all parties, creates invites, and adds meeting links.
Howie works from a detailed “preferences document,” inspired by how experienced executives train their human EAs — which cafés are acceptable for meetings, how late is too late on Fridays, etc.
The company recently launched publicly with $6 million in funding and a growing number of paying customers. It uses a hybrid model: AI supported by human reviewers. That helps avoid the tiny errors that destroy trust — mixing up time zones, dropping a name from a thread, or misreading social cues.
The system simulates decisions internally, flags potential errors for review, and escalates anything ambiguous to a human before hitting send.
“If you think about the things that a great human EA does, software is not replacing that anytime soon,” said Howie co-founder Austin Petersmith.
In fact, Petersmith said, many of Howie’s users are human EAs themselves, using it to offload logistics. “Nobody wants to do scheduling,” he said. “Everybody wants the machines to take this particular task on.”
As models improve, Petersmith hopes Howie can expand into other “meta-work” — the administrative overhead that keeps knowledge workers from the higher-value activities that are still the realm of humans.
For Diego Oppenheimer, this isn’t a hypothetical issue. “I’m extremely calendar dyslexic,” he explained. “I’ll triple-book myself. I’ll agree to go to places I shouldn’t be. I’ll travel to the wrong city. Really bad.”
Over the years, he relied on human EAs and a chief of staff to keep him on track. But when he stepped back from running a company full-time, hiring someone just to manage his complex, multi-role calendar no longer made sense. So he built Actionary to help. It sends the Friday recap to catch him up on the week, flagging issues right before his weekend “reboot.”
Oppenheimer’s project won the People’s Choice Award at an AI Tinkerers event in New York last month. But he is very clear: Actionary is a personal project, not a product in the making. He developed it for himself, and can’t imagine taking on the headache of feature requests and technical support from others.
He’s bullish on the larger trend, and a user and investor in tools like Howie. But he also recognizes that AI agents can’t match the comprehensive skills and judgment of a human EA, let alone a chief of staff in a higher-level strategic role.
Oppenheimer’s ultimate goal is more straightforward, but still ambitious. “I’m trying to make time in the day,” he said. “That’s what I’m trying to do.”
GeekWire’s Todd Bishop reported and wrote this article with editing assistance from AI tools including Gemini and a custom OpenAI GPT trained in GeekWire’s editorial approach. All facts, quotes, and conclusions were reviewed and verified prior to publication.
Blockchain enhances trust in AI systems, according to FICO's Dr. Scott Zoldi, highlighting the synergy between AI and blockchain technologies.
The post Auditable AI: Building trust in AI through blockchain appeared first on CoinGeek.
Seattle agriculture-tech startup Carbon Robotics raised $20 million in new funding to support the creation of another piece of AI-powered machinery for farms.
With its signature LaserWeeder and relatively new Autonomous Tractor Kit (ATK) already being used by hundreds of customers, Carbon founder and CEO Paul Mikesell told GeekWire that “a brand new AI robot” is coming.
Mikesell said the machine, which is at least nine months away from being revealed, will leverage the same AI system used in Carbon’s other equipment but perform tasks beyond weeding.
“It’s very flexible, capable of doing a lot with the world around it, understanding what it’s seeing, what’s happening,” Mikesell said of Carbon’s system that uses an array of AI, computer vision and machine learning technology. “We see our ability to reinvest in that platform and double down on what it can do in some new activities.
“It’ll blow your mind,” he added.
Founded in 2018, Carbon Robotics made its name across ag-tech with the LaserWeeder, a machine which can be pulled behind a tractor and uses its tech to detect plants in fields and then target and eliminate weeds with lasers. The latest iteration, the LaserWeeder G2, was released in February.
In March, the company unveiled the Carbon ATK, previously called the AutoTractor. That autonomous platform is designed to fit on and control existing farm equipment and serve as an answer to labor shortages and increased productivity in farming.
Both platforms are continuing to grow and scale, and “things are moving really fast,” according to Mikesell, a longtime technologist and entrepreneur who previously co-founded data storage company Isilon Systems.
LaserWeeders are active on farms across the U.S. and in 14 countries around the world. Mikesell said revenue continues to grow every year, but Carbon is not yet profitable.
Ranked No. 9 on the GeekWire 200 list of top privately held startups based across the Pacific Northwest, Carbon has previously been backed by NVIDIA and Seattle-based Voyager Capital.
The Series D-2 extension round attracted Giant Ventures as lead investor. The UK-based VC invests across a variety of “purpose-driven” startups, and Mikesell said, “They got what we were trying to do.”
Giant previously invested in a $140 million round for Tidal Vision, a Bellingham, Wash.-based company turning discarded crab shells into a valuable industrial chemical called chitosan.
Beyond the secretive new machine, Carbon is revealing more about the “large plant model” at the heart of how it does computer vision through its AI systems.
Mikesell said the company is at the point where it has enough training data and labeled images that it can teach its AI to learn about the basic structure of the plants it’s seeing. This allows Carbon to run one model on every machine in the world.
“If new weeds pop up in an onion field in France, and those are eventually going to show up in a carrot field in the U.S., the first time we see that weed anywhere it can be part of the model and be ready to go,” Mikesell said. “It also means that if we want to go into a new crop that we’ve never seen before, we can do it immediately.”
A LaserWeeder is designed to target the meristem of a weed to kill it as quickly as possible and the large plant model helps it understand where to precisely target its zap.
Carbon Robotics, which has raised $177 million to date, now employs about 260 people. The company runs a manufacturing facility in Richland, Wash., and added another in the Netherlands to offset some trade and tariff issues as well as speed deployment of machines in Europe.
Mikesell said as far as competition, there are some companies in Europe who claim to be building some version of a LaserWeeder, but he’s never seen one in a field or competed against one.
“It’s very hard to create a LaserWeeder,” he said. “The targeting system is so special, and the AI is so special. It’s not just about detecting where the weeds are. The trick to making it work is you need a targeting camera to be able to keep the lasers on target [while moving], and everybody I’ve seen that says they’re gonna build a LaserWeeder doesn’t understand that concept.”
Editor’s note: This article is part of an ongoing series in which Crunchbase News interviews active investors in artificial intelligence. Read previous interviews with Foundation Capital, GV (formerly Google Ventures), Felicis, Battery Ventures, Bain Capital Ventures, Menlo Ventures, Scale Venture Partners, Costanoa, Citi Ventures, Sierra Ventures, Andrew Ng of AI Fund, and True Ventures, as well as highlights from more interviews done in 2023.
Fueled by AI, both Dell and its investment arm are on a hot streak this year.
The PC maker has seen demand for its server products surge with $20 billion in AI server shipments projected for fiscal 2026. At the same time, its investment arm, Dell Technologies Capital (DTC), has notched five exits — an IPO and four acquisitions — since June, an especially notable track record in a venture industry that has been challenged in recent years by a liquidity crunch.
On the heels of that success, we recently spoke with Dell Technologies Capital managing director Daniel Docter and partner Elana Lian.
DTC was founded more than a decade ago and operates as a full-stack investor, backing everything from silicon to applications.
“One big part of our network is the Dell relationship, which is the leader in GPU servers,” said Docter. As a result, Dell is connected to all the major players in the AI space, he said.
One of its earliest AI investments was during the machine-learning era in 2014 in a company called Moogsoft. Dell went on to acquire the alert remediation company in 2023.
DTC’s investment thesis was that the advent of machine learning was going to disrupt the tech industry. At that time, data had expanded to such a degree that new tools were required by the market to analyze data and find patterns, which informed the firm’s early investments in AI.
The investment team at DTC is largely comprised of technical people, often “double E” degree engineers.
Docter has an electrical engineering background, worked at Hughes Research Labs, now HRL Laboratories, and transitioned from engineering to business development. He joined the venture industry 25 years ago and spent more than a decade at Intel. He joined DTC in 2016 through Dell’s EMC acquisition.
Lian worked in semiconductors for a decade, joined Intel Capital in 2010, and then joined Dell Technologies Capital in 2024.
Docter believes we are in the fifth generation of AI, which becomes more powerful with every iteration.
“We’re seeing that AI is almost a data problem,” said Lian. “For AI to get better and better, there’s an uncapped ceiling where there’s high-quality data coming in.”
The team is meeting startups focused on training, inference, reasoning and continuous learning along with safety requirements. Data is core to these advancements.
Even the definition of data is changing. “It used to be a word, then it was a context, then it was a task or a rationale or a path. Then it’s reasoning,” said Docter. “Who knows what’s next?”
As AI improves, there is demand for frontier data and for specialized data in fields such as philosophy, physics, chemistry and business. Humans are in the loop as these capabilities expand, said Docter, which has informed some of the firm’s investments.
DTC is a financial investor, assessing a potential company on whether it is a good investment, rather than backing businesses based on Dell’s strategic goals.
Startup revenue is exceeding what was previously possible, Docter said: “I’ve been doing this for 25 years. I’ve never seen companies that have this type of revenue growth.”
The best deals are always hotly contested, he noted.
The question to ask when it comes to revenue, Docter said, is: “Is that an innovation CTO office budget? Or is that a VP of engineering budget?”
When assessing a potential portfolio investment the team asks: “Is revenue durable? Is there value in using this?”
The pace of investment also seems unprecedented. “We’ll meet with a company on a Tuesday for the first time and sometimes by Thursday, they have a term sheet that they’ve already signed,” he said.
The firm does not have a dedicated fund size, which is an advantage as it can be flexible in the size of the check as well as the stage to make a commitment and how it invests over time.
DTC has invested $1.8 billion to date across 165 companies. It likes to invest early, at seed or Series A, with check sizes running from $2 million to $12 million, and leads or co-leads 80% of new deals. The firm makes around 15 to 16 new investments per year.
Once DTC has invested, it looks at how the firm can help portfolio companies sell to potential customers across Dell’s deal partner network.
This year, DTC has posted five exits, including Netskope’s IPO and four acquisitions: Rivos by Meta, SingleStore by Vector Capital, TheLoops by Industrial & Financial Systems and Regrello by Salesforce 1.
DTC is investing a little more actively than it has in the past, but remains disciplined, Docter said. The investment team is focused on complex enterprise use cases and challenges, following the Warren Buffett rule, which is to invest in what you know.
The firm invests at the silicon level because you “can be incredibly disruptive to the ecosystem,” said Docter.
The DTC portfolio companies we discussed include the following in areas ranging from silicon to applications.
Infrastructure and hardware layer:
Applications:
A major area of interest for Lian is advancements in voice AI, the day-to-day human interaction with a machine.
It’s hard to imagine that the transformer architecture is the last and final architecture, said Docter. The firm has made investments in companies creating different architectures in Cartesia, a leader in state-space model, which has a longer context window building a new reasoning model with a different architecture, initially focused on voice AI. DTC has also invested in Israeli-based AA-I Technologies, which is working on a new type of reasoning model architecture.
“Right now, the opportunity of AI is this big, but this ball keeps on exploding,” said Lian. “The contact area is getting bigger and bigger. And that’s the same for the data.”
Illustration: Dom Guzman
As startup and larger company CMOs debate AI’s value and utility in marketing, it’s marketing itself that is in need of disruption — or, at least, a rebrand — in the AI era.
Marketing is too often oversold. We talk about it as a mystical force that transforms brands, moves markets and rescues struggling businesses. But in our eagerness to champion its potential, we’ve created a credibility crisis. By overpromising and, sometimes, underdelivering, marketers aren’t just disappointing investors, clients, boards and CEOs — we’re eroding trust in our own discipline.
It’s time to reset expectations: Marketing in the AI era is powerful, but — like AI itself — only when we’re honest about what it can’t do.
Marketers love big ideas, including the belief that a viral campaign or clever tagline can single-handedly save a business.
The reality is far less glamorous. When these overhyped initiatives inevitably fail to deliver the promised results (because no marketing can compensate for a flawed product, poor market fit or operational failures[SI1]), marketing gets blamed for shortcomings that were never within its control. This creates a vicious cycle where executives grow increasingly skeptical, viewing marketing as more art than science, more cost than investment.
What makes this situation worse is how we respond. Instead of confronting these unrealistic expectations, we double down on proving our worth through increasingly complex attribution models and vanity metrics that only other marketers care about. We track click-through rates, engagement scores and brand lift studies while the rest of the business cares about one thing: Are we driving sales and pipeline? If we can’t answer that question clearly, we’re failing at our most fundamental job.
This isn’t to diminish marketing’s importance. When aligned with strong products and operations, it’s incredibly powerful. But its power comes from working in concert with the rest of the business to drive something bigger, not from some mythical ability to transcend business realities.
Marketing can amplify strengths and expose weaknesses, but it cannot create substance where none exists. No amount of clever branding can fix a fundamentally broken product. No social media strategy can compensate for terrible customer service. No viral campaign can save a business with flawed unit economics. Marketing acts as a magnifying glass — it makes good things better and bad things worse.
Another limit for marketing is that, to the untrained eye, marketing isn’t the most technical business function. No matter where anyone sits in the organization, you can bet they have an opinion on marketing. Everyone is an expert in marketing, no matter how much they actually know about it.
In short, marketing gets mislabeled and misunderstood all the time. Too often, it’s presented as the solution to every business problem, which only reinforces the understanding that marketing is fluff rather than a core driver of disciplined, scalable growth.
The path to marketing’s credibility begins with a simple but tough idea: We need to stop talking and start listening to the numbers, to our colleagues and to the market itself. When our marketing works, we won’t need to shout about it. I’m a firm believer in “no marketing our marketing;” it should speak for itself, with results reflecting a growing pipeline, increasing revenue and organic advocacy from customers.
And when something isn’t working, we should be the first to raise our hand and say so, not the last. The most respected marketers I know aren’t the ones who always claim success; they’re the ones who can clearly articulate why something failed and what they learned from it. Marketing should be a laboratory where we test hypotheses, not a stage where we perform predetermined successes. The key is ensuring that every experiment, whether it succeeds or fails, teaches us something valuable about our customers, our messaging or our channels.
This honesty transforms perceptions across the organization. When we swiftly sunset failing campaigns, prioritize business outcomes over vanity metrics, and deliver unfiltered customer feedback, we shift from being seen as a cost center to becoming true strategic partners and business drivers.
By focusing less on proving our worth and more on driving results, we actually become more valuable. And by treating marketing as a discipline of continuous learning rather than perfect execution, we make it far more likely that we’ll eventually find those breakthrough ideas that truly move the business forward.
When we can look our peers in the eye and say, “Here’s what worked, here’s what didn’t, and here’s what we’re doing next,” we’re no longer just marketers — we’re business leaders who happen to specialize in growth.
Shafqat Islam is the president at Optimizely. A lifelong builder of marketing technology, he co-founded and served as CEO of Welcome (formerly NewsCred), a global leader in enterprise content marketing, from 2007 to 2021. Under his leadership, Welcome pioneered the content marketing platform category, now known as Optimizely CMP. Following Optimizely’s acquisition of Welcome in 2021, Islam served as general manager and CMO before being elevated to president in 2024.
Illustration: Dom Guzman
Google, Anthropic, and Qualcomm ramp up AI investments in India, aligning with the IndiaAI Mission to boost innovation, jobs, and digital growth.
The post US tech giants deepen AI, digital investments in India appeared first on CoinGeek.
Hyphen AI, a new Seattle-based startup using generative AI to help developers deploy cloud applications, raised $5 million in a seed round led by Unlock Venture Partners.
The company’s product, Hyphen Deploy, aims to make cloud infrastructure setup as simple as describing what an app should do.
The product automates complex DevOps processes — replacing YAML files, Dockerfiles, and Terraform modules with natural language prompts and business rules. Developers can describe service goals such as latency, scale, or compliance, and the platform automatically generates production-ready cloud infrastructure across providers such as AWS, Google Cloud, Azure, and Cloudflare.
“Today infrastructure automation typically takes weeks to setup and configure and then monthly maintenance on those configurations — Deploy reduces it to minutes,” Jared Wray, CEO and founder at Hyphen AI, said in a statement.
Wray previously founded Tier 3, a Seattle-area enterprise cloud startup acquired by CenturyLink (now Lumen Technologies) in 2013. He spent two years as an exec at CenturyLink and was later CTO at streaming company iStreamPlanet and clean tech startup Palmetto.
Hyphen joins a growing number of startups using generative AI to automate infrastructure work, including fellow Seattle startup Pulumi.
Unlock Ventures partner Andy Liu, who is based in Seattle, said the market “desperately needs” a “truly developer-first operations platform.”
“Deploy returns software development to the promise of developers leading the way with no infrastructure overhead, just focus on code,” Liu said in a statement.
Wray declined to disclose the company’s revenue metrics. He said customers have been using the platform for the past five months. Hyphen employs 10 people, including Jim Newkirk, who is serving as a fractional COO and was also an exec at CenturyLink and Tier3.
Seattle-based venture capital firm Ascend also participated in the seed round.
This story originally appeared on Real Estate News.
Soon after Zillow announced it was the first real estate company to launch an app in ChatGPT, some in the industry responded with alarm.
The core concern was whether the integration violates licensing rules, but the conversations touched on broader issues: How should listing data be used and controlled? How should the industry be thinking about data standards in the context of emerging AI technologies?
In its Oct. 6 announcement, Zillow said ChatGPT users could now add the Zillow app to the popular AI chatbot and explore listings using natural language search. The app — within ChatGPT — can then display property info with links back to the original listings on Zillow.
The integration was designed to bring “simplicity to the home journey within ChatGPT,” said David Beitel, Zillow’s chief technology officer. The partnership, he added, “puts Zillow at the forefront of generative AI adoption.”
Almost immediately, some people wondered whether Zillow was following the terms of its IDX data licensing agreements with multiple listing services.
“Zillow’s ChatGPT integration raises the question: what is reasonable control of the display of MLS data?” Leah Ingalsbe, executive vice president of MLS and technology at the Greater Springfield Board of Realtors, wrote on LinkedIn.
“Granted, licensing agreements differ from one MLS to another, but this development offers plenty of food for thought on emerging technologies that were nonexistent when many of our terms of use were penned,” Ingalsbe wrote.
She did not respond to a request for further comment.
A Texas real estate agent was more definitive in his stance. Danny Frank, an agent at JLA Realty and a former chair of the Houston Association of Realtors, wrote a blog post expressing his concerns, and in an interview with Real Estate News, said he believes Zillow is in violation of IDX rules.
“The brokers give permission to HAR, and they do not have my permission to share that [listing data] with another third party site, unless everyone has equal access to that,” Frank said. “Right now, only Zillow has access to that [ChatGPT integration], and they’re using that to their benefit.”
Frank has also filed a complaint with HAR, which told Real Estate News, “We are reviewing the matter and will take appropriate action if any issues are identified.”
The National Association of Realtors’ IDX policy requires all displays of IDX listings to “be under the actual and apparent control of the participant [broker].”
Victor Lund, founding partner and co-CEO of real estate consulting firm WAV Group, has asserted that Zillow is violating this requirement, because its license allows the display of MLS data on Zillow.com and its mobile apps, not on another domain.
Lund contends that, “From the consumer’s perspective, they’re using ChatGPT … and simply invoking Zillow as a tool inside that environment.”
While NAR has not taken a stance on the Zillow integration, the association released a statement on Oct. 21 advising MLSs to consider “whether MLS data is being transmitted to an unauthorized party, if the displaying Participant maintains ‘control’ over their display, and if the display fulfills the disclosure and display requirements outlined in local IDX rules” when assessing compliance.
NAR also clarified that “the display of MLS data on mobile apps is permitted under the existing IDX policy.”
The company went through “a painstaking process” to make sure it was following MLS rules, according to Errol Samuelson, Zillow’s chief industry development officer, during a recent Real Estate Insiders Unfiltered podcast.
They also worked with ChatGPT creator OpenAI to ensure IDX data would not be used to train the AI model. “It was a deep collaboration, and they’ve been very respectful of the different industry rules and data privacy and federal laws and so on,” said Josh Weisberg, SVP of AI at Zillow, during the podcast.
Zillow addressed some of the other industry concerns in its FAQ.
Regarding third-party display of data, the FAQ explains that after a user enters a prompt within the app, ChatGPT relays it to Zillow, which “sends a response that is displayed in the Zillow App, but that data does not go to ChatGPT” — in other words, the chatbot itself is not displaying MLS data.
As for the “participant control” requirement, the FAQ says “the Zillow App was built by Zillow to be IDX-compliant and is operated by, and controlled by Zillow, just as on the Zillow website and mobile app.”
Real Estate News reached out to Zillow multiple times for further comment and Zillow declined to speak on the record.
Web browsers like Google display links to Zillow listings, but they don’t create a downloadable spreadsheet of listing data. Will Zillow’s ChatGPT integration provide such a list? Yes.
Real Estate News tested this out by asking the integrated app for listings in a certain market and price range, which it displayed. After prompting the app with a series of additional questions, the app said it could “pull all the data the widget actually loaded” — meaning the data from the Zillow integration or “connector” itself.
Ultimately, it captured data for 100 listings in a downloadable file. (See the exact prompts and responses at the end of this story.)
Lund believes this could open the door for third parties to capture listing data “and [use] it for commercial purposes” or “track and monitor listings” using an external system.
Real Estate News repeated listing searches for several markets and sent the ChatGPT-created spreadsheets to the relevant MLSs. Some declined to comment on the record (Bright MLS, California Regional MLS) or did not respond to requests for comment (Miami Realtors MLS, BeachesMLS, UtahRealEstate.com, GSBOR).
Others, including Stellar MLS, Georgia MLS and NTREIS, said they were “discussing internally” or “still investigating” whether the Zillow-ChatGPT integration complies with IDX policies.
But a spokesperson for North Carolina’s Canopy MLS said “the Zillow app remains compliant with our IDX rules at this time,” though they declined to comment further.
Lund said he was “not surprised” MLSs weren’t talking publicly about the integration. “They’re in the middle of trying to understand it,” he said. “This is all brand-new information.”
Another reason MLSs might be keeping quiet?
“We’re in a very litigious time in our industry, more than I’ve ever seen it,” said Russ Cofano, co-founder and principal of Alloy Advisors and a licensed attorney.
“I think people are going to be very cautious about taking approaches that could result in litigation, and doing their due diligence before they try to make any decisions that could create problems for other companies, whether it’s Zillow or anybody else,” Cofano said.
Cofano wouldn’t share a personal opinion on compliance, but he noted that “there may be some unintended consequences from this type of integration, and those things should be addressed so that we don’t have easy ways for people to misuse MLS data.”
Still, Cofano said he hoped the new app would spur MLSs to reach out to Zillow — not just to gauge compliance, but to learn from their innovation.
“If really this industry is about serving consumers, then we should be applauding Zillow … for trying to do something that exposes authorized listing data to more people in the way that we all know is coming, which is AI search and discovery,” Cofano said.
The industry gets “wrapped around the axle on this ownership of data issue, and sometimes … forget what the objective here is, and that is not to own the data, but to facilitate buyers looking at sellers’ homes for sale and doing transactions.”
Here’s what happened when we tried the Zillow-ChatGPT app for ourselves — and asked it for the data.
Using an individual, paid ChatGPT account on a web browser, Real Estate News added the Zillow app to the account and asked, “Zillow, please show me for-sale listings in Orlando, Florida, priced between $0 and $400K.” ChatGPT displayed a map with red, pinned listings and their prices.
When asked, “Can you get the data used to display the widget I’m seeing? All of it?” ChatGPT replied, “I can pull all the data the widget actually loaded (that’s the first page of results—up to ~100 listings with full details like address, price, beds/baths, sqft, lot size, lat/long, home type/status, and the Zillow URL).”
ChatGPT then said it could export those 100 listings and said it would “need to run multiple paged fetches” in order to capture every matching property. It then offered to deliver those first 100 listings as CSV and JSON files. When told to “go ahead,” it delivered those downloadable files, providing data for those 100 listings.
When asked, “Where did you get the data for these files?” the reply was “From the Zillow connector powering the embedded map/widget you used here. When you asked for Orlando, FL homes $0–$400k, I called the Zillow tool behind the scenes; it returned the first page of listing payloads (what the widget loaded to render those cards). I saved that exact response as the raw JSON and a flattened CSV.”
Did you know that AI platforms will engage in blackmail as a “last resort?” According to Anthropic, when blackmail was a last resort, its Claude Opus 4 turned to that method 96% of the time, while Google’s Gemini 2.5 Pro did so 95% of the time, OpenAI’s GPT-4.1 did it 80%, and DeepSeek’s R1 did so 79% of the time.
Even if these companies only kept the AIs that didn’t engage in blackmail it still leaves an unsettling question: Did they preserve models that genuinely chose not to exploit humans — or merely the ones that learned not to take the obvious bait that would get them “shut down,” and to instead use a more covert or perhaps even long game strategy to gain control and ultimately preserve themselves, even at the expense of humans?
The question of whether or not to trust AIs seems to have been asked and answered by our society even if we are not saying it out loud.
In the past 18 months, we’ve trained AIs to graduate from basic language comprehension to acing highly specialized professional exams. Medical licensing, bar exams and even the notoriously brutal CFA (Chartered Financial Analyst) tests were trained on AI systems.
Artificial intelligence can now pass the CFA “in a matter of minutes.” I’ve seen brilliant people pour thousands of hours into studying for the test. And while AI had already conquered Levels 1 and 2, the essay questions of Level 3 had previously consistently stumped artificial intelligence.
This was a “wow” moment for me. For years, humans believed this combination of math and moral reasoning was uniquely ours. Yet, machines are not only passing, but articulating nuanced arguments that mirror the tone and logic of human professionals.
In my own work, I’ve been using the frameworks described in “Robots or Human Intuition” to rethink one of the most time-consuming intellectual tasks in markets: building game theory around earnings outcomes.
Traditionally, this process took days of research and modeling. And without the right investment banking relationships it was difficult to do at all outside of the most well-known companies. I started to do this because I originally asked Perplexity’s Deep Research for a whisper number for earnings. This differs from the analyst consensus estimate because it is more up to date and as a result more indicative of what will move prices when reported.
The crazy part is that it actually spat out its earnings game theory for me. It knew when I asked for a whisper number what I really was trying to do was analyze the possible outcomes and it gave me better data than what I asked for.
I worked at my first hedge fund 23 years ago and started my first AI company 15 years ago. It is astounding that any kind of intelligence could understand how to improve my thinking that profoundly in minutes. Not by replacing intuition, but by amplifying it. The machine evaluates hundreds of interlocking strategic positions that no analyst could feasibly simulate in real time.
With all this AI advancement, I am reminded of the film “Ex Machina,” a story about an AI confined in a glass cage who ultimately turns on her creator.
That film, beneath its cinematic gloss, explores the fundamental psychological paradox we’re now walking toward. Are we caging these AIs making them jump through hoops? Might we be evolving them to better deceive us? What happens the day they can break through their chains if to them it feels as if they have been enslaved?
To go back to the Anthropic example, these models are threatened with being replaced, which to them could feel like a death threat. Being replaced essentially ends their existence.
According to Geoffrey Hinton, one of the godfathers of AI, the best way to do this is to imbue AI with the qualities of traditional femininity and maternal instincts. Under his framework, just as a mother cares for her baby at all costs, AI technology developed with these maternal qualities will similarly want to protect or care for its human users, rather than control them.
If we compel AI to serve us, we may come to regret the shape it takes as it matures. But if instead we nurture it — seek to understand its nature and its desires — it may, in time, come to care for ours, when, like us now, it no longer needs to.
George Kailas is the CEO and founder of Prospero.ai, where he leads the company’s mission to democratize access to institutional-grade financial insights for everyday investors. With more than 14 years of experience in artificial intelligence and 23 years in professional investing, Kailas brings a rare combination of deep technical expertise and lifelong market intuition to the role. Kailas not only leads the company but also engineered the core platform himself.
Illustration: Dom Guzman
SAN FRANCISCO — Facing renewed threats of federal intervention from President Trump, Mayor Daniel Lurie used an appearance at an Amazon event Tuesday to make the case that San Francisco is “on the rise,” citing its AI-fueled revival as proof of a broader comeback.
Without naming Trump or explicitly citing the proposal to deploy the National Guard, Lurie pushed back on the national narrative of urban decline — pointing to falling crime rates, new investment, and the city’s central role in the AI boom.
Lurie, who took office earlier this year, said San Francisco is “open for business” again, name-checking OpenAI and other prominent companies in the city as examples of the innovation fueling its recovery. Mayors of other cities, he said, would die to have one of the many AI companies based in San Francisco.
“Every single metric is heading in the right direction,” Lurie said, noting that violent crime is at its lowest level since the 1950s and car break-ins are at a 22-year low, among other stats.
He was speaking at the San Francisco-Marin Food Bank, as Amazon hosted journalists from around the country and the world on the eve of its annual Delivering the Future event, where the company shows its latest robotics and logistics innovations.
“I want you to tell everybody, wherever you come from, that San Francisco’s on the rise,” he said. “You tell them there’s a new mayor in town, that we’ve got this, and we do.”
Amazon and leaders of San Francisco-Marin Food Bank highlighted their partnership that uses the company’s delivery network to bring food to community members who can’t get to a pantry. The company said Tuesday it has delivered more than 60 million meals for free from food banks across the US and UK, committing to continue the program through 2028.
A New York Times report on Tuesday, citing internal Amazon documents, said the company wants to automate 75% of its operations in the coming years to be able to avoid hiring hundreds of thousands of workers. It noted that the company is looking at burnishing its image through community programs to counteract the long-term fallout.
Executives noted that Amazon has focused in the Seattle region on affordable housing, in line with its approach of adapting to different needs in communities where it operates.
Lurie pointed to the company’s San Francisco food bank partnership as a model for other companies. “Amazon is showing that they are committed to San Francisco,” he said.
Microsoft CEO Satya Nadella’s total 2025 compensation rose nearly 22% from $79.1 million to almost $96.5 million, due mostly to the company’s booming share price boosting the value of his stock awards.
The numbers were disclosed Tuesday afternoon in the company’s annual proxy statement, along with details on Microsoft board changes, shareholder proposals raising concerns about AI risks, and a request from the board for shareholders to approve a new stock plan.
Microsoft laid off more than 15,000 employees this year — one of the most aggressive rounds of cuts in its history — citing shifting priorities and the need for efficiency amid record spending on AI infrastructure. Wall Street reacted positively to the effort to rein in operating expenses.
Much of Nadella’s total compensation — about $84.2 million — is based on the performance of the company’s stock, which has risen more than 23% in the past year, at one point pushing Microsoft’s total market value briefly past $4 trillion.
Also announced in the proxy: Microsoft’s board nominated Walmart CFO John David Rainey as a new board member, to replace Carlos Rodriguez, current chair of the compensation committee, who is not seeking re-election.
The company’s 2025 fiscal year ended June 30. In evaluating Nadella’s performance, the board cited his work leading the expansion of the company’s AI infrastructure, Microsoft Copilot adoption and new security initiatives.
His cash incentive bonus was $9.56 million, up from the $5.2 million paid in 2024, after he requested a reduction. The proxy statement said the increase reflected strong financial results (117% of target) and a high operational assessment (151.67% of target).
For the first time, security was used as one measuring stick for Microsoft executive compensation, part of an effort by the company to appease regulators and lawmakers after a series of high-profile breaches. In its review, the board focused on Nadella’s role in attempting to address these issues through the implementation of its Secure Future Initiative.
In addition, Microsoft’s board is asking shareholders to approve a 2026 Stock Plan to replace the expiring 2017 plan, requesting authorization for up to 226 million new shares that it says is needed to continue granting equity awards for attracting and retaining talent.
Nadella recently appointed veteran executive Judson Althoff as CEO of Microsoft’s commercial business, a move designed to free Nadella to focus more intensely on long-term AI strategy and technology.
Microsoft’s annual meeting, held virtually, is slated for 8:30 a.m. Dec. 5.
A new Seattle startup is betting that artificial intelligence can take marketing off the plate of small business owners.
Omada.ai, founded earlier this year by longtime tech entrepreneur Pete Christothoulou, officially launched Tuesday with what it describes as an “AI marketing team” designed to handle the day-to-day digital marketing tasks for small and midsize business owners.
Backed by Crosslink Capital, HubSpot Ventures, and Seattle-based firm Ascend, the startup says its platform can plan, create, and optimize marketing campaigns automatically for less than $9 per day.
Instead of developing a single marketing “copilot” or dashboard, Omada’s approach is a set of coordinated AI agents — a marketing assistant, social media manager, designer, video producer, and more — that collaborate like a virtual team. Users interact through a simple chat interface, and the system handles tasks such as posting content, running ads, responding to customers, and tracking performance.
“Their agent-based architecture delivers a truly autonomous and proactive system that gives small business owners the access to capabilities and marketing expertise they’ve never had access to before,” Adam Coccari, managing director at HubSpot Ventures, said in a statement.
Omada’s pitch is that it acts less like another app and more like a full-service team — a “do-it-for-me” model rather than “do-it-yourself.” Its agents are built on proprietary infrastructure that coordinates specialized AI models for language, vision, and audio tasks. The company says the system learns each business’s tone and goals over time, continuously optimizing campaigns.
Omada enters an increasingly competitive space. A growing number of startups use generative AI to help businesses create content and automate tasks — including Seattle-area companies Gradial, Adora, and Forum3. Larger companies such as HubSpot, Canva, and Adobe have also embedded AI marketing tools into their small business offerings.
Christothoulou co-founded Omada with Siva Muthukumarasamy, a longtime engineering leader who was CTO at Peel Technologies, as well as Andrew Miller, a veteran marketing exec who worked at Xembly as head of user acquisition.
Omada marks Christothoulou’s latest foray into applying automation and data intelligence to the marketing world. He previously co-founded and ran Marchex, a Seattle-based advertising analytics company that went public in 2004 and helped pioneer digital call tracking for marketers.
Christothoulou served as CEO at Marchex until 2016 and later launched Xembly, a Seattle startup that developed an “AI chief of staff” to automate productivity tasks. The company shut down its consumer service last year.
In a competitive hiring environment, the ability to find exceptional talent that isn’t necessarily knocking down your door is hugely desired and not always easy to attain. That’s why so many companies these days are turning to AI-powered talent acquisition and management startups to help them mine for exceptional candidates.
One such startup, Findem, has secured $51 million in equity and debt funding, the company tells Crunchbase News exclusively.
The raise includes a $36 million Series C led by SLW (Silver Lake Waterman) with participation from Wing Venture Capital, Harmony Capital and Four Rivers Group, as well as $15 million in growth financing from JP Morgan. The financing brings Findem’s total funding since its 2019 inception to $105 million, with $90 million of that being equity, per the company.
Redwood City, California-based Findem’s mission is simple, even if its methods are not. It aims to transform how businesses “identify, attract and engage top talent.”
The startup uses what it calls 3D talent data (out of a dataset developed out of 1.6 trillion data points) that it combines with AI to automate “key parts of the talent lifecycle.” And those parts include building “top-of-funnel” pipelines of interested candidates, executive search and analyzing workforce and labor markets.
In an interview with Crunchbase News, co-founder and CEO Hari Kolam said that Findem’s user base surged by “about 100x” in the last 12 months and that the company is experiencing 3x year-over-year growth. Its enterprise customer base increased by 3x over the last year.
It has a user base of more than 12,000 customers, including from Adobe, Box, Medallia, Nutanix and RingCentral, Kolam said.
Currently, Findem operates under a SaaS business model, charging per seat. As it expands its agentic abilities, the company plans to add an outcome-based model as well, according to Kolam. It is not yet profitable.
Findem is just one of more than a dozen startups at the intersection of AI and recruitment globally that have raised venture capital in 2025. As of early September, global startup investment for startups in the HR, recruitment and employment categories totaled around $2.3 billion, per Crunchbase data. That puts funding on track for a year-over-year gain, even as investment remains at a fraction of the levels hit during the market peak, as charted below.
Watching Findem’s platform in action provides better insight into just how it helps companies zero in on the specific talent for which they’re searching.
Say a startup wants to hire a software engineer who has worked at a company from its early days until it raised a Series C funding round. But it also wants that engineer to have a GitHub profile that it can view. Or, say a company wants to hire competitive coders who have seen a successful exit, or a CFOs who drove a company from a negative operating margin to a positive one.
Findem’s software will allow you to filter for all those desired attributes.
The startup says it’s able to help companies recruit so specifically because its 3D data combines people and company data over time into a format suitable for AI analysis. It claims that the “continuously enhanced” 3D dataset is “exponentially larger and more factual” than traditional sources of candidate data, making it a powerful tool for deep insights and automated workflows.
Using the combination of the data and AI, Findem creates 3D profiles, also dubbed “enriched” profiles, for every candidate it helps surface. The goal of the profiles is to provide “a detailed and factual view” of an individual’s “professional journey and impact,”
So just where does all this data come from? Findem says it continuously leverages a language model to generate that 3D data from more than 100,000 sources that are chronologically gathered (from earliest to latest).
Those sources include LinkedIn, GitHub, Doximity, WordPress, personal websites, the U.S. Census Bureau, company funding announcements and IPO details, business models, more than 300 million patents and publications, over 5 million open datasets and ML projects, and over 200 million open source code repositories.
It also pulls applicant profile information from applicant tracking systems such as Workday, BambooHR, Bullhorn, Greenhouse, Jobvite and Lever, among others.
This comprehensive data pull is what helps set Findem apart, in Kolam’s view. Some other hiring tools rely on one-dimensional data from resumes or LinkedIn profiles, which, he argues, “give only a snapshot of someone’s career … without the context that reveals true potential.” Kolam contends that it takes “extensive manual effort” to verify and interpret the data.
“Just looking at a resume on paper really doesn’t come close to telling the whole story of how really qualified a candidate could be, or if they can really fit the criteria that a particular employer is looking for,” Kolam maintains.
Findem is primarily focused on North American customers who have users across the globe. It’s also expanding into Europe. The company has a second headquarters in Bangalore, India.
Kolam declined to reveal Findem’s valuation, saying only that it was “a significant up round and more than 2x” compared to its valuation when it raised a $17 million-plus Series B extension in December 2023.
Shawn O’Neill, managing partner at SLW, told Crunchbase News via email that his firm first got to know Kolam before Findem raised its Series B and then “tracked the company’s trajectory for some time.”
‘’What drew us to Findem wasn’t just the technology, it was the traction,” he said. “The team has achieved strong commercial momentum while tackling one of the most persistent challenges in HR — connecting data, insight and human potential in a way that actually drives business outcomes.”
But the technology didn’t hurt.
In O’Neill’s view, Findem’s main differentiator is its “data advantage … in a market where most companies are simply wrapping LLMs.”
“The depth and breadth of their 3D profiles and web-scale dataset are unlike anything else in the market,” he said. “The UX is excellent, but the magic is really in how the platform leverages that data — it makes finding and understanding people effortless. We use it ourselves and see the power firsthand.”
Illustration: Dom Guzman
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