The moonshot factory, otherwise known as X, the moonshot factory, is a research and development facility and Google-owned organization that was founded in 2010. The factory is located in a former shopping mall about one and one-half miles from parent company Alphabet’s headquarters in Mountain View, California.
The moonshot factory is a Google-owned facility where the company seeks to solve humanity’s most wicked problems with radical new technologies.
X (formerly Google X) uses the facility to invent and launch moonshot technologies that can make the world a substantially better place to live. X defines a “moonshot” as one that lies at the intersection of a big problem, radical solution, and breakthrough technology.
The moonshot factory aims to find ideas that can deliver 10x impact and not those that are merely incremental improvements on the status quo. According to a 2016 article by Astro Teller (the self-confessed Captain of Moonshots), an X project “must solve a problem that affects millions or billions of people; it has to have an audacious, sci-fi sounding technology; and there has to be at least a glimmer of hope that it’s actually achievable in the next 5-10 years.”
Core principles and approach of the moonshot factory
X has realized that a moonshot happens when teams of passionate people dare to challenge the perspectives of others and aim for what others consider impossible.
This mission is supported by a principles-based approach as follows.
Tackle hard problems
Teams should fall in love with the problem, not the tech designed to solve it. They should understand the problem deeply before creative solutions are found.
This requires that the company seek out what it calls “T-shaped” personnel – or those who possess deep expertise, intellectual flexibility, and the ability to collaborate with others in different domains.
Find radical solutions
Radical solutions require teams with deep expertise from a wide gamut of disciplines and industries. They must be able to define sci-fi ideas that make a real-world impact as quickly as practicable. Collective expertise can mostly be found in:
X believes that the company itself is a moonshot, but instead of a radical new technology, it is employees that are the breakthrough idea.
To create a culture conducive to moonshots, certain values and practices help employees avoid pursuing the conventional or comfortable. This ensures that purpose-driven creativity is the path of least resistance.
Assemble small but diverse teams
Moonshot factory teams are not only highly skilled and experienced: they also offer diverse perspectives and come from different backgrounds.
In some teams at X, scientists collaborate with puppeteers and concert pianists, while in others machine learning experts mingle with marine biologists. Each moonshot has a core staff that is kept as small as possible, but teams are supplemented on occasion by a permanent roster of in-house experts.
Many of these experts help the team’s moonshot make contact with the real-world and address issues related to physics, mechanical engineering, user experience research, public opinion, and public policy.
Balance the portfolio
The moonshot factory must strike the delicate balance between placing large bets on the future and not scaring off the people who fund idea development.
To ensure the longevity of the moonshot factory and X itself, the team looks to balance the portfolio with a mixture of different industries and problems. The portfolio should also contain a mix of short-term (closer to 5 years) and long-term (closer to 10 years) ideas with clear budgets and limitations.
Be responsibly irresponsible
Perhaps unsurprisingly, moonshot factory teams embrace failure and see it as an effective way to learn and make progress. According to Astro Teller, this is one of the company’s most important values.
Employees are encouraged to be responsibly irresponsible and make frequent mistakes that end in failure. This requires that convention is dismissed from the outset and critical risks are identified early. Following this strategy, the team can “learn cheaply” because they receive rapid feedback on whether the idea is viable without spending vast sums of money.
Establish kill signals
Following on from the point above is the notion of kill signals. These are metrics that if a team reaches (or indeed fails to reach within a certain timeframe), indicate that the project should be killed. Using kill signals, moonshot factory teams remain intellectually honest and can make difficult decisions with clarity.
Foghorn is one example of a project that was shelved. While the project successfully produced sustainable fuel from seawater, it was determined that it could not do so at a rate that was cheaper than gasoline from the pump. After X killed the project, it nevertheless published the results in a scientific paper and awarded the team a bonus.
Active moonshot factory projects
Chorus
The Chorus project aims to improve how goods are moved and used around the world. For the past three and a half years, a small team has been working with various partners to understand supply chain vulnerabilities and why delays, bottlenecks, and waste occur.
Despite goods worth trillions of dollars being transported globally each year, many businesses still lack the tools to identify where their items are in real time. While the project is very much in its infancy, Chorus is currently developing new software and machine learning tools to improve visibility into a product’s location.
To that end, the Chorus team has asked some important questions. What if every item, box, and pallet had a voice? What if perishable items such as food or important medical supplies could send an alert if they were too cold, too hot, or near their expiry date?
However, an agricultural system that is standardized and maximized for productivity comes with inherent risks. Food crops become more vulnerable to disease, pests, and climate change. The soil in which crops are grown also become less fertile over time with large applications of pesticides and fertilizer.
Mineral seeks to address this issue by helping farmers and food producers unlock the genetic diversity of some of the thousands of uncultivated edible plant species. Might there be some species that are more resilient to a changing climate or have the ability to restore soil health?
The Mineral plant rover
Based on consultation with producers around the world, the Mineral team saw an opportunity to build new software and hardware that would help them make sense of vast and diverse information. Basic platforms could show soil, weather, and historical crop data, but the team also developed a prototype plant rover that could assess how crops were responding to their environment in real-time.
One prototype has spent several years analyzing strawberry and soybean crops in the United States. It takes a high-quality image of each plant and counts every berry or bean as well as plant height and total leaf area.
When imagery from the rover is combined with soil and weather data, machine learning is used to identify patterns and yield insights into how plants respond to their environment. Mineral graduated from the rapid prototype stage in January 2023 and is now an independent subsidiary of Alphabet.
Graduated moonshot factory projects
Loon
Project Loon was an early research and development project that commenced in 2011. The project offered internet access to remote and rural areas with high-altitude balloons that created an aerial wireless network.
The first balloons were launched in New Zealand in 2013 and at one point, Google planned to have thousands of them in the stratosphere. Loon was spun out as a separate company in July 2018 but was shut down in early 2021 with the company citing a lack of commercial viability.
Brain
Brain was also started in 2011 as a way to increase access to the benefits of AI and machine learning. While advances in this field are occurring at a rapid pace today, progress was slow in 2011 and deep learning was very much confined to academia.
Google employees Jeff Dean and Greg Corrado together with Stanford professor Andrew Ng started the project with a large-scale deep-learning software system known as DistBelief. Use of the proprietary system grew rapidly across Google in various research and commercial contexts, with the second-generation system later known as TensorFlow.
TensorFlow is a flexible system that can be used with programming languages such as JavaScript, C++, Java, and Python. This flexibility makes it suitable for a diverse range of applications in different industries.
Brain is one of the company’s most successful projects to date and, according to Astro Teller, has more than covered the entire cost of X.
Waymo
Waymo started life as the Google Self-Driving Car Project in January 2009. Waymo LLC is now an autonomous driving tech company and Alphabet subsidiary with research initially performed at the Google X lab headed by co-founder Sergey Brin.
The impetus for starting Waymo was to make driving safer, with Google noting on the project’s website that 94% of the 1.25 million road accident deaths around the world each year are caused by human error.
The Waymo autonomous vehicle was based on a Toyota Prius with sensors that would allow it to detect objects during the day and night. The team later conducted over 100,000 miles of autonomous driving in challenging situations around California. These included navigating the Golden Gate Bridge and up mountainous roads to Lake Tahoe.
Firefly
After a second vehicle based on a Lexus SUV was trained to recognize pedestrians, cyclists, and hundreds of different roadside objects, the project team wondered if they could build an autonomous vehicle from scratch.
A prototype named Firefly was later developed with no steering wheel or pedals and an assortment of LIDAR, radar, and camera sensors were seamlessly built into its design.
While banned in some states, these vehicles took to the streets in California and Texas where they proved capable of handling even the most difficult driving tasks. Firefly could detect and respond to emergency vehicles and even mastered complex, multi-lane four-way stop intersections.
Waymo graduated from X in 2016. By that time, its self-driving vehicles had totaled more than 2 million miles of driving on American roads – equivalent to the amount of driving a person could do if given 300 years.
Chronicle
In 2016, X started on a moonshot that would help companies combat cyberattacks. With the frequency and effectiveness of cyberattacks increasing faster than companies could train professionals to prevent them, it was clear that a solution was needed.
The team recognized that vast amounts of security-related data made it hard for organizations to respond to threats in a timely manner. In response, Chronicle developed a cybersecurity intelligence and analytics platform to enable enterprise clients to better understand and manage their data.
More specifically, the platform combines large amounts of storage and computational power with machine learning to help teams find patterns in the data. These patterns, which are often missed by trained experts, provide a holistic view of a security situation and seek to shorten the time between when an attack occurs and when it is discovered.
The team subsequently developed a security telemetry platform called Backstory and announced that they would be collaborating with the Google Cloud team. Backstory and a few other tools are now offered to Google Cloud customers as part of its autonomic security solutions feature.
While Chronicle has enabled businesses to respond to threats with greater speed and agility, X recognizes that the real moonshot is yet to be achieved. One day, it is hoped that Chronicle will help clients predict and deflect cyberattacks proactively.
Key takeaways
The Brain moonshot factory is a Google-owned facility where the company seeks to solve humanity’s most wicked problems with radical new technologies.
X understands that a moonshot occurs when teams of passionate people dare to challenge the perspectives of others and aim for what is considered impossible. Some core principles include: be responsibly irresponsible, balance the portfolio, and assemble small but diverse teams.
Some of the moonshot factory’s graduated projects include Loon, a balloon-based wireless aerial network, the autonomous vehicle project Waymo, the cloud-based cybersecurity project Chronicle, and the machine learning project Brain.
Key Insights:
The Moonshot Factory (X): X, also known as the Moonshot Factory, is a research and development facility owned by Google, focused on solving humanity’s most challenging problems using radical new technologies.
Moonshot Definition: X defines a “moonshot” as a project that tackles a big problem with a radical solution and breakthrough technology, aiming for 10x impact rather than incremental improvements.
Core Principles: X follows a principles-based approach, including falling in love with the problem, finding radical solutions with diverse expertise, and fostering a culture conducive to moonshots.
Small, Diverse Teams: X assembles small but diverse teams, bringing together experts from various disciplines and backgrounds to work on moonshot projects.
Balancing the Portfolio: X balances its portfolio with a mixture of different industries and problems, including short-term and long-term ideas with clear budgets.
Embracing Failure: X encourages teams to be responsibly irresponsible, embracing failure as a way to learn and make progress. Kill signals are used to identify when a project should be terminated.
Active Projects: Among the active projects at X are Chorus, aiming to improve global goods movement, and Mineral, focused on diversifying food crops for resilience and sustainability.
Graduated Projects: Notable graduated projects include Loon, an aerial wireless network using high-altitude balloons, Waymo, an autonomous driving tech company, and Chronicle, a cybersecurity intelligence platform.
Brain Project: The successful Brain project focused on AI and machine learning, leading to the creation of TensorFlow, which has more than covered the entire cost of X.
Other Moonshots Case Studies
1. Google’s Project Loon
Why a Moonshot: Project Loon was a moonshot because it aimed to provide internet access to the most remote parts of the world using high-altitude balloons, a radical solution leveraging breakthrough technology to tackle a significant global challenge.
Management Approach: The project was managed through X (formerly Google X), Google’s moonshot factory, which encourages rapid prototyping and iterative testing. Key to management was the “fail fast” philosophy, allowing the team to learn and iterate quickly.
Impact/Implications: Project Loon advanced telecommunications technology and provided proof of concept that broadband services could be delivered via high-altitude balloons. Despite being shut down in 2021 due to commercial viability challenges, it influenced future tech approaches to remote connectivity.
2. Waymo (Google)
Why a Moonshot: Originally known as the Google Self-Driving Car Project, Waymo sought to transform transportation with autonomous vehicles, aiming to reduce traffic accidents and change the nature of driving—integrating AI and robotics in unprecedented ways.
Management Approach: Waymo’s development was characterized by extensive real-world testing and partnerships with automotive manufacturers and other tech firms to integrate and advance autonomous technology. This project was also incubated under X, benefiting from Alphabet’s ecosystem which supports ambitious, technology-driven projects.
Impact/Implications: Waymo has led to significant advancements in autonomous driving technology, influencing the entire automotive industry. It operates a commercial self-driving taxi service in Phoenix, AZ, and continues to expand testing and operations, underscoring ongoing impacts in reducing human error on the roads.
3. Tesla’s Gigafactory
Why a Moonshot: Tesla’s approach to manufacturing electric vehicles (EVs) and batteries on a massive scale with its Gigafactory was a moonshot due to its aim to drastically reduce the cost of batteries and accelerate the adoption of EVs to combat climate change.
Management Approach: The project is managed through bold planning and massive capital investment, emphasizing vertical integration and innovation in both product development and manufacturing processes. Tesla’s management style is aggressive in timelines and scale, pushing the boundaries of what is traditionally expected in automotive manufacturing.
Impact/Implications: The Gigafactory has significantly reduced battery costs and scaled up production capacity, making EVs more accessible. It has prompted the auto industry to accelerate their own EV and battery production plans and has implications for global energy policy and green technology adoption.
4. Microsoft’s Project Natick
Why a Moonshot: Project Natick investigated the feasibility of underwater data centers, using the ocean to naturally cool data centers and potentially reduce the environmental impact of cloud computing.
Management Approach: Managed as an experimental initiative under Microsoft Research, the project involved deploying a prototype data center underwater and monitoring its performance over an extended period. The approach emphasized environmental sustainability and innovative thinking in addressing data center efficiency.
Impact/Implications: The successful two-year underwater deployment demonstrated that the concept could work, with implications for reducing the environmental footprint of data centers and exploring alternative methods to power cloud computing infrastructure. This project could lead to broader adoption of similar technologies in the industry.
5. IBM Quantum Computing
Why a Moonshot: IBM’s venture into quantum computing aims to revolutionize computing by harnessing quantum mechanics to process information in fundamentally new ways. The goal is to achieve breakthroughs in fields like medicine, materials science, and complex system modeling that are beyond the reach of classical computers.
Management Approach: IBM manages this moonshot by leveraging its extensive research division and collaborating with universities, research labs, and industry partners. The project is also part of the IBM Q Network, a community dedicated to advancing quantum computing and exploring commercial applications.
Impact/Implications: Quantum computing could potentially solve complex problems millions of times faster than classical computers. IBM’s early prototypes and the Q Network are laying the groundwork for future applications in cryptography, optimization, and artificial intelligence, potentially transforming how we solve some of the world’s most complex issues.
6. Neuralink
Why a Moonshot: Founded by Elon Musk, Neuralink’s mission is to develop ultra-high bandwidth brain-machine interfaces to connect humans and computers. This ambitious project aims to facilitate direct communications between technology and the human brain, potentially helping to treat neurological diseases and merging human cognition with AI.
Management Approach: Neuralink approaches this moonshot with a secretive but intense R&D effort, combining neuroscientists, engineers, and other specialists. The company focuses on developing scalable high-fidelity neural implants and emphasizes rapid iteration and innovation.
Impact/Implications: If successful, Neuralink could revolutionize neuroscience and medicine by providing new treatments for brain disorders and injuries. Long-term, the project could lead to symbiosis with artificial intelligence, significantly altering human capabilities and society.
7. Blue Origin’s New Shepard
Why a Moonshot: Blue Origin, founded by Jeff Bezos, aims to make space travel more accessible and affordable with its New Shepard suborbital space vehicle. The project is considered a moonshot because of its goal to lower the cost of space travel through reusable rocket technology.
Management Approach: The project is managed through iterative development and rigorous testing, with a focus on safety and precision engineering. Blue Origin emphasizes vertical integration, manufacturing most components in-house to maintain quality and reduce costs.
Impact/Implications: New Shepard has the potential to open up new possibilities for space tourism, research, and commercial spaceflight. Reusability has already influenced the space industry, pushing competitors like SpaceX to accelerate similar technologies, thereby advancing the broader commercial space sector.
Large language models (LLMs) are AI tools that can read, summarize, and translate text. This enables them to predict words and craft sentences that reflect how humans write and speak.
Prompt engineering is a natural language processing (NLP) concept that involves discovering inputs that yield desirable or useful results.
Like most processes, the quality of the inputs determines the quality of the outputs in prompt engineering. Designing effective prompts increases the likelihood that the model will return a response that is both favorable and contextual.
Developed by OpenAI, the CLIP (Contrastive Language-Image Pre-training) model is an example of a model that utilizes prompts to classify images and captions from over 400 million image-caption pairs.
OpenAI is an artificial intelligence research laboratory that transitioned into a for-profit organization in 2019. The corporate structure is organized around two entities: OpenAI, Inc., which is a single-member Delaware LLC controlled by OpenAI non-profit, And OpenAI LP, which is a capped, for-profit organization. The OpenAI LP is governed by the board of OpenAI, Inc (the foundation), which acts as a General Partner. At the same time, Limited Partners comprise employees of the LP, some of the board members, and other investors like Reid Hoffman’s charitable foundation, Khosla Ventures, and Microsoft, the leading investor in the LP.
OpenAI has built the foundational layer of the AI industry. With large generative models like GPT-3 and DALL-E, OpenAI offers API access to businesses that want to develop applications on top of its foundational models while being able to plug these models into their products and customize these models with proprietary data and additional AI features. On the other hand, OpenAI also released ChatGPT, developing around a freemium model. Microsoft also commercializes opener products through its commercial partnership.
OpenAI and Microsoft partnered up from a commercial standpoint. The history of the partnership started in 2016 and consolidated in 2019, with Microsoft investing a billion dollars into the partnership. It’s now taking a leap forward, with Microsoft in talks to put $10 billion into this partnership. Microsoft, through OpenAI, is developing its Azure AI Supercomputer while enhancing its Azure Enterprise Platform and integrating OpenAI’s models into its business and consumer products (GitHub, Office, Bing).
Stability AI is the entity behind Stable Diffusion. Stability makes money from our AI products and from providing AI consulting services to businesses. Stability AI monetizes Stable Diffusion via DreamStudio’s APIs. While it also releases it open-source for anyone to download and use. Stability AI also makes money via enterprise services, where its core development team offers the chance to enterprise customers to service, scale, and customize Stable Diffusion or other large generative models to their needs.
Gennaro is the creator of FourWeekMBA, which reached about four million business people, comprising C-level executives, investors, analysts, product managers, and aspiring digital entrepreneurs in 2022 alone | He is also Director of Sales for a high-tech scaleup in the AI Industry | In 2012, Gennaro earned an International MBA with emphasis on Corporate Finance and Business Strategy.
