Archiv der Kategorie: Disruption

The Woman who showed President Biden ChatGPT

and Helped Set the Course for AI

Arati Prabhakar has the ear of the US president and a massive mission: help manage AI, revive the semiconductor industry, and pull off a cancer moonshot.

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one day in March 2023, Arati Prabhakar brought a laptop into the Oval Office and showed the future to Joe Biden. Six months later, the president issued a sweeping executive order that set a regulatory course for AI.

This all happened because ChatGPT had stunned the world. In an instant it became very, very obvious that the United States needed to speed up its efforts to regulate the AI industry—and adopt policies to take advantage of it. While the potential benefits were unlimited (Social Security customer service that works!), so were the potential downsides, like floods of disinformation or even, in the view of some, human extinction. Someone had to demonstrate that to the president.

The job fell to Prabhakar, because she is the director of the White House Office of Science and Technology Policy and holds cabinet status as the president’s chief science and technology adviser; she’d already been methodically educating top officials about the transformative power of AI. But she also has the experience and bureaucratic savvy to make an impact with the most powerful person in the world.

Born in India and raised in Texas, Prabhakar has a PhD in applied physics from Caltech and previously ran two US agencies: the National Institute of Standards and Technology and the Department of Defense’s Advanced Research Projects Agency. She also spent 15 years in Silicon Valley as a venture capitalist, including as president of Interval Research, Paul Allen’s legendary tech incubator, and has served as vice president or chief technology officer at several companies.

Prabhakar assumed her current job in October 2022—just in time to have AI dominate the agenda—and helped to push out that 20,000-word executive order, which mandates safety standards, boosts innovation, promotes AI in government and education, and even tries to mitigate job losses. She replaced biologist Eric Lander, who had resigned after an investigation concluded that he ran a toxic workplace. Prabhakar is the first person of color and first woman to be appointed director of the office.

We spoke at the kitchen table of Prabhakar’s Silicon Valley condo—a simply decorated space that, if my recollection is correct, is very unlike the OSTP offices in the ghostly, intimidating Eisenhower Executive Office Building in DC. Happily, the California vibes prevailed, and our conversation felt very unintimidating—even at ease. We talked about how Bruce Springsteen figured into Biden’s first ChatGPT demo, her hopes for a semiconductor renaissance in the US, and why Biden’s war on cancer is different from every other president’s war on cancer. I also asked her about the status of the unfilled role of chief technology officer for the nation—a single person, ideally kind of geeky, whose entire job revolves around the technology issues driving the 21st century.

Steven Levy: Why did you sign up for this job?

Arati Prabhakar: Because President Biden asked. He sees science and technology as enabling us to do big things, which is exactly how I think about their purpose.

What kinds of big things?

The mission of OSTP is to advance the entire science and technology ecosystem. We have a system that follows a set of priorities. We spend an enormous amount on R&D in health. But both public and corporate funding are largely focused on pharmaceuticals and medical devices, and very little on prevention or clinical care practices—the things that could change health as opposed to dealing with disease. We also have to meet the climate crisis. For technologies like clean energy, we don’t do a great job of getting things out of research and turning them into impact for Americans. It’s the unfinished business of this country.

It’s almost predestined that you’d be in this job. As soon as you got your physics degree at Caltech, you went to DC and got enmeshed in policy.

Yeah, I left the track I was supposed to be on. My family came here from India when I was 3, and I was raised in a household where my mom started sentences with, “When you get your PhD and become an academic …” It wasn’t a joke. Caltech, especially when I finished my degree in 1984, was extremely ivory tower, a place of worship for science. I learned a tremendous amount, but I also learned that my joy did not come from being in a lab at 2 in the morning and having that eureka moment. Just on a lark, I came to Washington for, quote-unquote, one year on a congressional fellowship. The big change was in 1986, when I went to Darpa as a young program manager. The mission of the organization was to use science and technology to change the arc of the future. I had found my home.

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How did you wind up at Darpa?

I had written a study on microelectronics R&D. We were just starting to figure out that the semiconductor industry wasn’t always going to be dominated by the US. We worked on a bunch of stuff that didn’t pan out but also laid the groundwork for things that did. I was there for seven years, left for 19, and came back as director. Two decades later the portfolio was quite different, as it should be. I got to christen the first self-driving ship that could leave a port and navigate across open oceans without a single sailor on board. The other classic Darpa thing is to figure out what might be the foundation for new capabilities. I ended up starting a Biological Technologies Office. One of the many things that came out of that was the rapid development and distribution of mRNA vaccines, which never would have happened without the Darpa investment.

One difference today is that tech giants are doing a lot of their own R&D, though not necessarily for the big leaps Darpa was built for.

Every developed economy has this pattern. First there’s public investment in R&D. That’s part of how you germinate new industries and boost your economy. As those industries grow, so does their investment in R&D, and that ends up being dominant. There was a time when it was sort of 50-50 public-private. Now it’s much more private investment. For Darpa, of course, the mission is breakthrough technologies and capabilities for national security.

Are you worried about that shift?

It’s not a competition! Absolutely there’s been a huge shift. That private tech companies are building the leading edge LLMs today has huge implications. It’s a tremendous American advantage, but it has implications for how the technology is developed and used. We have to make sure we get what we need for public purposes.

Is the US government investing enough to make that happen?

I don’t think we are. We need to increase the funding. One component of the AI executive order is a National AI Research Resource. Researchers don’t have the access to data and computation that companies have. An initiative that Congress is considering, that the administration is very supportive of, would place something like $3 billion of resources with the National Science Foundation.

That’s a tiny percentage of the funds going into a company like OpenAI.

It costs a lot to build these leading-edge models. The question is, how do we have governance of advanced AI and how do we make sure we can use it for public purposes? The government has got to do more. We need help from Congress. But we also have to chart a different kind of relationship with industry than we’ve had in the past.

What might that look like?

Look at semiconductor manufacturing and the CHIPS Act.

We’ll get to that later. First let’s talk about the president. How deep is his understanding of things like AI?

Some of the most fun I’ve gotten on the job was working with the president and helping him understand where the technology is, like when we got to do the chatbot demonstrations for the president in the Oval Office.

What was that like?

Using a laptop with ChatGPT, we picked a topic that was of particular interest. The president had just been at a ceremony where he gave Bruce Springsteen the National Medal of Arts. He had joked about how Springsteen was from New Jersey, just across the river from his state, Delaware, and then he made reference to a lawsuit between those two states. I had never heard of it. We thought it would be fun to make use of this legal case. For the first prompt, we asked ChatGPT to explain the case to a first grader. Immediately these words start coming out like, “OK, kiddo, let me tell you, if you had a fight with someone …” Then we asked the bot to write a legal brief for a Supreme Court case. And out comes this very formal legal analysis. Then we wrote a song in the style of Bruce Springsteen about the case. We also did image demonstrations. We generated one of his dog Commander sitting behind the Resolute desk in the Oval Office.

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So what was the president’s reaction?

He was like, “Wow, I can’t believe it could do that.” It wasn’t the first time he was aware of AI, but it gave him direct experience. It allowed us to dive into what was really going on. It seems like a crazy magical thing, but you need to get under the hood and understand that these models are computer systems that people train on data and then use to make startlingly good statistical predictions.

There are a ton of issues covered in the executive order. Which are the ones that you sense engaged the president most after he saw the demo?

The main thing that changed in that period was his sense of urgency. The task that he put out for all of us was to manage the risks so that we can see the benefits. We deliberately took the approach of dealing with a broad set of categories. That’s why you saw an extremely broad, bulky, large executive order. The risks to the integrity of information from deception and fraud, risks to safety and security, risks to civil rights and civil liberties, discrimination and privacy issues, and then risks to workers and the economy and IP—they’re all going to manifest in different ways for different people over different timelines. Sometimes we have laws that already address those risks—turns out it’s illegal to commit fraud! But other things, like the IP questions, don’t have clean answers.

There are a lot of provisions in the order that must meet set deadlines. How are you doing on those?

They are being met. We just rolled out all the 90-day milestones that were met. One part of the order I’m really getting a kick out of is the AI Council, which includes cabinet secretaries and heads of various regulatory agencies. When they come together, it’s not like most senior meetings where all the work has been done. These are meetings with rich discussion, where people engage with enthusiasm, because they know that we’ve got to get AI right.

There’s a fear that the technology will be concentrated among a few big companies. Microsoft essentially subsumed one leading startup, Inflection. Are you concerned about this centralization?

Competition is absolutely part of this discussion. The executive order talks specifically about that. One of the many dimensions of this issue is the extent to which power will reside only with those who are able to build these massive models.

The order calls for AI technology to embody equity and not include biases. A lot of people in DC are devoted to fighting diversity mandates. Others are uncomfortable with the government determining what constitutes bias. How does the government legally and morally put its finger on the scale?

Here’s what we’re doing. The president signed the executive order at the end of October. A couple of days later, the Office of Management and Budget came out with a memo—a draft of guidance about how all of government will use AI. Now we’re in the deep, wonky part, but this is where the rubber meets the road. It’s that guidance that will build in processes to make sure that when the government uses AI tools it’s not embedding bias.

That’s the strategy? You won’t mandate rules for the private sector but will impose them on the government, and because the government is such a big customer, companies will adopt them for everyone?

That can be helpful for setting a way that things work broadly. But there are also laws and regulations in place that ban discrimination in employment and lending decisions. So you can feel free to use AI, but it doesn’t get you off the hook.

Have you read Marc Andreessen’s techno-optimist manifesto?

No. I’ve heard of it.

There’s a line in there that basically says that if you’re slowing down the progress of AI, you are the equivalent of a murderer, because going forward without restraints will save lives.

That’s such an oversimplified view of the world. All of human history tells us that powerful technologies get used for good and for ill. The reason I love what I’ve gotten to do across four or five decades now is because I see over and over again that after a lot of work we end up making forward progress. That doesn’t happen automatically because of some cool new technology. It happens because of a lot of very human choices about how we use it, how we don’t use it, how we make sure people have access to it, and how we manage the downsides.

“I’m trying to figure out if you’re going to write a bunch of nice research papers, or you’re gonna move the needle on cancer.”

How are you encouraging the use of AI in government?

Right now AI is being used in government in more modest ways. Veterans Affairs is using it to get feedback from veterans to improve their services. The Social Security Administration is using it to accelerate the processing of disability claims.

Those are older programs. What’s next? Government bureaucrats spend a lot of time drafting documents. Will AI be part of that process?

That’s one place where you can see generative AI being used. Like in a corporation, we have to sort out how to use it responsibly, to make sure that sensitive data aren’t being leaked, and also that it’s not embedding bias. One of the things I’m really excited about in the executive order is an AI talent surge, saying to people who are experts in AI, “If you want to move the world, this is a great time to bring your skills to the government.” We published that on AI.gov.

How far along are you in that process?

We’re in the matchmaking process. We have great people coming in.

OK, let’s turn to the CHIPS Act, which is the Biden administration’s centerpiece for reviving the semiconductor industry in the US. The legislation provides more than $50 billion to grow the US-based chip industry, but it was designed to spur even more private investment, right?

That story starts decades ago with US dominance in semiconductor manufacturing. Over a few decades the industry got globalized, then it got very dangerously concentrated in one geopolitically fragile part of the world. A year and a half ago the president got Congress to act on a bipartisan basis, and we are crafting a completely different way to work with the semiconductor industry in the US.

Different in what sense?

It won’t work if the government goes off and builds its own fabs. So our partnership is one where companies decide what products are the right ones to build and where we will build them, and government incentives come on the basis of that. It’s the first time the US has done that with this industry, but it’s how it was done elsewhere around the world.

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Some people say it’s a fantasy to think we can return to the day when the US had a significant share of chip and electronics manufacturing. Obviously, you feel differently.

We’re not trying to turn the clock back to the 1980s and saying, “Bring everything to the US.” Our strategy is to make sure that we have the robustness we need for the US and to make sure we’re meeting our national security needs.

The biggest grant recipient was Intel, which got $8 billion. Its CEO, Pat Gelsinger, said that the CHIPS Act wasn’t enough to make the US competitive, and we’d need a CHIPS 2. Is he right?

I don’t think anyone knows the answer yet. There’s so many factors. The job right now is to build the fabs.

As the former head of Darpa, you were part of the military establishment. How do you view the sentiment among employees of some companies, like Google, that they should not take on military contracts?

It’s great for people in companies to be asking hard questions about how their work is used. I respect that. My personal view is that our national security is essential for all of us. Here in Silicon Valley, we completely take for granted that you get up every morning and try to build and fund businesses. That doesn’t happen by accident. It’s shaped by the work that we do in national security.

Your office is spearheading what the president calls a Cancer Moonshot. It seems every president in my lifetime had some project to cure cancer. I remember President Nixon talking about a war on cancer. Why should we believe this one?

We’ve made real progress. The president and the first lady set two goals. One is to cut the age-adjusted cancer death rate in half over 25 years. The other is to change the experience of people going through cancer. We’ve come to understand that cancer is a very complex disease with many different aspects. American health outcomes are not acceptable for the most wealthy country in the world. When I spoke to Danielle Carnival, who leads the Cancer Moonshot for us—she worked for the vice president in the Obama administration—I said to her, “I’m trying to figure out if you’re going to write a bunch of nice research papers or you’re gonna move the needle on cancer.” She talked about new therapies but also critically important work to expand access to early screening, because if you catch some of them early, it changes the whole story. When I heard that I said, “Good, we’re actually going to move the needle.”

Don’t you think there’s a hostility to science in much of the population?

People are more skeptical about everything. I do think that there has been a shift that is specific to some hot-button issues, like climate and vaccines or other infectious disease measures. Scientists want to explain more, but they should be humble. I don’t think it’s very effective to treat science as a religion. In year two of the pandemic, people kept saying that the guidance keeps changing, and all I could think was, “Of course the guidance is changing, our understanding is changing.” The moment called for a little humility from the research community rather than saying, “We’re the know-it-alls.”

Is it awkward to be in charge of science policy at a time when many people don’t believe in empiricism?

I don’t think it’s as extreme as that. People have always made choices not just based on hard facts but also on the factors in their lives and the network of thought that they are enmeshed in. We have to accept that people are complex.

Part of your job is to hire and oversee the nation’s chief technology officer. But we don’t have one. Why not?

That had already been a long endeavor when I came on board. That’s been a huge challenge. It’s very difficult to recruit, because those working in tech almost always have financial entanglements.

I find it hard to believe that in a country full of great talent there isn’t someone qualified for that job who doesn’t own stock or can’t get rid of their holdings. Is this just a low priority for you?

We spent a lot of time working on that and haven’t succeeded.

Are we going to go through the whole term without a CTO?

I have no predictions. I’ve got nothing more than that.

There are only a few months left in the current term of this administration. President Biden has given your role cabinet status. Have science and technology found their appropriate influence in government?

Yes, I see it very clearly. Look at some of the biggest changes—for example, the first really meaningful advances on climate, deploying solutions at a scale that the climate actually notices. I see these changes in every area and I’m delighted.

Source: https://www.wired.com/story/arati-prabhakar-ostp-biden-science-tech-adviser/

Microsoft’s Recall technology bears resemblance to George Orwells 1984 dystopia in several key factors

Microsoft’s Recall technology, an AI tool designed to assist users by automatically reminding them of important information and tasks, bears resemblance to George Orwell’s „1984“ dystopia in several key aspects:

1. Surveillance and Data Collection:
– 1984: The Party constantly monitors citizens through telescreens and other surveillance methods, ensuring that every action, word, and even thought aligns with the Party’s ideology.
– Recall Technology: While intended for productivity, Recall collects and analyzes large amounts of personal data, emails, and other communications to provide reminders. This level of data collection can raise concerns about privacy and the potential for misuse or unauthorized access to personal information.

2. Memory and Thought Control:
– 1984: The Party manipulates historical records and uses propaganda to control citizens‘ memories and perceptions of reality, essentially rewriting history to fit its narrative.
– Recall Technology: By determining what information is deemed important and what reminders to provide, Recall could influence users‘ focus and priorities. This selective emphasis on certain data could subtly shape users‘ perceptions and decisions, akin to a form of soft memory control.

3. Dependence on Technology:
– 1984: The populace is heavily reliant on the Party’s technology for information, entertainment, and even personal relationships, which are monitored and controlled by the state.
– Recall Technology: Users might become increasingly dependent on Recall to manage their schedules and information, potentially diminishing their own capacity to remember and prioritize tasks independently. This dependence can create a vulnerability where the technology has significant control over daily life.

4. Loss of Personal Autonomy:
– 1984: Individual autonomy is obliterated as the Party dictates all aspects of life, from public behavior to private thoughts.
– Recall Technology: Although not as extreme, the automation and AI-driven suggestions in Recall could erode personal decision-making over time. As users rely more on technology to dictate their actions and reminders, their sense of personal control and autonomy may diminish.

5. Potential for Abuse:
– 1984: The totalitarian regime abuses its power to maintain control over the population, using technology as a tool of oppression.
– Recall Technology: In a worst-case scenario, the data collected by Recall could be exploited by malicious actors or for unethical purposes. If misused by corporations or governments, it could lead to scenarios where users‘ personal information is leveraged against them, echoing the coercive control seen in Orwell’s dystopia.

While Microsoft’s Recall technology is designed with productivity in mind, its potential implications for privacy, autonomy, and the influence over personal information draw unsettling parallels to the controlled and monitored society depicted in „1984.“

Why Elon Musk should consider integrating OpenAI’s ChatGPT „GPT-4o“ as the operating system for a brand new Tesla SUV – Here are the five biggest advantages to highlight

  1. Revolutionary User Interface and Experience:
    • Natural Language Interaction: GPT-4o’s advanced natural language processing capabilities allow for seamless, conversational interaction between the driver and the vehicle. This makes controlling the vehicle and accessing information more intuitive and user-friendly.
    • Personalized Experience: The AI can learn from individual driver behaviors and preferences, offering tailored suggestions for routes, entertainment, climate settings, and more, enhancing overall user satisfaction and engagement. 
  2. Enhanced Autonomous Driving and Safety:
    • Superior Decision-Making: GPT-4o can significantly enhance Tesla’s autonomous driving capabilities by processing and analyzing vast amounts of real-time data to make better driving decisions. This improves the safety, reliability, and efficiency of the vehicle’s self-driving features.
    • Proactive Safety Features: The AI can provide real-time monitoring of the vehicle’s surroundings and driver behavior, offering proactive alerts and interventions to prevent accidents and ensure passenger safety.
  3. Next-Level Infotainment and Connectivity:
    • Smart Infotainment System: With GPT-4o, the SUV’s infotainment system can offer highly intelligent and personalized content recommendations, including music, podcasts, audiobooks, and more, making long journeys more enjoyable.
    • Seamless Connectivity: The AI can integrate with a wide range of apps and services, enabling drivers to manage their schedules, communicate, and access information without distraction, thus enhancing productivity and convenience.
  4. Continuous Improvement and Future-Proofing:
    • Self-Learning Capabilities: GPT-4o continuously learns and adapts from user interactions and external data, ensuring that the vehicle’s performance and features improve over time. This results in an ever-evolving user experience that keeps getting better.
    • Over-the-Air Updates: Regular over-the-air updates from OpenAI ensure that the SUV remains at the forefront of technology, with the latest features, security enhancements, and improvements being seamlessly integrated.
  5. Market Differentiation and Brand Leadership:
    • Innovative Edge: Integrating GPT-4o positions Tesla’s new SUV as a cutting-edge vehicle, showcasing the latest in AI and automotive technology. This differentiates Tesla from competitors and strengthens its reputation as a leader in innovation.
    • Enhanced Customer Engagement: The unique AI-driven features and personalized experiences can drive stronger customer engagement and loyalty, attracting tech-savvy consumers and enhancing the overall brand image.

By leveraging these advantages, Tesla can create a groundbreaking SUV that not only meets but exceeds consumer expectations, setting new standards for the automotive industry and reinforcing Tesla’s position as a pioneer in automotive and AI technology.

Real World Use Cases for Apples Vision Pro + Version 2 – with the new operating system ChatGPT „GPT-4o“

The integration of advanced AI like OpenAI’s GPT-4o into Apple’s Vision Pro + Version 2 can significantly enhance its vision understanding capabilities.
Here are ten possible use cases:

1. Augmented Reality (AR) Applications:
– Interactive AR Experiences: Enhance AR applications by providing real-time object recognition and interaction. For example, users can point the device at a historical landmark and receive detailed information and interactive visuals about it.
– AR Navigation: Offer real-time navigation assistance in complex environments like malls or airports, overlaying directions onto the user’s view.

2. Enhanced Photography and Videography:
– Intelligent Scene Recognition: Automatically adjust camera settings based on the scene being captured, such as landscapes, portraits, or low-light environments, ensuring optimal photo and video quality.
– Content Creation Assistance: Provide suggestions and enhancements for capturing creative content, such as framing tips, real-time filters, and effects.

3. Healthcare and Medical Diagnosis:
– Medical Imaging Analysis: Assist in analyzing medical images (e.g., X-rays, MRIs) to identify potential issues, providing preliminary diagnostic support to healthcare professionals.
– Remote Health Monitoring: Enable remote health monitoring by analyzing visual data from wearable devices to track health metrics and detect anomalies.

4. Retail and Shopping:
– Virtual Try-Ons: Allow users to virtually try on clothing, accessories, or cosmetics using the device’s camera, enhancing the online shopping experience.
– Product Recognition: Identify products in stores and provide information, reviews, and price comparisons, helping users make informed purchasing decisions.

5. Security and Surveillance:
– Facial Recognition: Enhance security systems with facial recognition capabilities for authorized access and threat detection.
– Anomaly Detection: Monitor and analyze security footage to detect unusual activities or potential security threats in real-time.

6. Education and Training:
– Interactive Learning: Use vision understanding to create interactive educational experiences, such as identifying objects or animals in educational content and providing detailed explanations.
– Skill Training: Offer real-time feedback and guidance for skills training, such as in sports or technical tasks, by analyzing movements and techniques.

7. Accessibility and Assistive Technology:
– Object Recognition for the Visually Impaired: Help visually impaired users navigate their surroundings by identifying objects and providing auditory descriptions.
– Sign Language Recognition: Recognize and translate sign language in real-time, facilitating communication for hearing-impaired individuals.

8. Home Automation and Smart Living:
– Smart Home Integration: Recognize household items and provide control over smart home devices. For instance, identifying a lamp and allowing users to turn it on or off via voice commands.
– Activity Monitoring: Monitor and analyze daily activities to provide insights and recommendations for improving household efficiency and safety.

9. Automotive and Driver Assistance:
– Driver Monitoring: Monitor driver attentiveness and detect signs of drowsiness or distraction, providing alerts to enhance safety.
– Object Detection: Enhance autonomous driving systems with better object detection and classification, improving vehicle navigation and safety.

10. Environmental Monitoring:
– Wildlife Tracking: Use vision understanding to monitor and track wildlife in natural habitats for research and conservation efforts.
– Pollution Detection: Identify and analyze environmental pollutants or changes in landscapes, aiding in environmental protection and management.

These use cases demonstrate the broad potential of integrating advanced vision understanding capabilities into Apple’s Vision Pro + Version 2, enhancing its functionality across various domains and providing significant value to users.

Why Apple uses ChatGPT 4o as its new operating system for Apples Vision Pro + Version 2

Apple’s Vision Pro + Version 2, utilizing OpenAI’s ChatGPT „GPT-4o“ as the operating system offers several compelling marketing benefits. Here are the key advantages to highlight:

1. Revolutionary User Interface:
– Conversational AI: GPT-4o’s advanced natural language processing capabilities allow for a conversational user interface, making interactions with Vision Pro + more intuitive and user-friendly.
– Personalized Interactions: The AI can provide highly personalized responses and suggestions based on user behavior and preferences, enhancing user satisfaction and engagement.

2. Unmatched Productivity:
– AI-Driven Multitasking: GPT-4o can manage and streamline multiple tasks simultaneously, significantly boosting productivity by handling scheduling, reminders, and real-time information retrieval seamlessly.
– Voice-Activated Efficiency: Hands-free operation through advanced voice commands allows users to multitask efficiently, whether they are working, driving, or engaged in other activities.

3. Advanced Accessibility:
– Inclusive Design: GPT-4o enhances accessibility with superior voice recognition, understanding diverse speech patterns, and offering multilingual support, making Vision Pro + more accessible to a broader audience.
– Adaptive Assistance: The AI can provide context-aware assistance to users with disabilities, further promoting inclusivity and ease of use.

4. Superior Integration and Ecosystem:
– Apple Ecosystem Synergy: GPT-4o integrates seamlessly with other Apple devices and services, offering a cohesive and interconnected user experience across the Apple ecosystem.
– Unified User Experience: Users can enjoy a consistent and unified experience across all their Apple devices, enhancing brand loyalty and overall user satisfaction.

5. Enhanced Security and Privacy:
– Secure Interactions: Emphasize GPT-4o’s robust security measures to ensure user data privacy and protection, leveraging OpenAI’s commitment to ethical AI practices.
– Trustworthy AI: Highlight OpenAI’s dedication to ethical AI usage, reinforcing user trust in the AI-driven functionalities of Vision Pro +.

6. Market Differentiation:
– Innovative Edge: Position Vision Pro + as a cutting-edge product that stands out in the market due to its integration with GPT-4o, setting it apart from competitors.
– Leadership in AI: Showcase Apple’s leadership in technology innovation by leveraging OpenAI’s state-of-the-art advancements in AI.

7. Future-Proofing:
– Continuous Innovation: Regular updates from OpenAI ensure that Vision Pro + remains at the forefront of AI technology, with continuous improvements and new features.
– Scalable Solutions: The AI platform’s scalability allows for future enhancements, ensuring the product remains relevant and competitive over time.

8. Customer Engagement:
– Proactive Support: GPT-4o can offer proactive customer support and real-time problem-solving, leading to higher customer satisfaction and loyalty.
– Engaging Experiences: The AI can create engaging and interactive experiences, making the device more enjoyable and useful for daily activities.

9. Enhanced Creativity:
Creative Assistance: GPT-4o can assist users with creative tasks such as content creation, brainstorming, and project management, providing valuable support for both personal and professional use.
– Innovative Features: Highlight the unique AI-driven features that empower users to explore new creative possibilities, enhancing the appeal of Vision Pro +.

10. Efficient Learning and Adaptation:
– User Learning: GPT-4o continuously learns from user interactions, becoming more efficient and effective over time, offering a progressively improving user experience.
– Adaptive Technology: The AI adapts to user needs and preferences, ensuring that the device remains relevant and useful in a variety of contexts.

By leveraging these benefits, Apple can market the Vision Pro + Version 2 as a pioneering product that offers unparalleled user experience, productivity, and innovation, driven by the advanced capabilities of OpenAI’s GPT-4o.

He Emptied an Entire Crypto Exchange Onto a Thumb Drive. Then He Disappeared

Source: https://www.wired.com/story/faruk-ozer-turkey-crypto-fraud/

Faruk Özer just started a 11,196-year prison sentence. Did he almost get away with the biggest heist in Turkey’s history, or was it all just a big misunderstanding?

Faruk Fatih Özer stood in front of a passport control officer at Istanbul Airport, a line of impatient travelers queuing behind him. He pulled his face mask below his chin for the security camera. Surely he was nervous. The 27-year-old had unruly black hair, a boy-band face, and a patchy beard. Normally he overcompensated for his callow features by dressing in a pressed three-piece suit. But this spring day he wore black trainers and a navy-blue sweater hastily pulled over a white polo shirt, as if he had dressed in a dash. A small backpack was slung over his right shoulder. He looked like someone who could have been going on a last-minute day trip—or someone planning to never come back. At 5:57 pm on April 20, 2021, the guard stamped his Turkish passport and Özer shuffled through the crowd to Gate C, a flash drive containing a rumored $2 billion in crypto stashed in his belongings.

After Özer’s plane reached Tirana, Albania, at 9:24 that night, he checked into the Mondial, a popular 4-star business hotel in the capital’s commercial district. A couple of days later, he looked at his social media accounts. A mob was very angry with him: Customers couldn’t access their money on the exchange Thodex, where he was founder and CEO, and people were accusing him of absconding with their funds.

Özer posted a public letter to his company’s website and his social accounts. “I feel compelled to make this statement in order to respond urgently to these allegations,” he wrote. The accusations weren’t true, he said. Thodex—which had nearly half a million investors and $500 million in daily trade volume—was investigating what Özer claimed was a suspected cyberattack that caused “an abnormal fluctuation in the company account.” Assets would be frozen for five days while Thodex resolved the issue. This was terribly bad timing for the big business deal he said he was en route to make: selling the company, or so he had told some employees and his brother and sister before he left. All would be made right. “There will be no victims,” he promised. “I personally declare that I will return to Turkey within a few days and ensure that the facts are revealed in cooperation with judicial authorities and that I will do my best to prevent users from suffering.” Of course, there was this possibility too: He was in the midst of pulling off the biggest heist in Turkey’s history.

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Before dawn the day after Özer posted the letter, police squads fanned out across Istanbul and public prosecutors opened an investigation. Law enforcement arrested 62 people, including Thodex employees at all levels of the company—and Özer’s older brother and sister, Güven and Serap. Interpol issued a red notice, a request for law enforcement worldwide to find and “provisionally arrest” Özer pending his extradition to Turkey. Search teams deployed across Albania, Montenegro, Kosovo, and North Macedonia. There were reported sightings of the dark-haired young man across Tirana, rumors that he had gone to a poultry farm, that an executive from the Albanian football league was sheltering him. Soon, the Albanian police arrested people accused of aiding and abetting him. But no one seemed to know exactly where Özer was.

Özer had vanished at a particularly precarious time in crypto’s annals: In the weeks leading up to his disappearance, so-called rug pulls—when a cryptocurrency exchange or altcoin developer absconds with investors’ funds—had crypto investors around the globe flabbergasted. The CEO of Mirror Trading International, a crypto trading company based in South Africa, defrauded users of more than $1 billion, then skipped town; TurtleDex, an anonymous decentralized finance storage project on Binance, reportedly vanished with $2.4 million; another decentralized finance project, Meerkat, reportedly fleeced investors out of $31 million (of which they paid back 95 percent). Blockchain analysis firm Chainalysis ranked rug pulls as the primary scam of 2021, accounting for 37 percent of all cryptocurrency scam revenue that year, up from 1 percent the year before.

Thodex was at the top of that roster, and nearly every major outlet from Bloomberg to Newsweek published headlines like “Turkish Crypto Exchange Goes Bust as Founder Flees Country” and “Turkish Cryptocurrency Founder Faruk Fatih Özer Seen Fleeing Country With Suspected $2 Billion From Investors.” CoinGeek called it “the biggest scam in the digital asset industry in 2021.” The New York Times’ headline read, “Possible Cryptocurrency Fraud Is Another Blow to Turkey’s Financial Stability.” In Turkey, the country I now call home, people were reeling: For years, crypto had been built up—largely by Özer but by others too—as a way out of economic volatility. Now it seemed like just another way to lose your life savings. But something felt off to me, like the whole story wasn’t being told.

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ILLUSTRATION: PRINCESS HIDIR

Faruk was born in February 1994, the youngest of three. He was inseparable from his brother, Güven, and sister, Serap. They grew up camping, playing video games, and cooking together. Friends always pointed out their shared sense of humor. His parents ran a print and copy shop in the city of Kocaeli, down the street from their house. They were observant Muslims who gave their children meaningful names: “trust” (Güven), “mirage” (Serap), and “the one who distinguishes between right and wrong” (Faruk).

Kocaeli is an industrial port town about 100 kilometers east of Istanbul surrounded by a checkerboard of tobacco and sugar beet fields, petrochemical plants, and paper mills. Roman emperors once lived there, and their crumbling fortress walls still wind through the landscape. After the Ottoman Empire collapsed, Kocaeli became a manufacturing boomtown, and its residents muscled the newly minted Republic of Turkey into the Industrial Revolution.

When Özer was born, Turkey’s economy was in a tailspin. A fragile financial system, irresponsible borrowing, and political corruption had triggered a brief period of triple-digit inflation. The lira’s volatility threatened the savings of its entire population. So many people moved their domestic assets to foreign-currency deposits that, by the end of the year, an astonishing 50 percent of bank deposits in Turkey were in a foreign currency. The year before, that figure was just 1 percent.

That same month Özer was born, a charismatic orator with a sympathetic gaze and push-broom mustache began campaigning through Istanbul’s streets in a paisley kipper tie. Recep Tayyip Erdoğan railed against the secular elite who had led the country to near economic collapse. A devout Muslim, he walked the streets of his home neighborhood, Kasımpaşa, a hardscrabble district where he grew up selling simit, or sesame bread, promising reform. In an upset election, he coasted into the role of mayor of Istanbul.

Around the same time, two Turkish business moguls launched Turkcell, the nation’s first mobile communication system. (This was a year and a half before the same technology was released in the US.) By 2003, Erdoğan was elected prime minister, kicking off a decade of unprecedented growth that foreign observers called Turkey’s “Silent Revolution.” In a turn away from his predecessors, he governed through the lens of a businessman, inaugurating a massive building boom across the country and ultimately wrangling Turkey’s rampant inflation. His pro-business rhetoric boosted the middle class and set Turkey on a path to European Union membership.

Özer also caught the spirit of entrepreneurship at an early age. As a teenager in the mid-aughts, he worked after-school shifts at his parents’ print shop. “Ever since I was a child, I wanted to do my own business, no matter what sector it was,” he said. At the end of his second year in high school, he decided that further study would not lead him to that dream, so he dropped out.

By 2013, Turkey’s gross domestic product had nearly tripled, the lira hovered just above the dollar, and the country was negotiating entry into the EU. BtcTurk, Turkey’s first crypto exchange (and reportedly the world’s fourth), was preparing to launch. Then, in May of that year, a group of activists gathered at Gezi Park in Istanbul to protest plans to redevelop it into a shopping mall with Ottoman-era architecture. They bridled not only at the loss of green space but also at the glorification of Turkey’s Islamist past in a society that called itself secular. Police brutally cracked down on the protesters, sparking a nationwide movement. Within weeks, more than 3 million people had taken part in the demonstrations, their frustration now encompassing the growing authoritarianism of Erdoğan’s government. Thousands were injured, and at least five died. Özer had just turned 19. In the following years, Erdoğan tossed a record number of journalists in jail and censored the internet, and foreign investors recoiled.

Around that time, Ismail H. Polat, an expert in engineering, information tech, and new media, was the first person to cover crypto on his YouTube channel. Now a lecturer at Istanbul’s Kadir Has University, the way he tells it, crypto was about trying to be financially free. In those early days, he says, “it was not the coin, but the spirit.” (After all, bitcoin was worth only $77 at the time.) For young people who felt that Erdoğan had pulled the rug out from under them, whether they knew it explicitly or not, crypto was a new way to protest.

At the same time, Özer’s generation was watching as tech startups were taking off around the world. Facebook had bought Instagram for $1 billion, and that spurred entrepreneurs to begin churning out apps. A lot of them were gaming-focused; Candy Crush brought in $1.5 billion in revenue in 2013. The Özers took note.

By then, Turkcell had become one of the world’s largest companies. Turkey’s mobile infrastructure and smartphone adoption rate became one of the fastest growing in the world. Polat credits this as the foundation for what came next: The dream began to shift from mere employment to entrepreneurship. Güven cofounded a company called Inline Yazılım; Faruk started one called Inline Teknoloji a few years later and another called Game Bridge after that. The brothers figured out how to crank out chintzy apps—cut-and-paste washboard abs for Instagram photos (pre-vanity-filters era) and addictive gambling games. “I started to sell almost every product that I thought could make a profit on the internet,” he told me. “This is how I took my first step into business life.”

By 2017, 14 years into Erdoğan’s rule, Turkey’s economy had come full circle. Erdoğan’s unorthodox economic policies—repeatedly cutting interest rates—were supposed to raise investment and make Turkey less dependent on foreign powers. Instead they led the country into an economic crisis; the value of the lira hit the skids, and after a failed coup attempt in 2016 people figured it would only get worse. Just as they had 23 years earlier, citizens began searching for places to shelter their money. Voilà, 2017 was also the first year bitcoin’s value shot sky high, from $9,000 to $20,000. Global trade volume also skyrocketed from $99 million to $16 billion.

Being early investors in tech wasn’t something that had historically been available to the average person in Turkey. The instant millionaires and billionaires and unicorns pretty much lived elsewhere. Now, Faruk Özer saw a possibility. People in Turkey could shelter their money in what was clearly going to be the next big tech boom. But the biggest opportunity wasn’t in trading coins—it was in running a cryptocurrency exchange. Exchanges collect people’s money and, for a commission, invest it; that gives people who don’t have the time or skills to invest directly into the blockchain a pathway to crypto. Users who go through an exchange don’t even have their own digital wallets; their money is stored until they withdraw it. (Hence the industry warning: “Not your wallet, not your coins.”)

“During conversations with friends, we realized the deficiencies in the cryptocurrency exchange sector in Turkey and that the market was open to new players,” Özer said. There were no regulations on running a cryptocurrency exchange; Özer could open one easier than he could open a simit stand. He could become rich. Everybody might become rich. So at the age of 23, Özer founded Thodex with 40,000 lira—around $11,100 at the time—of his own money. Soon, Serap began working for Özer as a company accountant; Güven too seemed to be around a lot, but his ties were unofficial.

Using a playbook from Silicon Valley, Özer began spreading the gospel of crypto around Turkey. By this time, there were a few other notable exchanges, but Özer gave crypto a face and a ubiquitous presence. He put up ads on billboards and at bus stations; he installed Turkey’s first bitcoin ATM in a luxury mall in Istanbul’s posh Nişantaşı neighborhood, more as a stunt than anything. In a TV interview, he explained, “People realized that it is a technology that they can turn into cash at any time. One of the most important helping tools in the spread of this perception was undoubtedly bitcoin ATMs.” He aired television commercials pushing Thodex, featuring a dozen Turkish celebrities, including actress Pınar Deniz and pop star Simge. That caught the attention of Turkey’s middle and upper classes.

Soon, Özer was ingratiated into the upper echelons of Turkish society. He was invited to sit on the board of organizations such as Blockchain Turkey, a respected crypto nonprofit in Istanbul, alongside the country’s biggest bankers. He was attending private meetings with Turkey’s highest ministers. He appeared regularly on news channels and in tech blogs. At one point, Thodex was hacked for many millions of lira (supposedly $14 million US), allegedly from an IP address in China. Özer says he compensated the customers’ losses out of his own pocket and reported the theft to the Istanbul Public Prosecutor’s cybercrime unit. Experts from regulating agencies audited Thodex’s financial infrastructure—and Özer claimed they gave the company a clean bill of health. (Though that couldn’t be confirmed.)

Istanbul began to feel like Las Vegas. Dazzling billboards and banners hawking crypto coins were everywhere. Backgammon cafés, where old men have been drinking tea and talking politics for centuries, buzzed with crypto gossip. Signs appeared on barber shops and storefronts advising customers that they could pay with bitcoin. Bitcoin booths opened in the Grand Bazaar, next to the gold-trading stalls where people once sheltered their money when empires collapsed. By 2020, Turkey had more people using cryptocurrency than almost anywhere else in the world. Istanbul had concluded a sweeping economic metamorphosis, from a historic trading post to an information technology leader to one of the top mobile gaming centers on the planet. And finally to a cryptocurrency capital. Crypto “transformed into a national mindset” among young people, Polat says.

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ILLUSTRATION: PRINCESS HIDIR

He added, though: “Money is an agreement between a government and its society in terms of national unity. But on the other side of the medallion, if everyone leaves the fiat—if everyone leaves the social agreement of their nation—it could derail the world.”

In 2020, Thodex moved into a sprawling high-rise in Kadıköy, a chockablock district on the Asian side of Istanbul. It had a big open floor plan and views of the Marmara Sea. Thodex was now the fastest-growing and second-largest cryptocurrency exchange in Turkey. In the office, Özer had a reputation for keeping to himself. His 85 employees, most of whom applied for their jobs at Thodex through ads on platforms like LinkedIn, say they were paid on time, received bonuses, got their time-off requests approved quickly, and enjoyed the Thodex-branded coffee mugs and other swag. But the young man in charge clearly had some bluster. One former employee told me Özer’s avatar on the in-house messaging system was an image of Leonardo DiCaprio’s character from The Wolf of Wall Street.

Like any CEO, Özer wanted to make it as easy as possible for customers to spend money on his products. He started a customer service center called Thodex Academy that offered an introductory guide to cryptocurrencies for new investors. He offered scandalously cheap commission rates, so low that industry experts were stumped as to how Thodex could be making a profit. The company also allowed people to buy crypto via credit cards; at times that was money an investor didn’t really have, but the hope was that a coin’s value would go up faster than the interest accrued on the card. (The US Securities and Exchange Commission was alerting the public to avoid crypto exchanges promoting the practice.)

Özer was also intent on taking Thodex global. In 2020, the company secured a money services business license from the US Treasury’s Financial Crimes Enforcement Network. Özer endlessly paraded it around like a trophy, touting it in news interviews, on Thodex’s website, and on social media. Like the audit a few years earlier, the license seemed to prove that Thodex was legit—that it had passed all the record-keeping and anti-money-laundering checks, that it was a company people could trust.

By March 2021, 16 percent of people in Turkey were using crypto, putting the nation in the top five for crypto use, along with Nigeria, Vietnam, the Philippines, and Peru—all countries with struggling economies.

The value of the lira, meanwhile, was at a historic low—as was Erdoğan’s approval rating. In April, Turkey’s Central Bank announced a ban on the use of crypto for purchases or services, set to go into effect later that month. This sent a shock wave across the global crypto market. (Bitcoin’s value dropped 4.6 percent.) The central bank issued a statement saying bitcoin could “cause non-recoverable losses.” It also said bitcoin’s use could undermine confidence in the lira. The bank and Erdoğan promised more regulations to follow. Özer’s entire empire was under threat.

Özer had, for a few months, been running a PR blitz for Thodex’s fourth birthday, giving away iPhones, PlayStations, a Porsche Panamera, and Dogecoins. It worked—sort of. Thodex’s trade volume reportedly climbed to $538 million. At the same time, the price of bitcoin soared again, reaching a high of $63,000. If there ever was a ripe moment to flee with Thodex’s cold wallet, this was it. Days later, Özer stood at the Istanbul Airport, a ticket to Tirana in his hands.

As “wanted” fliers with Özer’s picture went up on telephone poles across Albania, Erdoğan held a Q&A with college students from all over the country and talked about his “war” with crypto. The outlook for Thodex’s 400,000 customers was grim. So many Thodex investors had put all of their money into Özer’s company—and all of their spouses’, parents’, in-laws’, and kids’ money too. They had taken out loans and lines of credit to buy more crypto.

One investor named Mahmut—$100,000 lost—had been using Thodex for three years and was in the final stages of buying a house and car for his family. (Like so many victims, he didn’t want to use his full name, because of a heavy burden of shame.) Mahmut had tried to withdraw his money—a mixture of savings and loans—in the days before the collapse, but it never came through. He attempted suicide three times. When his 2-year-old was diagnosed with autism, Mahmut took a job as a security guard at a storage facility to help pay for services for his son. At least two other investors were reported to have died by suicide.

Then there were friends of the family; Güven and Serap had also recruited customers. One was a former colleague of Güven’s; they had worked together through an advertising agency. Güven personally made a Thodex account for this colleague. Over time the two became friends, calling each other on birthdays. About Güven, the friend told me, “He’s a nice person who likes to joke around.”

But when he saw the maintenance notice on Thodex’s website, he called Güven to ask about withdrawing his money. “His phone was off, and would never turn on again after that,” he said. “I believed in it. It had public credibility. It had a license. It had an office. Tax registration number. Employees. It checked out as a reputable company,” he said. He lost $7,900—but, worse than that, he said, “I trusted it so much that I recommended Thodex to many friends, and they all lost their money too.”

Victims formed support groups online. People were devastated about losing their life savings, but they were also plagued by the mystery of what happened to Özer; they talked about Özer haunting them in recurring dreams. People questioned how Özer—this self-made 27-year-old who didn’t quite fit the profile of a tech bro or a transnational cybercriminal—could engineer the biggest theft Turkey had ever seen all by himself.

Two months after the collapse, Sedat Peker, an infamous, self-exiled mafia boss turned YouTuber, inflamed the situation with a series of cryptic posts on his YouTube channel and Twitter, accusing Turkey’s interior minister of collusion and profiteering. (The minister denied it, but photos of Özer and him at a 2019 meeting fueled a heap of conspiracy theories.) At one point before he disappeared, Özer apparently stayed in pandemic lockdown with the son of a member of Turkey’s parliament as they prepared to launch a new digital wallet service called Hoppara. A lawyer representing some victims speculated on a phone call with me that Thodex was actually a money laundering scheme, that Özer was just a puppet, and some other power was pulling the strings. Others I spoke with echoed this theory, and more than a few people strongly cautioned me to stop my reporting. Soon I started receiving anonymous threats.

Some vigilante programmers tried to trace the blockchain to see if they could find out if this was the work of one individual, a criminal network, or a vulnerable system. (As the joke about people who steal money and blame it on hackers goes, “I lost my bitcoin in a boating accident.”) They came up short. While others looked for the money, I decided to search for Özer. He reminded me of the young founders I used to interview in San Francisco when I was covering tech in 2013—ambitious, naive, and at times loose with ethics. Also, what aspiring fugitive would say where they were absconding to and then post letters from the road? So I booked a ticket for Tirana, the last place Özer was seen.

Two days later my apartment in Istanbul was ransacked in a way that seemed like a stern message. The wood on my front door was split open by force, and the contents of every drawer and cabinet were tossed around. The lock to a safe was snipped open, its contents—expensive camera equipment and cash—remained untaken. Even a laptop on the entry table appeared untouched. I canceled the trip.

Nearly one year after he disappeared, there was no sign of Özer or the money. Rumors flew that he was no longer in Albania. He could be in a hotel in Montenegro, a yacht off Kosovo, or a tropical hideaway in Thailand, people said; or he could be locked in a Tirana basement. People told me they thought he was dead, resting in a shallow grave.

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ILLUSTRATION: PRINCESS HIDIR

Güven and Serap idled in prison, awaiting their brother’s capture so that their trial could begin. In March 2022, Istanbul’s public prosecutor and Turkey’s Financial Crimes Board released the findings of their investigation. They painted Özer as a rapacious mountebank who used star-powered pitchmen to dupe people into funneling their savings into his criminal organization. The report asserted that, in 2017, Özer founded Thodex with the intention of operating it as a crime ring for money laundering and that every employee was a willing part of it—from the top executives down to the call-center workers who were placating customers and the social media managers who lured victims with promotions and sweepstakes.

The report traced scads of transactions through a shadowy web of financial accounts all allegedly under Özer’s control. It said that about $8 million in Thodex-held assets had been cashed out in gold bricks in Malta a few weeks before Özer’s disappearance. Özer might have escaped, but his employees and family were facing the possibility of spending many lifetimes in prison.

Just as hope of Özer’s resurfacing dwindled, the drama took a new twist: Sevgi Erarslan, a lawyer whom Özer’s father had hired initially to pay the victims back, then to represent his son in absentia, introduced herself to Twitter via a shocking tweet: She said she would refund any victims of Thodex if they legally withdrew their complaint against her client.

A wave of questions followed—where was this money coming from, and was Özer not just alive but in contact with Erarslan while wanted by Interpol? I sent her an email asking if I could speak with Özer. To my surprise, she called me immediately, saying that Özer was willing to be in touch. We began an occasional correspondence, though I often wondered if he really was the person answering my questions, if he was even really alive.

The trial went forward under a swirl of confusion and skepticism—about Özer’s whereabouts, about the large payments now being sent to victims from undisclosed sources, and about Erarslan’s legitimacy as his official lawyer. Of the 62 people who had been initially arrested, 21 were charged. On an overcast July morning, officers escorted those defendants into Istanbul’s beige brutalist courthouse, just a short walk from the Thodex headquarters where all the people on trial had once worked.

Güven wore a dark olive blazer, and his mustache was trimmed into a neat chevron. Serap sat with her back turned to him, cloaked in a trench coat pulled over a midnight black abaya. Özer’s lawyer, Erarslan, wore Turkey’s satin lawyer cloak and carried a Louis Vuitton purse. The sound of dozens of handcuffs being unhitched echoed through the cavernous room.

When Güven’s name was called, he stood, flanked by two crew-cut court officers sporting the dark irony of his name emblazoned on their vests (“Güvenlik” means “security”). He told the court that he had no official ties to Thodex. “I only come to the office for tea with my siblings,” he said. He explained how Thodex subcontracted his company for advertising services, and added: “My brother asked me to give him my personal account that I wasn’t using, so I let him use it.” Saying that his brother told him he was going to Albania to try to sell the company, he corroborated Faruk’s claim.

Without looking at her brother, Serap rose and explained that she was only an accountant; her job was to forward documents to an accounting firm. Like Güven, she had given Faruk her personal crypto exchange and bank account information at his request. “I didn’t think he was going to use it,” Serap said. “I can’t say that my brother opened those fake accounts in my name. Identity theft is common; it could have been anyone.” The head judge twirled her pen as Serap spoke, her voice sputtering and cracking. “I have been suffering from both physiological and psychological problems. I am so worn out physically.” When she began losing her breath and stumbling over her sentences, the judge excused her from the stand.

Özer’s siblings and former employees told the court about Özer’s request for access to scores of other accounts where he could personally initiate trades. To the prosecutors—and probably plenty of other people listening—this looked a lot like evidence of money laundering.

When the presiding judge called Özer’s name, Erarslan stood to testify on his behalf, waving a handwritten power of attorney, claiming to be able to represent the missing CEO. The judge shut her down, saying that the document was invalid.

One of the victims’ lawyers stood up and shouted that Erarslan should be kicked out of the courtroom. Erarslan shouted back. The room erupted into a circus. Judges and lawyers, victims and defendants—all volleyed slurs and accusations at one another. One of the defendants’ lawyers yelled at the judge: “You need to know how crypto works. To have a fair trial you have to understand how crypto works.”

Late one August night in 2022, a few days before the defendants back in Istanbul were to receive their verdicts, it became clear that Özer was very much alive. The Albanian police had been tailing a BMW X5 that they suspected was Özer’s and traced the car to an elegant two-story art-deco villa in the hills of Vlorë, a ritzy tourist town on what’s billed as the Albanian Riviera.

Just outside the driveway, they pulled over and waited. At 2:30 am, two people left the house and got into the BMW and started to pull out of the driveway. The cops stopped it, arrested two young men, and a police squad in bulletproof vests and balaclavas stormed the villa. In video footage taken of the raid, Özer, shirtless and wearing red shorts, looks shocked. He stumbles around the room. On a wall behind him, liquor bottles line the shelves. Three women were in the villa too. An officer grabbed Özer, and the young CEO, now wearing a white polo shirt, was handcuffed, escorted down a short flight of stairs, shuttled into an unmarked white van, and driven into the night.

At the police station in Tirana, Özer told the cops that he had been “hiding in the streets of poor neighborhoods.” He added that he got around by bus. “Since I grew up in a poor neighborhood in Turkey, I know how to deal with poor people. I looked for a house to rent by asking people on the streets.” He also said that he had been living off of $10,000 in cash he had brought into the country and money that was occasionally being wired to him—plus, some crypto trading. He confessed that he planned on eventually escaping to Greece.

For nearly a year, Özer sat in an Albanian prison, appealing his extradition back to Turkey. “We are trying to explain to the court that if I am extradited, I do not have a chance to get a fair trial,” he wrote to me. He called the trial “tragicomic.” His appeal was unsuccessful. In June 2023 he arrived at Istanbul Superior Court. His head was shaved; he still had the scruffy beard. The once buoyant tech founder was now staring down a prison sentence that could carry an astonishing 43,000 years.

When he finally stood before the court, judges allowed Özer to tell his story. Leaping at the chance, he powered up a presentation full of images and graphics, something not dissimilar to a pitch deck. Then he started to read from a 60-page soliloquy, his defense attorney clicking through slides at his instruction.

“I did not defraud anyone, I did not smuggle money abroad, I did not establish or manage a criminal organization,” he said with both frustration and sincerity in his voice. “I started a company.” He recognized people got hurt. Then he began trying to make a case that prosecutors (and the media) had simply, mistakenly, criminalized a business failure.

He mentioned a litany of those supposed problems: getting hacked, an “atomic bomb” of Dogecoin panic buying and selling that drained Thodex’s funds after the central bank announced its drastic curtailing of crypto. “The day I bought my ticket to go to Albania, most of the Dogecoin withdrawal requests could no longer be met,” he said. In his retelling, Özer arrived at the conclusion that the company itself was worth more than the funds in its wallet, so he saw only one way out: selling it. He started, he said, shopping it around in Istanbul, Italy, and the Balkans. He eventually set up meetings with several potential buyers in Albania. He didn’t give their names. Özer showed the courtroom a news clip with the widely circulated headline that he had fled with $2 billion and defrauded 400,000 people. “As soon as this news started to appear, both investors were off the table … I had no other chance to cover the losses of the stolen cryptocurrencies.” He pointed out that the indictment estimated the damages were closer to $43 million and the number of official claimants against him totaled 2,027 people.

Then his testimony took a Shakespearean turn. “This was also done with the aim of killing me or having me killed,” he said without flinching. “To announce to the world that a 27-year-old man with $2 billion in his pocket is alone in a country with high crime rates like Albania.” That, coupled with a red notice from Interpol on his back, and his face on wanted posters around Albania, made him fear for his life. So, he said, he bought a tent and took a taxi to the southern Albanian coastline. He had hoped to ride out the nightmare camping alone. “I needed time for the truth to emerge,” he told the room.

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ILLUSTRATION: PRINCESS HIDIR

He told the court that he grew his beard down to his clavicle, shaved his head. He said that when he learned about the prosecutor’s indictment—that his brother and sister were charged with fraud, that they would stand trial in a month, and that without Özer they would most likely take the blame for Thodex’s fall and spend the rest of their lives in prison—he had a wild idea: If every claimant were paid back, did a crime ever really happen? He did, in fact, have the Thodex cold wallet on him, he told the judges, though he claims to not remember how much was in it. He asked Erarslan to help him pay back the roughly 2,000 plaintiffs who had lost their money.

And they did, in part. In total, while on the run, he paid approximately 185 million lira ($10 million at the time) to more than 1,000 claimants. As Özer tells it, when the cold wallet was empty, he threw it into the Ionian Sea.

When he addressed his use of other people’s accounts to trade crypto—an action at the center of the case—he started to sound defiant and a little condescending: “Startup founders take all responsibilities, as the nature of startups requires,” he said. He underscored that they had no authority in the company and no access to these accounts. “There is no lawlessness or irregularity. Moreover, I am neither the first nor the last nor the only person to arbitrage the cryptocurrency market.”

Near the end of his address, Özer’s frustrations seemed to turn to bitterness and hubris. He faced the judges and said it was “absurd to think that the IQ level of the person who made such a stupid escape plan” was the same as that of a criminal mastermind allegedly capable of deceiving Turkish financial regulators for four years. “I am smart enough to lead any institution on earth,” Özer said. Then he had Erarslan pull up an image of a cartoon mocking the court. Visibly annoyed, the chief judge ordered him to remove it.

The verdict came quietly on a balmy Thursday in September 2023 to an almost empty courtroom. Özer stood and solemnly read the lyrics from a Turkish folk song, “The End of the Road Is Visible.”

The chief judge handed Güven, Serap, and Özer the same sentence: 11,196 years in prison—for establishing and managing a criminal organization and laundering assets. Most of the other defendants were released. It was the longest sentence in Turkey’s history, handed out the month before the Republic’s centennial.

Faruk Fatih Özer became a poster child of crypto crimes, but he also became an accidental representation of a particular economic era—and the lengths people will go through to flee it. To the Turkish regime, he was not so much an opponent as an unfortunate product of flawed economic policies. In that light, the draconian sentence is punishment not only for a crime but also for shining a spotlight on decades of embarrassing failures, ones that were made clear to the entire country the day that Özer disappeared.

So perhaps it’s no surprise that Turkey remains a haven for cryptocurrencies. In the year after Thodex went bust, inflation in the country hit a 24-year high of 85.5 percent. Prices for goods nearly doubled—and so did the percentage of Turks who owned bitcoin, ether, and other currencies. In terms of trade volume, the country ranks fourth globally, behind the US, the UK, and India. After decades of watching their currency devalue, their businesses and nest eggs get scrambled, the Turkish people aren’t going to pass up the dream so easily. Earlier this year, the country’s finance minister said the government was working to finalize new regulations on crypto, “to make this field safer and to eliminate possible risks.” So although Özer picked a fight with an authoritarian regime and lost—whether because he believed too fully in the gospel of decentralization, because he was a naive kid, because he was a cynical hustler, or some combination of all three—the flames of economic revolution that he helped fan aren’t going out anytime soon.

Source: https://www.wired.com/story/faruk-ozer-turkey-crypto-fraud/

WhatsApp Chats Will Soon Work With Other Encrypted Messaging Apps

Source: https://www.wired.com/story/whatsapp-interoperability-messaging/

New EU rules mean WhatsApp and Messenger must be interoperable with other chat apps. Here’s how that will work.

WhatsApp icon seen with many colorful icons

A frequent annoyance of contemporary life is having to shuffle through different messaging apps to reach the right person. Messenger, iMessage, WhatsApp, Signal—they all exist in their own silos of group chats and contacts. Soon, though, WhatsApp will do the previously unthinkable for its 2 billion users: allow people to message you from another app. At least, that’s the plan.

For about the past two years, WhatsApp has been building a way for other messaging apps to plug themselves into its service and let people chat across apps—all without breaking the end-to-end encryption it uses to protect the privacy and security of people’s messages. The move is the first time the chat app has opened itself up this way, and it potentially offers greater competition.

It isn’t a shift entirely of WhatsApp’s own making. In September, European, lawmakers designated WhatsApp parent Meta as one of six influential “gatekeeer” companies under its sweeping Digital Markets Act, giving it six months to open its walled garden to others. With just a few weeks to go before that time is up, WhatsApp is detailing how its interoperability with other apps may work.

“There’s real tension between offering an easy way to offer this interoperability to third parties whilst at the same time preserving the WhatsApp privacy, security, and integrity bar,” says Dick Brouwer, an engineering director at WhatsApp who has worked on Meta rolling out encryption to its Messenger app. “I think we’re pretty happy with where we’ve landed.”

Interoperability in both WhatsApp and Messenger—as dictated by Europe’s rules—will initially focus on text messaging, sending images, voice messages, videos, and files between two people. Calls and group chats will come years down the line. Europe’s rules apply only to messaging services, not traditional SMS messaging. “One of the core requirements here, and this is really important, is for users for this to be opt-in,” says Brouwer. “I can choose whether or not I want to participate in being open to exchanging messages with third parties. This is important, because it could be a big source of spam and scams.”

WhatsApp users who opt in will see messages from other apps in a separate section at the top of their inbox. This “third-party chats” inbox has previously been spotted in development versions of the app. “The early thinking here is to put a separate inbox, given that these networks are very different,” Brouwer says. “We cannot offer the same level of privacy and security,” he says. If WhatsApp were to add SMS, it would use a separate inbox as well, although there are no plans to add it, he says.

Overall, the idea behind interoperability is simple. You shouldn’t need to know what messaging app your friends or family use to get in touch with them, and you should be able to communicate from one app to another without having to download both. In an ideal interoperable world, you could, for example, use Apple’s iMessage to chat with someone on Telegram. However, for apps with millions or billions of users, making this a reality isn’t straightforward—encrypted messaging apps use their own configurations and different protocols and have different standards when it comes to privacy.

Despite WhatsApp working on its interoperability plan for more than a year, it will still take some time for third-party chats to hit people’s apps. Messaging companies that want to interoperate with WhatsApp or Messenger will need to sign an agreement with the company and follow its terms. The full details of the plan will be published in March, Brouwer says; under EU laws, the company will have several months to implement it.

Brouwer says Meta would prefer if other apps use the Signal encryption protocol, which its systems are based upon. Other than its namesake app and the Meta-owned messengers, the Signal Protocol is publicly disclosed as being used in Google Messages and Skype. To send messages, third-party apps will need to encrypt content using the Signal Protocol and then package it into message stanzas in the eXtensible Markup Language (XML). When receiving messages, apps will need to connect to WhatsApp’s servers.

“We think that the best way to deliver this approach is through a solution that is built on WhatsApp’s existing client-server architecture,” Brouwer says, adding it has been working with other companies on the plans. “This effectively means that the approach that we’re trying to take is for WhatsApp to document our client- server protocol and letting third-party clients connect directly to our infrastructure and exchange messages with WhatsApp clients.”

There is some flexibility to WhatsApp interoperability. Meta’s app will also allow other apps to use different encryption protocols if they can “demonstrate” they reach the security standards that WhatsApp outlines in its guidance. There will also be the option, Brouwer says, for third-party developers to add a proxy between their apps and WhatsApp’s server. This, he says, could give developers more “flexibility” and remove the need for them to use WhatsApp’s client-server protocols, but it also “increases the potential attack vectors.”

So far, it is unclear which companies, if any, are planning to connect their services to WhatsApp. WIRED asked 10 owners of messaging or chat services—including Google, Telegram, Viber, and Signal—whether they intend to look at interoperability or had worked with WhatsApp on its plans. The majority of companies didn’t respond to the request for comment. Those that did, Snap and Discord, said they had nothing to add. (The European Commission is investigating whether Apple’s iMessage meets the thresholds to offer interoperability with other apps itself. The company did not respond to a request for comment. It has also faced recent challenges in the US about the closed nature of iMessage.)

Matthew Hodgson, the cofounder of Matrix, which is building an open source standard for encryption and operates the messaging app Element, confirms that his company has worked with WhatsApp on interoperability in an “experimental” way but that he cannot say any more due to signing a nondisclosure agreement. In a talk last weekend, Hodgson demonstrated “hypothetical” architectures for ways that Matrix could connect to the systems of two gatekeepers that don’t use the same encryption protocols.

Meanwhile, Julia Weis, a spokesperson for the Swiss messaging app Threema, says that while WhatsApp did approach it to discuss its interoperability plans, the proposed system didn’t meet Threema’s security and privacy standards. “WhatsApp specifies all the protocols, and we’d have no way of knowing what actually happens with the user data that gets transferred to WhatsApp—after all, WhatsApp is closed source,” Weis says. (WhatsApp’s privacy policy states how it uses people’s data.)

When the EU first announced that messaging apps may have to work together in early 2022, many leading cryptographers opposed the idea, saying it adds complexity and potentially introduces more security and privacy risks. Carmela Troncoso, an associate professor at the Swiss university École Polytechnique Fédérale de Lausanne, who focuses on security and privacy engineering, says interoperability moves could potentially lead to different power relationships between companies, depending on how they are implemented.

“This move for interoperability will, on the one hand, open the market, but also maybe close the market in the sense that now the bigger players are going to have more decisional power,” Troncoso says. “Now, if the big player makes a move and you want to continue being interoperable with this big player, because your users are hooked up to this, you’re going to have to follow.”

While the interoperability of encrypted messaging apps may be possible, there are some fundamental challenges about how the systems will work in the real world. How much of a problem spam and scamming will be across apps is largely unknown until people start using interoperable setups. There are also questions about how people will find each other across different apps. For instance, WhatsApp uses your phone number to interact and message other people, while Threema randomly generates eight-digit IDs for people’s accounts. Linking up with WhatsApp “could de-anonymize Threema users,” Weis, the Threema spokesperson says.

Meta’s Brouwer says the company is still working on the interoperability features and the level of support it will make available for companies wanting to integrate with it. “Nobody quite knows how this works,” Brouwer says. “We have no idea what the demand is.” However, he says, the decision was made to use WhatsApp’s existing architecture to run interoperability, as it means that it can more easily scale up the system for group chats in the future. It also reduces the potential for people’s data to be exposed to multiple servers, Brouwer says.

Ultimately, interoperability will evolve over time, and from Meta’s perspective, Brouwer says, it will be more challenging to add new features to it quickly. “We don’t believe interop chats and WhatsApp chats can evolve at the same pace,” he says, claiming it is “harder to evolve an open network” compared to a closed one. “The second you do something different—than what we know works really well—you open up a wormhole of security, privacy issues, and complexity that is always going to be much bigger than you think it is.”

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Inside Apple’s Big Plan to Bring Generative AI to All Its Devices

Apple was caught flat-footed when ChatGPT and other AI tools took the technology industry by storm. But the company is now preparing its response and plans to develop features for its full range of devices. Also: The future of the Mac comes into focus, a cheaper Apple Pencil debuts, and the Vision Pro gets closer.

One of the most intense and widespread endeavors at Apple Inc. right now is its effort to respond to the AI frenzy sweeping the technology industry.

The company has some catching up to do. Apple largely sat on the sidelines when OpenAI’s ChatGPT took off like a rocket last year. It watched as Google and Microsoft Corp. rolled out generative AI versions of their search engines, which spit out convincingly human-like responses to users’ queries. Microsoft also updated its Windows apps with smarter assistants, and Amazon.com Inc. unveiled an AI-enhanced overhaul of Alexa.

All the while, the only noteworthy AI release from Apple was an improved auto-correct system in iOS 17.

Now, Chief Executive Officer Tim Cook says that Apple has been working on generative AI technology for years. But I can tell you in no uncertain terms that Apple executives were caught off guard by the industry’s sudden AI fever and have been scrambling since late last year to make up for lost time.

“There’s a lot of anxiety about this and it’s considered a pretty big miss internally,” a person with knowledge of the matter told Power On.

 

As I first reported in July, the company built its own large language model called Ajax and rolled out an internal chatbot dubbed “Apple GPT” to test out the functionality. The critical next step is determining if the technology is up to snuff with the competition and how Apple will actually apply it to its products.

Apple’s senior vice presidents in charge of AI and software engineering, John Giannandrea and Craig Federighi, are spearheading the effort. On Cook’s team, they’re referred to as the “executive sponsors” of the generative AI push. Eddy Cue, the head of services, is also involved, I’m told. The trio are now on course to spend about $1 billion per year on the undertaking.

Giannandrea is overseeing development of the underlying technology for a new AI system, and his team is revamping Siri in a way that will deeply implement it. This smarter version of Siri could be ready as soon as next year, but there are still concerns about the technology and it may take longer for Apple’s AI features to spread across its product line.

Federighi’s software engineering group, meanwhile, is adding AI to the next version of iOS. There’s an edict to fill it with features running on the company’s large language model, or LLM, which uses a flood of data to hone AI capabilities. The new features should improve how both Siri and the Messages app can field questions and auto-complete sentences, mirroring recent changes to competing services.

 

Apple’s software engineering teams are also looking at integrating generative AI into development tools like Xcode, a move that could help app developers write new applications more quickly. That would bring it in line with services like Microsoft’s GitHub Copilot, which offers auto-complete suggestions to developers while they write code.

And Cue’s organization is pushing to add AI to as many apps as possible. The group is exploring new features for Apple Music, including auto-generated playlists (this is something Spotify rolled out earlier this year in partnership with OpenAI), as well as the company’s productivity apps.

Craig Federighi and Eddy Cue.Photographer: David Paul Morris/Bloomberg

Cue’s team is examining how generative AI can be used to help people write in apps like Pages or auto-create slide decks in Keynote. Again, this is similar to what Microsoft has already launched for its Word and PowerPoint apps. Apple is also testing generative AI for internal customer service apps within its AppleCare group, I’ve previously reported.

One debate going on internally is how to deploy generative AI: as a completely on-device experience, a cloud-based setup or something in between. An on-device approach would work faster and help safeguard privacy, but deploying Apple’s LLMs via the cloud would allow for more advanced operations.

 

The on-device strategy also makes it harder for Apple to update its technology and adapt to a fast-changing industry. With that in mind, I wouldn’t be surprised if the company adopts a combined approach: using on-device processing for some features and the cloud for more advanced tasks.

When it comes to getting this right, the stakes are high. Generative AI has quickly become much more than a buzzword and will be central to the next several decades of computing. Apple knows it can’t afford to take a back seat.

Source: https://www.bloomberg.com/news/newsletters/2023-10-22/what-is-apple-doing-in-ai-revamping-siri-search-apple-music-and-other-apps-lo1ffr7p?embedded-checkout=true

Elon Musk’s challenge: Stay ahead of the competition

DETROIT, Feb 24 (Source: https://www.reuters.com/technology/elon-musks-challenge-stay-ahead-competition-2023-02-24/) – Elon Musk will confront a critical challenge during Tesla’s Investor Day on March 1: Convincing investors that even though rivals are catching up, the electric-vehicle pioneer can make another leap forward to widen its lead.

Tesla Inc (TSLA.O) was the No. 1 EV maker worldwide in 2022, but China’s BYD (002594.SZ) and others are closing the gap fast, according to a Reuters analysis of global and regional EV sales data provided by EV-volumes.com.

In fact, BYD passed Tesla in EV sales last year in the Asia-Pacific region, while the Volkswagen Group (VOWG_p.DE) has been the EV leader in Europe since 2020.

While Tesla narrowed VW’s lead in Europe, the U.S. automaker surrendered ground in Asia-Pacific as well as its home market as the competition heats up.

Reuters Graphics
Reuters Graphics

The most significant challenges to Tesla are coming from established automakers and a group of Chinese EV manufacturers. Several U.S. EV startups that hoped to ride Tesla’s coattails are struggling, including luxury EV maker Lucid (LCID.O), whose shares plunged 16% on Thursday after disappointing sales and financial results.

Over the next two years, rivals including General Motors Co (GM.N), Ford Motor Co (F.N), Mercedes-Benz (MBGn.DE), Hyundai Motor (005380.KS) and VW will unleash scores of new electric vehicles, from a Chevrolet priced below $30,000 to luxury sedans and SUVs that top $100,000.

On Wednesday, Mercedes used Silicon Valley as the backdrop for a lengthy presentation on how Mercedes models of the near-future will immerse their owners in rich streams of entertainment and productivity content, delivered through „hyperscreens“ that stretch across the dashboard and make the rectangular screens in Teslas look quaint. Executives also emphasized that only Mercedes has an advanced, Level 3 partially automated driving system approved for use in Germany, with approval pending in California.

In China, Tesla has had to cut prices on its best-selling models under growing pressure from domestic Chinese manufacturers including BYD, Geely Automobile’s (0175.HK) Zeekr brand and Nio (9866.HK).

China’s EV makers could get another boost if Chinese battery maker CATL (300750.SZ) follows through on plans to heavily discount batteries used in their vehicles.

Musk has said he will use the March 1 event to outline his „Master Plan Part 3“ for Tesla.

In the nearly seven years since Musk published his „Master Plan Part Deux“ in July 2016, Tesla pulled ahead of established automakers and EV startups in most important areas of electric vehicle design, digital features and manufacturing.

Tesla’s vehicles offered features, such as the ability to navigate into a parking space or make rude sounds, that other vehicles lacked.

Tesla’s then-novel vertically integrated battery and vehicle production machine helped achieve higher profit margins than most established automakers – even as bigger rivals lost money on their EVs.

Fast-forward to today, and Tesla’s „Full Self Driving Beta“ automated driving is still classified by the company and federal regulators as a „Level 2“ driver assistance system that requires the human motorist to be ready to take control at all times. Such systems are common in the industry.

Tesla earlier this month was compelled by federal regulators to revise its FSD software under a recall order.

Tesla has established a wide lead over its rivals in manufacturing technology – an area where it was struggling when Musk put forward the last installment of his „Master Plan.“

Now, rivals are copying the company’s production technology, buying some of the same equipment Tesla uses. IDRA, the Italian company that builds huge presses to form large one-piece castings that are the building blocks of Tesla vehicles, said it is now getting orders from other automakers.

Musk has told investors that Tesla can keep its lead in EV manufacturing costs. The company has promised investors that on March 1 they „will be able to see our most advanced production line“ in Austin, Texas.

„Manufacturing technology will be our most important long-term strength,” Musk told analysts in January. Asked if Tesla could make money on a vehicle that sold in the United States for $25,000 to $30,000 – the EV industry’s Holy Grail – Musk was coy.

„I’d probably be asking the same question,“ he said. „But we would be jumping the gun on future announcements.“

Source: https://www.reuters.com/technology/elon-musks-challenge-stay-ahead-competition-2023-02-24/

The ‘Enshittification’ of TikTok by

Cory Doctorow

Or how, exactly, platforms die.
TikTok logo on the facade of the TikTok headquarters building in Culver City California
Photograph: AaronP/Getty Images
 

Source: https://www.wired.com/story/tiktok-platforms-cory-doctorow/#intcid=_wired-verso-hp-trending_3ec533db-7676-4610-993a-21551c443ddb_popular4-1

Here is how platforms die: First, they are good to their users; then they abuse their users to make things better for their business customers; finally, they abuse those business customers to claw back all the value for themselves. Then, they die.

I call this enshittification, and it is a seemingly inevitable consequence arising from the combination of the ease of changing how a platform allocates value, combined with the nature of a „two-sided market,“ where a platform sits between buyers and sellers, hold each hostage to the other, raking off an ever-larger share of the value that passes between them.

When a platform starts, it needs users, so it makes itself valuable to users. Think of Amazon: For many years, it operated at a loss, using its access to the capital markets to subsidize everything you bought. It sold goods below cost and shipped them below cost. It operated a clean and useful search. If you searched for a product, Amazon tried its damndest to put it at the top of the search results.

 

This was a hell of a good deal for Amazon’s customers. Lots of us piled in, and lots of brick-and-mortar retailers withered and died, making it hard to go elsewhere. Amazon sold us ebooks and audiobooks that were permanently locked to its platform with DRM, so that every dollar we spent on media was a dollar we’d have to give up if we deleted Amazon and its apps. And Amazon sold us Prime, getting us to pre-pay for a year’s worth of shipping. Prime customers start their shopping on Amazon, and 90 percent of the time, they don’t search anywhere else.

That tempted in lots of business customers—marketplace sellers who turned Amazon into the „everything store“ it had promised from the beginning. As these sellers piled in, Amazon shifted to subsidizing suppliers. Kindle and Audible creators got generous packages. Marketplace sellers reached huge audiences and Amazon took low commissions from them.

This strategy meant that it became progressively harder for shoppers to find things anywhere except Amazon, which meant that they only searched on Amazon, which meant that sellers had to sell on Amazon. That’s when Amazon started to harvest the surplus from its business customers and send it to Amazon’s shareholders. Today, Marketplace sellers are handing more than 45 percent of the sale price to Amazon in junk fees. The company’s $31 billion „advertising“ program is really a payola scheme that pits sellers against each other, forcing them to bid on the chance to be at the top of your search.

 

Searching Amazon doesn’t produce a list of the products that most closely match your search, it brings up a list of products whose sellers have paid the most to be at the top of that search. Those fees are built into the cost you pay for the product, and Amazon’s „Most Favored Nation“ requirement for sellers means that they can’t sell more cheaply elsewhere, so Amazon has driven prices at every retailer.

 

Search Amazon for „cat beds“ and the entire first screen is ads, including ads for products Amazon cloned from its own sellers, putting them out of business (third parties have to pay 45 percent in junk fees to Amazon, but Amazon doesn’t charge itself these fees). All told, the first five screens of results for „cat bed“ are 50 percent ads.

This is enshittification: Surpluses are first directed to users; then, once they’re locked in, surpluses go to suppliers; then once they’re locked in, the surplus is handed to shareholders and the platform becomes a useless pile of shit. From mobile app stores to Steam, from Facebook to Twitter, this is the enshittification lifecycle.

This is why—as Cat Valente wrote in her magisterial pre-Christmas essay—platforms like Prodigy transformed themselves overnight, from a place where you went for social connection to a place where you were expected to “stop talking to each other and start buying things.”

This shell-game with surpluses is what happened to Facebook. First, Facebook was good to you: It showed you the things the people you loved and cared about had to say. This created a kind of mutual hostage-taking: Once a critical mass of people you cared about were on Facebook, it became effectively impossible to leave, because you’d have to convince all of them to leave too, and agree on where to go. You may love your friends, but half the time you can’t agree on what movie to see and where to go for dinner. Forget it.

Then, it started to cram your feed full of posts from accounts you didn’t follow. At first, it was media companies, whom Facebook preferentially crammed down its users‘ throats so that they would click on articles and send traffic to newspapers, magazines, and blogs. Then, once those publications were dependent on Facebook for their traffic, it dialed down their traffic. First, it choked off traffic to publications that used Facebook to run excerpts with links to their own sites, as a way of driving publications into supplying full-text feeds inside Facebook’s walled garden.

This made publications truly dependent on Facebook—their readers no longer visited the publications‘ websites, they just tuned into them on Facebook. The publications were hostage to those readers, who were hostage to each other. Facebook stopped showing readers the articles publications ran, tuning The Algorithm to suppress posts from publications unless they paid to „boost“ their articles to the readers who had explicitly subscribed to them and asked Facebook to put them in their feeds.

Now, Facebook started to cram more ads into the feed, mixing payola from people you wanted to hear from with payola from strangers who wanted to commandeer your eyeballs. It gave those advertisers a great deal, charging a pittance to target their ads based on the dossiers of non-consensually harvested personal data they’d stolen from you.

Sellers became dependent on Facebook, too, unable to carry on business without access to those targeted pitches. That was Facebook’s cue to jack up ad prices, stop worrying so much about ad fraud, and to collude with Google to rig the ad market through an illegal program called Jedi Blue.

 

Today, Facebook is terminally enshittified, a terrible place to be whether you’re a user, a media company, or an advertiser. It’s a company that deliberately demolished a huge fraction of the publishers it relied on, defrauding them into a „pivot to video“ based on false claims of the popularity of video among Facebook users. Companies threw billions into the pivot, but the viewers never materialized, and media outlets folded in droves.

But Facebook has a new pitch. It claims to be called Meta, and it has demanded that we live out the rest of our days as legless, sexless, heavily surveilled low-poly cartoon characters. It has promised companies that make apps for this metaverse that it won’t rug them the way it did the publishers on the old Facebook. It remains to be seen whether they’ll get any takers. As Mark Zuckerberg once candidly confessed to a peer, marveling at all of his fellow Harvard students who sent their personal information to his new website, „TheFacebook“:

I don’t know why.

They “trust me”

Dumb fucks.

Once you understand the enshittification pattern, a lot of the platform mysteries solve themselves. Think of the SEO market, or the whole energetic world of online creators who spend endless hours engaged in useless platform Kremlinology, hoping to locate the algorithmic tripwires, which, if crossed, doom the creative works they pour their money, time, and energy into. 

Working for the platform can be like working for a boss who takes money out of every paycheck for all the rules you broke, but who won’t tell you what those rules are because if he told you that, then you’d figure out how to break those rules without him noticing and docking your pay. Content moderation is the only domain where security through obscurity is considered a best practice.

The situation is so dire that organizations like Tracking Exposed have enlisted an human army of volunteers and a robot army of headless browsers to try to unwind the logic behind the arbitrary machine judgments of The Algorithm, both to give users the option to tune the recommendations they receive, and to help creators avoid the wage theft that comes from being shadow banned.

But what if there is no underlying logic? Or, more to the point, what if the logic shifts based on the platform’s priorities? If you go down to the midway at your county fair, you’ll spot some poor sucker walking around all day with a giant teddy bear that they won by throwing three balls in a peach basket.

The peach-basket is a rigged game. The carny can use a hidden switch to force the balls to bounce out of the basket. No one wins a giant teddy bear unless the carny wants them to win it. Why did the carny let the sucker win the giant teddy bear? So that he’d carry it around all day, convincing other suckers to put down five bucks for their chance to win one.

The carny allocated a giant teddy bear to that poor sucker the way that platforms allocate surpluses to key performers—as a convincer in a „Big Store“ con, a way to rope in other suckers who’ll make content for the platform, anchoring themselves and their audiences to it.

 

Which brings me to TikTok. TikTok is many different things, including “a free Adobe Premiere for teenagers that live on their phones.” But what made it such a success early on was the power of its recommendation system. From the start, TikTok was really, really good at recommending things to its users. Eerily good.

By making good-faith recommendations of things it thought its users would like, TikTok built a mass audience, larger than many thought possible, given the death grip of its competitors, like YouTube and Instagram. Now that TikTok has the audience, it is consolidating its gains and seeking to lure away the media companies and creators who are still stubbornly attached to YouTube and Insta.

Yesterday, Forbes’s Emily Baker-White broke a fantastic story about how that actually works inside of ByteDance, TikTok’s parent company, citing multiple internal sources, revealing the existence of a „heating tool“ that TikTok employees use to push videos from select accounts into millions of viewers‘ feeds.

These videos go into TikTok users‘ For You feeds, which TikTok misleadingly describes as being populated by videos „ranked by an algorithm that predicts your interests based on your behavior in the app.“ In reality, For You is only sometimes composed of videos that TikTok thinks will add value to your experience—the rest of the time, it’s full of videos that TikTok has inserted in order to make creators think that TikTok is a great place to reach an audience.

„Sources told Forbes that TikTok has often used heating to court influencers and brands, enticing them into partnerships by inflating their videos’ view count. This suggests that heating has potentially benefitted some influencers and brands—those with whom TikTok has sought business relationships—at the expense of others with whom it has not.“

In other words, TikTok is handing out giant teddy bears.

But TikTok is not in the business of giving away giant teddy bears. TikTok, for all that its origins are in the quasi-capitalist Chinese economy, is just another paperclip-maximizing artificial colony organism that treats human beings as inconvenient gut flora. TikTok is only going to funnel free attention to the people it wants to entrap until they are entrapped, then it will withdraw that attention and begin to monetize it.

„Monetize“ is a terrible word that tacitly admits that there is no such thing as an „attention economy.“ You can’t use attention as a medium of exchange. You can’t use it as a store of value. You can’t use it as a unit of account. Attention is like cryptocurrency: a worthless token that is only valuable to the extent that you can trick or coerce someone into parting with „fiat“ currency in exchange for it. You have to „monetize“ it—that is, you have to exchange the fake money for real money.

 

In the case of cryptos, the main monetization strategy was deception-based. Exchanges and „projects“ handed out a bunch of giant teddy-bears, creating an army of true-believer Judas goats who convinced their peers to hand the carny their money and try to get some balls into the peach-basket themselves.

But deception only produces so much „liquidity provision.“ Eventually, you run out of suckers. To get lots of people to try the ball-toss, you need coercion, not persuasion. Think of how US companies ended the defined benefits pension that guaranteed you a dignified retirement, replacing it with market-based 401(k) pensions that forced you to gamble your savings in a rigged casino, making you the sucker at the table, ripe for the picking.

Early crypto liquidity came from ransomware. The existence of a pool of desperate, panicked companies and individuals whose data had been stolen by criminals created a baseline of crypto liquidity because they could only get their data back by trading real money for fake crypto money.

The next phase of crypto coercion was Web3: converting the web into a series of tollbooths that you could only pass through by trading real money for fake crypto money. The internet is a must-have, not a nice-to-have, a prerequisite for full participation in employment, education, family life, health, politics, civics, even romance. By holding all those things to ransom behind crypto tollbooths, the holders hoped to convert their tokens to real money.

For TikTok, handing out free teddy-bears by „heating“ the videos posted by skeptical performers and media companies is a way to convert them to true believers, getting them to push all their chips into the middle of the table, abandoning their efforts to build audiences on other platforms (it helps that TikTok’s format is distinctive, making it hard to repurpose videos for TikTok to circulate on rival platforms).

Once those performers and media companies are hooked, the next phase will begin: TikTok will withdraw the „heating“ that sticks their videos in front of people who never heard of them and haven’t asked to see their videos. TikTok is performing a delicate dance here: There’s only so much enshittification they can visit upon their users‘ feeds, and TikTok has lots of other performers they want to give giant teddy-bears to.

Tiktok won’t just starve performers of the „free“ attention by depreferencing them in the algorithm, it will actively punish them by failing to deliver their videos to the users who subscribed to them. After all, every time TikTok shows you a video you asked to see, it loses a chance to show you a video it wants you to see, because your attention is a giant teddy-bear it can give away to a performer it is wooing.

This is just what Twitter has done as part of its march to enshittification: thanks to its „monetization“ changes, the majority of people who follow you will never see the things you post. I have ~500k followers on Twitter and my threads used to routinely get hundreds of thousands or even millions of reads. Today, it’s hundreds, perhaps thousands.

 

I just handed Twitter $8 for Twitter Blue, because the company has strongly implied that it will only show the things I post to the people who asked to see them if I pay ransom money. This is the latest battle in one of the internet’s longest-simmering wars: the fight over end-to-end.

In the beginning, there were Bellheads and Netheads. The Bellheads worked for big telcos, and they believed that all the value of the network rightly belonged to the carrier. If someone invented a new feature—say, Caller ID—it should only be rolled out in a way that allows the carrier to charge you every month for its use. This is Software-As-a-Service, Ma Bell style.

The Netheads, by contrast, believed that value should move to the edges of the network—spread out, pluralized. In theory, Compuserve could have „monetized“ its own version of Caller ID by making you pay $2.99 extra to see the „From:“ line on email before you opened the message— charging you to know who was speaking before you started listening—but they didn’t.

The Netheads wanted to build diverse networks with lots of offers, lots of competition, and easy, low-cost switching between competitors (thanks to interoperability). Some wanted this because they believed that the net would someday be woven into the world, and they didn’t want to live in a world of rent-seeking landlords. Others were true believers in market competition as a source of innovation. Some believed both things. Either way, they saw the risk of network capture, the drive to monetization through trickery and coercion, and they wanted to head it off.

They conceived of the end-to-end principle: the idea that networks should be designed so that willing speakers‘ messages would be delivered to willing listeners‘ end-points as quickly and reliably as they could be. That is, irrespective of whether a network operator could make money by sending you the data it wanted to receive, its duty would be to provide you with the data you wanted to see.

The end-to-end principle is dead at the service level today. Useful idiots on the right were tricked into thinking that the risk of Twitter mismanagement was „woke shadowbanning,“ whereby the things you said wouldn’t reach the people who asked to hear them because Twitter’s deep state didn’t like your opinions. The real risk, of course, is that the things you say won’t reach the people who asked to hear them because Twitter can make more money by enshittifying their feeds and charging you ransom for the privilege to be included in them.

As I said at the start of this essay, enshittification exerts a nearly irresistible gravity on platform capitalism. It’s just too easy to turn the enshittification dial up to eleven. Twitter was able to fire the majority of its skilled staff and still crank the dial all the way over, even with a skeleton crew of desperate, demoralized H1B workers who are shackled to Twitter’s sinking ship by the threat of deportation.

The temptation to enshittify is magnified by the blocks on interoperability: When Twitter bans interoperable clients, nerfs its APIs, and periodically terrorizes its users by suspending them for including their Mastodon handles in their bios, it makes it harder to leave Twitter, and thus increases the amount of enshittification users can be force-fed without risking their departure.

 

Twitter is not going to be a „protocol.“ I’ll bet you a testicle (not one of mine) that projects like Bluesky will find no meaningful purchase on the platform, because if Bluesky were implemented and Twitter users could order their feeds for minimal enshittification and leave the service without sacrificing their social networks, it would kill the majority of Twitter’s „monetization“ strategies.

An enshittification strategy only succeeds if it is pursued in measured amounts. Even the most locked-in user eventually reaches a breaking point and walks away, or gets pushed. The villagers of Anatevka in Fiddler on the Roof tolerated the cossacks‘ violent raids and pogroms for years, until they were finally forced to flee to Krakow, New York, and Chicago.

For enshittification-addled companies, that balance is hard to strike. Individual product managers, executives, and activist shareholders all give preference to quick returns at the cost of sustainability, and are in a race to see who can eat their seed-corn first. Enshittification has only lasted for as long as it has because the internet has devolved into “five giant websites, each filled with screenshots of the other four.”

With the market sewn up by a group of cozy monopolists, better alternatives don’t pop up and lure us away, and if they do, the monopolists just buy them out and integrate them into your enshittification strategies, like when Mark Zuckerberg noticed a mass exodus of Facebook users who were switching to Instagram, and so he bought Instagram. As Zuck says, „It is better to buy than to compete.“

This is the hidden dynamic behind the rise and fall of Amazon Smile, the program whereby Amazon gave a small amount of money to charities of your choice when you shopped there, but only if you used Amazon’s own search tool to locate the products you purchased. This provided an incentive for Amazon customers to use its own increasingly enshittified search, which it could cram full of products from sellers who coughed up payola, as well as its own lookalike products. The alternative was to use Google, whose search tool would send you directly to the product you were looking for, and then charge Amazon a commission for sending you to it.

The demise of Amazon Smile coincides with the increasing enshittification of Google Search, the only successful product the company managed to build in-house. All its other successes were bought from other companies: video, docs, cloud, ads, mobile, while its own products are either flops like Google Video, clones (Gmail is a Hotmail clone), or adapted from other companies‘ products, like Chrome.

Google Search was based on principles set out in founder Larry Page and Sergey Brin’s landmark 1998 paper, „Anatomy of a Large-Scale Hypertextual Web Search Engine,“ in which they wrote, “Advertising funded search engines will be inherently biased towards the advertisers and away from the needs of consumers.”

Even with that foundational understanding of enshittification, Google has been unable to resist its siren song. Today’s Google results are an increasingly useless morass of self-preferencing links to its own products, ads for products that aren’t good enough to float to the top of the list on its own, and parasitic SEO junk piggybacking on the former.

 

Enshittification kills. Google just laid off 12,000 employees, and the company is in a full-blown „panic“ over the rise of „AI“ chatbots, and is making a full-court press for an AI-driven search tool—that is, a tool that won’t show you what you ask for, but rather, what it thinks you should see.

Now, it’s possible to imagine that such a tool will produce good recommendations, like TikTok’s pre-enshittified algorithm did. But it’s hard to see how Google will be able to design a non-enshittified chatbot front-end to search, given the strong incentives for product managers, executives, and shareholders to enshittify results to the precise threshold at which users are nearly pissed off enough to leave, but not quite.

Even if it manages the trick, this-almost-but-not-quite-unusuable equilibrium is fragile. Any exogenous shock—a new competitor like TikTok that penetrates the anticompetitive „moats and walls“ of Big Tech, a privacy scandal, a worker uprising—can send it into wild oscillations.

Enshittification truly is how platforms die. That’s fine, actually. We don’t need eternal rulers of the internet. It’s okay for new ideas and new ways of working to emerge. The emphasis of lawmakers and policymakers shouldn’t be preserving the crepuscular senescence of dying platforms. Rather, our policy focus should be on minimizing the cost to users when these firms reach their expiry date: Enshrining rights like end-to-end would mean that no matter how autocannibalistic a zombie platform became, willing speakers and willing listeners would still connect with each other.

And policymakers should focus on freedom of exit—the right to leave a sinking platform while continuing to stay connected to the communities that you left behind, enjoying the media and apps you bought and preserving the data you created.

The Netheads were right: Technological self-determination is at odds with the natural imperatives of tech businesses. They make more money when they take away our freedom—our freedom to speak, to leave, to connect.

For many years, even TikTok’s critics grudgingly admitted that no matter how surveillant and creepy it was, it was really good at guessing what you wanted to see. But TikTok couldn’t resist the temptation to show you the things it wants you to see rather than what you want to see. The enshittification has begun, and now it is unlikely to stop.

It’s too late to save TikTok. Now that it has been infected by enshittifcation, the only thing left is to kill it with fire.