Waymo has developed a new computational model aimed at better addressing a key question: how does its self-driving technology compare to human drivers?

The robotaxi firm owned by Alphabet, which collaborated with TU Delft to create the model simulating human driving abilities, released a research article on this topic in Nature Communications on Wednesday.

Waymo indicated that it anticipates the new model will outperform the previous one employed over recent years. This updated model utilizes a framework known as active inference—suggesting that a driver continuously envisions potential futures and makes choices to navigate towards the safest, most foreseeable outcome.

The company indicated that this new model will enhance its understanding of human actions in crash situations encountered by its robotaxis.

“For decades, the automotive sector has utilized physical and virtual crash dummies to assess a car’s safety attributes, focusing on both its hardware and structural soundness,” Waymo mentioned in a blog entry on Wednesday. This new model, they stated, “advances this idea, acting as a behavioral standard for autonomous driving systems that can realistically illustrate what can be expected from a careful and skilled human driver when faced with traffic conflicts.”

An accurate representation of human driving behavior is essential for autonomous vehicle firms needing to evaluate the crash performance of their robotaxis. This comes at a pivotal time for Waymo, which is expanding into additional cities and facing increased scrutiny from regulators and the general public.

In January, after a Waymo robotaxi collided with a child near a Santa Monica school in California, the company utilized its prior model to assert that a vigilant human driver would have made contact at about 14 miles per hour. The Waymo robotaxi struck the child at only 6 miles per hour, having slowed down from 17 miles per hour, and the company reported minor injuries sustained by her. (The incident is still under investigation by the National Highway Traffic Safety Administration and the National Transportation Safety Board.)

The primary distinction between this new model—the Reference Driver—and its predecessor lies in its capacity to replicate a human driver’s actions leading up to a crash. Past models by Waymo (and other industry efforts) concentrated on mimicking “last-second, reactive” human responses, according to the firm.

In contrast, the Reference Driver can “emulate the internal ‘surprise’ experienced by a driver during a conflict, offering a more human-centric standard for autonomous driving systems that was previously unfeasible to automate at scale,” stated Arkady Zgonnikov, an assistant professor at TU Delft.

Waymo claims this new driver model is adaptable to simulate a “diverse array of road user behaviors beyond just avoiding collisions,” and that it is more capable for use in “extensive test sets comprising thousands of scenarios.”

“The model can illustrate and assess various intricate, real-world collisions within a virtual setting, pinpointing performance enhancements with unparalleled quickness and effectiveness,” the company noted.

Waymo is also eager for others to join in advancing the Reference Driver. The firm announced on Wednesday that it is releasing the research code for the model under an academic, non-commercial license, enabling its usage for research, instruction, personal trials, and scientific publication.

As technology firms compete to obtain the computational power essential for training and implementing AI systems, Meta is making its initial investment in AI infrastructure in India by partnering with conglomerate Reliance Industries in a market that is swiftly becoming a central hub for AI infrastructure.

The collaboration, unveiled on Wednesday, will involve Meta working with Reliance on a 168-megawatt AI-enabled data center located in Jamnagar, Gujarat, furthering a partnership that has progressed from Meta’s multibillion-dollar investment in Reliance’s Jio Platforms to a $100 million joint venture initiated last year to create enterprise AI solutions for clients in India and international markets.

This agreement comes as India solidifies its position as a prime destination for AI infrastructure investments, with technology giants exploring new regions for data centers amidst skyrocketing demand for computational power to train and deploy AI models. Companies such as Microsoft, Amazon, Google, OpenAI, and Uber have recently declared AI and cloud infrastructure investments in the nation, which has notably increased its data center presence in recent times.

The influx into India is not limited to international tech companies. Earlier this week, Blackstone-supported AirTrunk revealed intentions to invest $30 billion to construct 5 gigawatts of data center capacity in India by 2030, while Indian conglomerates like Adani and Tata Consultancy Services have also announced significant data center expansion initiatives aimed at accommodating AI workloads.

The government in New Delhi has aimed to draw such investments by implementing policy incentives, including tax exemptions through 2047 for foreign cloud providers on services sold internationally, as long as those services are operated from Indian data centers.

According to government data, India’s installed data center capacity has increased from approximately 375 megawatts in 2020 to around 1.5 gigawatts by 2025. Industry projections suggest that this figure could escalate to over 8 gigawatts by the decade’s close, fueled by cloud adoption, AI workloads, and increasing demand for local data processing.

The Meta-Reliance pact represents the latest development in a bond that has increasingly strengthened since Meta invested $5.7 billion in Jio Platforms in 2020. Since that time, the two firms have broadened their partnership in areas like digital services, enterprise AI, and now the infrastructure supporting cutting-edge AI systems.

As part of this collaboration, Meta is leasing capacity at Reliance’s new Jamnagar facility, which the companies have stated will operate on renewable energy and utilize desalinated seawater for cooling. Meta has pledged to bear the total cost of the energy and water necessary for its operations there.

Reliance indicated that the 168-megawatt facility will be operational within two years and has the potential for future expansion. Moreover, this data center will also fulfill Meta’s global infrastructure and AI computing needs, integrating India more directly into the company’s international network of AI facilities.

According to the agreement, Reliance stated it would offer end-to-end services ranging from design and construction to renewable energy, connectivity, and ongoing operations, indicating the conglomerate’s aspirations to establish itself as a comprehensive provider for AI infrastructure among global tech companies.

In addition, Meta disclosed that it has secured nearly 1 gigawatt of new renewable energy capacity in India through contracts with CleanMax and Fourth Partner Energy, which will enhance the renewable power supply for the Jamnagar facility.

The firms did not reveal the financial details of the agreement, the specific types of AI workloads that will be managed at the facility, or whether Meta intends to pursue further AI infrastructure investments in India.

Emad Dlala, a high-ranking official at electric vehicle manufacturer Lucid Motors, has departed from the organization only a few months after ascending to a prominent position, according to TechCrunch.

Dlala’s departure marks the first significant executive exit since Lucid Motors appointed Silvio Napoli as its new CEO in April. Napoli transitioned to Lucid following a successful career in various leadership roles at Schindler Group, a producer of escalators and elevators, and officially assumed the CEO position just last week.

In a statement provided to TechCrunch, Lucid Motors verified Dlala’s exit and indicated that the company is “revamping its structure to enhance innovation and improve execution under CEO Silvio Napoli.”

As part of this restructuring, Lucid Motors announced that Vivek Attaluri, its vice president of vehicle engineering, and Marc Solsona Palomar, vice president of software, will now report directly to Napoli. 

“Emad Dlala has chosen to leave the company to explore other opportunities. We appreciate Emad for his numerous contributions over the years and wish him success in his future pursuits. Lucid remains dedicated to refining our organization and processes to fully utilize our team’s strengths, and will share additional actions shortly,” the company stated.

Dlala opted not to comment.

Having been with Lucid Motors for over a decade, Dlala ranks among the company’s longest-serving staff and executives. During the last five years, he held both the positions of vice president and senior vice president, leading the company’s powertrain division.

In November, he was promoted to oversee all of “Engineering and Digital” concurrently with Lucid Motors’ separation from its long-standing chief engineer Eric Bach. Bach has since filed a lawsuit against Lucid Motors for wrongful termination—although the lawsuit has recently been paused pending arbitration, as per federal court records.

The company has encountered changes in the months that followed. In February, Lucid Motors laid off 12% of its workforce, as first reported by TechCrunch. The search for a new CEO was concluded after a year-long effort to replace Peter Rawlinson, who unexpectedly left in early 2025.

Dlala’s exit occurs just months before the launch of Lucid Motors’ inaugural mass-market vehicle based on its mid-sized platform, named Cosmos, a mid-size EV expected to be priced below $50,000. This would represent the Saudi-owned company’s first significant opportunity to offer a more affordable, widely embraced vehicle.

This cutting-edge EV is also a key element of Lucid’s arrangement to supply robotaxis to Uber. Lucid Motors has committed to developing robotaxis in collaboration with autonomous vehicle firm Nuro, initiating with its Gravity SUV. The self-driving Gravity is set to be deployed on the streets of San Francisco by year’s end.

Google has significantly lowered the price of its budget AI subscription plan, intensifying a price competition that has arisen in emerging markets and extending it to American users.

On Monday, the company revealed that it is reducing the monthly cost of Google AI Plus from $7.99 to $4.99 — while also increasing the storage offered at that level, from 200 gigabytes to 400 gigabytes.

Vikas Kansal, the product leader for Gemini AI subscriptions, shared on X that the storage enhancements would be available to users in the coming days.

Launched in January, Google AI Plus emerged as the most affordable paid AI subscription in the U.S. market, targeting individual users and students rather than corporate clients. The new pricing reinforces this focus.

It also offers a solid feature range, including video generation through Omni Flash; the creative studio Google Flow; and NotebookLM, Google’s AI research assistant. Users seeking additional features or higher usage limits can upgrade to Google’s AI Pro or AI Ultra tiers.

However, the more intriguing narrative here pertains not just to Google’s offerings. Subscription pricing had not been a focal point among AI providers in the U.S. until recently — and this change carries significant implications for the overall market, according to Chi-Hua Chien, co-founder and managing partner at Goodwater Capital, a venture firm centered on consumers in the Bay Area.

Chien perceives Monday’s announcement as a significant move in what he refers to as the commoditization era of AI infrastructure, highlighting Google’s structural strengths — vertical integration, extensive distribution, and the capacity to bundle — as critical elements likely to diminish margins for standalone AI providers over time.

The historical analogy he cites is revealing. “If you look at the web era, the infrastructure firms were Microsoft, Cisco, Oracle, Northern Telecom, Lucent, Akamai, Equinix,” he told TechCrunch. “Many of those companies thrived for a while but aren’t valued highly today.” He attributes this to the trend during major tech transitions — from PC to web to mobile — where infrastructure players become “commoditized very swiftly because the end user isn’t concerned with, ‘Oh, are my bits traveling on Cisco networking gear?’ They’re just considering, ‘How can I transfer my bits for the least cost?’”

This is not news to those developing foundational models. They have long understood that basic AI capabilities would eventually become a commodity, and that true competition would manifest at the application and distribution levels. What Chien suggests is that “eventually” is now.

“My forecast for many of these infrastructure firms — and when I refer to infrastructure, I include OpenAI or Anthropic, or the backend elements, energy, chips, hosting — there will be a time when these companies hold value,” he stated. “However, over time, you will observe them becoming increasingly commoditized.”

This is certainly something that a larger set of investors will soon be reflecting upon. Both OpenAI and Anthropic have confidentially filed for IPOs, and their ability to secure premium valuations may soon face scrutiny from the kind of price competition Chien describes.

This competition has been intensifying for almost a year in markets such as India, which has one of the fastest-growing AI user populations globally. OpenAI initiated the trend there in August of last year, unveiling ChatGPT Go at about $4.60 a month — a small fraction of its standard $20 Plus plan. Google responded in December with its own under-$5 AI Plus plan for Indian users.

Monday’s announcement indicates that the rationale driving those emerging-market strategies — undercutting, bundling, and acquiring users before competitors — has now transitioned to the U.S. market.

Notably, Anthropic has not followed suit. Unlike OpenAI and Google, it has yet to offer localized pricing for India or a budget tier elsewhere, which may become increasingly unavoidable as its competitors continue to reduce prices.

Last year, Justin Ernest identified a significant disparity in the functioning of venture capital: Family offices and smaller institutional investors were keen to invest in the most rapidly growing AI firms but lacked access to relevant cap tables. 

With over five years at Playground Global, focusing on deep tech investment and aiding in fundraising, Ernest felt assured that his connections with both investors and founders would enable him to close that gap.

Instead of embarking on a traditional VC fund, which he claims takes new managers between 12 to 18 months, Ernest leveraged his network to obtain allocations of stock in prominent, later-stage companies. He then presents these individual opportunities to a consortium of around 30 smaller institutional investors via special purpose vehicles (SPVs), single-asset funds, and nominee structures. In the latter model, his firm, Sabertooth Capital, holds shares on behalf of participating investors rather than through a conventional SPV.

In the past year, Sabertooth has invested nearly $500 million across 10 companies, such as Anthropic, Anduril, Base Power, Databricks, PsiQuantum, and SpaceX. The firm approaches each deal as a distinct fund, typically structuring it as an SPV, enabling the fund’s investors to acquire shares in the entity that holds the stock.

He’s issuing checks ranging from $10 million to $275 million — acquiring substantial portions of shares — and consistently takes part in officially sanctioned, company-approved funding rounds.

Sabertooth isn’t the sole firm providing family offices the chance to acquire equity in individual high-profile, late-stage startups. Nonetheless, Ernest swiftly attracted considerable investment from them due to his solid reputation in the sometimes murky realm of small allocations and SPVs aimed at family offices.

“Justin is genuinely an investor,” remarked Benjamin Wagner, a CIO for a family office overseeing the wealth of 50 individuals. “He possesses judgement, expertise, and a high level of technical knowledge, which truly sets him apart from other organizations that seem to, in my view, just try to gather capital.”

When Wagner sought to invest directly in PsiQuantum, the quantum computing startup last valued at $7 billion, the company’s CFO recommended that he invest via Sabertooth.

“So, from the first time I met [Ernest], I recognized his authenticity,” Wagner stated. “Justin’s access is certainly distinct from some of these transient organizations.”

That endorsement is highly significant. During a period when startups like Anthropic and Anduril are cracking down on unauthorized SPVs, investing through Sabertooth provides smaller limited partners with a sense of security. They trust their funds to an investor who is directly vetted and respected by the companies themselves.

Beyond his technical acumen, the Harvard Business School graduate refined his communication skills after largely overcoming a childhood speech impediment. Ernest attributes his ability to secure stock allocations when highly sought-after tech firms raise funds to his extensive network.

“I’ve always considered my sort of superpower is being the nucleus of my network, and I enjoy using that strategically,” he shared with TechCrunch.

For example, he can typically garner investor capital for a new SPV from family offices within a tight timeframe.

“I have a devoted set of LPs,” he remarked. “I can usually make four, five, or six phone calls and know exactly what my LPs will commit.”

Ernest conveyed to TechCrunch that currently, he aims to continue expanding his business of raising funds for specific companies on behalf of his dedicated LP base. However, his ultimate aspiration is to ultimately establish a traditional venture fund. This is a challenging endeavor, but he believes Sabertooth’s strong returns through these one-off SPVs will demonstrate his track record, which is a critical factor for investors when deciding to support a new fund.

He’s making progress towards that goal. Sabertooth has already achieved a significant return from chipmaker Groq, which was licensed and acqui-hired by Nvidia for $20 billion late last year. Next on the horizon is SpaceX’s much-anticipated IPO this Friday, along with Anthropic’s expected public listing later this year. These are set to generate an even larger windfall for his investors.

However, SPVs lack the same level of credibility as traditional VC funds. Yet Ernest remains optimistic that starting with them, and building a strong reputation with family offices, rather than launching an emerging venture fund and competing head-to-head with rivals was the right strategic choice. “I wanted to be actively involved,” he stated. “I believe this will turn out to be one of the best vintages of our lifetime.”

Updated to reflect Sabertooth’s total capital deployed.

The competition for power in AI data centers has extended to some unexpected sectors, notably the automotive industry. 

Last year, battery recycling company Redwood Materials initiated the trend by launching a new energy-storage division and a project that connected used EV batteries to a Crusoe data center in Nevada. Subsequently, Ford announced it was reallocating some of its battery-manufacturing capabilities to produce grid-scale batteries. Now, GM is revealing its own — potentially more ambitious — energy-storage system (ESS) plans. 

On Tuesday, GM introduced two new phases in its strategy for the energy-storage sector. The most significant move is GM’s partnership with energy-storage startup Peak Energy. For this collaboration, GM is creating a completely new sodium-ion battery chemistry designed for grid-scale usage.

Outside of China, no other automaker has declared intentions to manufacture sodium-ion cells. 

“Our entry into the market is through ESS, which is the straightforward approach,” Kurt Kelty, GM’s vice president of battery and sustainability, informed TechCrunch. “The performance attributes are exactly what is required in that sector.”

GM did not disclose the financial investment in this energy-storage initiative to TechCrunch. However, it has been reported that the company has allocated $900 million towards the commercialization of new battery chemistries, which encompasses a new battery-development facility.

Sodium-ion batteries function similarly to lithium-ion ones, but they substitute certain materials to create cells that are more affordable, longer-lasting, and less likely to overheat. The trade-off is that sodium-ion batteries tend to be bulkier and heavier to hold the same energy capacity. 

Peak Energy has already been developing energy-storage solutions utilizing sodium-ion batteries. Since sodium-ion batteries behave differently than lithium-ion types, Peak has designed an energy-storage system that accounts for these disparities. Its grid-scale batteries do not require cooling or fire-suppression systems due to a reduced risk of overheating. This configuration lowers initial costs and is expected to eliminate expensive maintenance, Paul Menson, director of energy-storage commercialization at GM, shared with TechCrunch. 

“The most challenging aspect to engineer was actually eliminating the part altogether,” he stated. “Remove the component, remove the issue.”

GM intends to provide sodium-ion cells to the startup, which will subsequently incorporate them into its products. However, this process will not begin immediately.

The initial GM cells are anticipated to commence trial production at the company’s Battery Cell Development Center in 2028. TechCrunch recently had an exclusive preview of this new facility, which GM believes will decrease the commercialization timeline for sodium-ion batteries by approximately one year, thereby cutting costs in the process. 

Nonetheless, GM’s sodium-ion cells remain several years away from entering commercial production. In the interim, the automaker will supply lithium iron phosphate (LFP) cells to LG Energy Solution for its energy-storage systems. LG Energy Solution is already collaborating with GM through its Ultium joint venture, which manufactures batteries for the automaker’s electric vehicles.

In addition to the collaborations with LG and Peak, GM announced it is enhancing its partnership with Redwood Materials, the battery-recycling and energy-storage startup established by former Tesla executive J.B. Straubel. 

Redwood already acquires scrap from GM’s battery production facilities and recycled battery packs from its EVs. GM has a pipeline of around 10,000 battery packs designated for Redwood, and the startup has been running a 12 megawatt/63 megawatt-hour microgrid using second-life battery packs at a Crusoe data center located in Sparks, Nevada. GM mentioned it is purchasing a 7.2 megawatt-hour Redwood system for deployment at one of its Michigan plants, which it estimates will result in savings of approximately $3 million over its lifespan.

The GM installation is described as “step one” for Redwood, according to Cal Lankton, chief commercial officer for Redwood, who expressed to TechCrunch.

Data centers, where Redwood already operates, and industrial locations like GM’s are “vastly different environments,” he noted. While data centers typically utilize batteries almost continuously to mitigate power fluctuations from GPUs, industrial sites are more inclined to employ them for reducing peak power demand, which can lead to lower monthly electricity expenses, and for providing backup power during outages.

“The factory is genuinely enthusiastic because now we have a more reliable operation,” Kelty stated. “Ultimately, we aim to have similar installations in all our factories. It just makes strong financial sense.”

After two years and a $250 million lawsuit, Apple is preparing to launch a revamped AI Siri for your phones, laptops, and even the mixed reality headset, assuming you’re among the few who actually utilize the Apple Vision Pro. At Monday’s WWDC keynote, Apple disclosed a wealth of new details regarding these anticipated AI-driven updates that are designed to leverage our hardware, which is claimed to be “built for Apple Intelligence.”

Honestly, it’s quite challenging for AI to captivate me enough to incorporate it into my everyday activities. I remain skeptical about LLMs consistently providing accurate information, I find it ethically questionable (and undesirable) to rely on AI for writing assistance, and I lack a strong desire to see my likeness as a Studio Ghibli character. Nonetheless, the allure of AI occasionally entices me.

That’s the sentiment I experienced while viewing Apple’s Siri AI demonstrations, which envision a reality where your phone is equipped with a perpetually active, ever-helpful assistant that understands everything about you and can assist in managing conversations across around 12 different applications simultaneously.

To echo Katy Perry, it feels so inappropriate (what are the privacy concerns?), yet it feels so suitable (I am inundated by my phone and crave help to navigate it all).

I envision Siri as my own personal Emily from “The Devil Wears Prada”—a “second brain” that foresees my needs even before I identify them. I want Siri to analyze my messages and automatically create an event when a friend and I decide to have dinner on Thursday. I want Siri to alert me when I pass by CVS that I have a prescription awaiting collection. If I neglect to respond to an essential work email, I want Siri to nudge me regarding my lack of response.

While the Siri AI won’t be capable of executing all of that immediately, it is certainly progressing in the correct direction. During one WWDC example, Justin Titi, an Apple senior director focused on AI engineering, prompts the smart assistant to remind him of a dessert his daughter recently mentioned. Siri searches through Titi’s phone to find a text from about a month prior, when his daughter expressed a desire to make coconut cookies. It’s straightforward, but enlisting Siri to locate that message saves time compared to scrolling through an entire month of chats to find that one particular text.

The revamped Siri is intended to utilize “personal context,” which includes any information entered into Apple-native applications such as iMessage, Notes, Calendar, Mail, Photos, and more. Siri will also recognize what appears on your screen, for instance, if you view a picture of a beautiful park on Instagram, you can ask it for the location of that park. (It remains unclear whether Siri will be able to connect with non-native Apple apps; it appears it might depend on developers to facilitate that.)

There are existing applications like Poppy and Poke that attempt to offer this kind of mobile, autonomous AI. However, the dilemma with these AI personal assistant tools is that utilizing them requires relinquishing a significant amount of personal data and privacy, which may lead to more complications (remember the incident when a Meta researcher used OpenClaw and inadvertently erased her entire inbox?).

I can’t say I’m fond of sharing my personal information with any tech corporation, but Apple at least appears to prioritize security more than other FAANG (or MANGOS?) companies. On-device AI will consistently be more secure and less resource-intensive than cloud computing, as the data is processed directly on your device. (This is how current Apple Intelligence features like email summaries and AI emojis are produced.) However, for the more complicated tasks Siri will face, Apple has innovated private cloud compute (PCC), a method for devices to analyze complex data via the cloud without exposing your data to Apple itself. (If hacking PCC is feasible, it hasn’t been accomplished yet, despite Apple offering a $1 million bounty for bugs.)

During a recent discussion with writer Calvin Kasulke — who is so immersed in the internet that he authored a novel set entirely on Slack — I admitted to what feels like a forbidden desire to delegate all of my “life admin” tasks to an AI.

“When you address the clutter of tech remnants in your life… I think the pertinent question is, ‘Is everything you have truly necessary?’ If it is necessary, shouldn’t you cultivate the skills and dedicate time to do it?” Calvin remarked. “I don’t believe those are skills someone should let fade away.”

He raises a valid point: Perhaps instead of instructing Siri to remind me of the TV series my friend suggested, I could focus more during my conversations with friends. I don’t want to develop a pattern of overlooking significant details from my discussions.

“I’m sorry, but all those advertisements that are like, ‘What if I had the computer purchase a birthday present for my child?’ I’m like, ‘What if you discovered what your child enjoys?’… like, I don’t know, it appears [they] don’t want to engage in the essential act of being a human,” he said.

Perhaps when I express a desire for Siri to emulate Emily from “The Devil Wears Prada,” I should recall that Emily’s character is on the brink of a breakdown. I understand I can’t psychologically affect Siri as Miranda Priestly did to Emily, but will I morph into someone who cannot operate without the friendly robotic voice in my phone? Do I wish to become that individual?

At least if I opt to step back from all of this, Apple will provide that option. Unlike Google’s contentious search redesign, the new AI Siri can be switched on and off, so using it is not compulsory. Until then, I’ll have to evaluate whether it’s worthwhile to indulge in the allure of Siri AI.

Anthropic has launched Claude Fable 5, the inaugural public version of its highly anticipated Mythos model. What can Fable achieve? A vast range of functionalities, it appears.

Ethan Mollick, a prominent AI researcher and academic at the University of Pennsylvania, has been experimenting with the model and appears to be thoroughly enjoying it.

In his explorations, Fable notably “surpassed essentially every other public model I have utilized by a significant margin,” Mollick noted on his Substack on Tuesday. He further mentioned that it was “competent across numerous challenges and yielded some astonishing outcomes — it could operate for up to twelve hours executing extensive multi-page specifications.” 

Perhaps most notably, Mollick leveraged Fable to develop a selection of video games — all of which were produced from “a single initial prompt” in Claude Code, according to the researcher.

Among these, Snake is precisely what you would expect. You control a Pac-Man-like serpent as it slithers around consuming apples. The snake is perpetually in motion, and if you veer off the screen, it’s game over. It has a very 1980s arcade vibe but, like many classic titles, it’s oddly captivating. I found myself playing it longer than I’d care to admit before recalling that I am a gainfully employed writer, not actually a fruit-loving serpent.

Next came Strata, where you navigate a seemingly infinite maze of underground tunnels with the objective of illuminating as many lanterns as possible. The visuals resemble a lower-quality version of Myst — not impressive — yet the mere existence of the game, created from a single prompt, is commendable.

Mollick even succeeded in crafting Duino, a game inspired by the Duino Elegies, the famed sequence of poems by Rainer Maria Rilke. The animation is my favorite aspect here — you play as a solitary figure in a nighttime setting — although the gameplay itself is minimal, consisting primarily of wandering around while passages from Rilke appear on the screen.

Beyond the array of immediate games Mollick created, he also utilized Fable to generate an isochronic map — a visual representation of travel times between any two points. The precision and intricacy are striking.

The implications are fairly obvious. Software projects that once demanded entire teams — such as games, mapping tools, and intricately detailed specifications — can now be initiated from a single prompt. This should be a cause for celebration among vibe coders globally. For founders and operators monitoring advancements in AI capabilities, it serves as a valuable data point regarding the rapid elevation of the baseline.