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Octane banks $2M for flexible billing software
Software billing startup Octane announced Tuesday that it raised $2 million on a post-money valuation of $10 million to advance its pay-as-you-go billing software.
Akash Khanolkar and his co-founders met a decade ago at Carnegie Mellon University and since then went off in different directions. In Khanolkar’s case, he ran a cloud consulting business and saw how fast companies like Datadog and Snowflake were coming to market and dealing with Amazon Web Services.
He found that the commonality in all of those fast-growing companies was billing software using a pay-as-you-go business model versus the traditional flat-rate plans, Khanolkar told TechCrunch.
However, he explained that monitoring consumption means that billing becomes complicated: companies now have to track how customers are using the software per second in order to bill correctly each month.
Seeing the shift toward consumption-based billing, the co-founders came back together in June 2020 to create Octane, a metered billing system that helps vendors create a plan, monitor usage and charge in a similar way to Snowflake and AWS, Khanolkar said.
“We are API-driven, and you as a vendor will send us usage data, and on our end, we store it and then do real-time aggregations so at the end of the month, you can accordingly bill customers,” Khanolkar said. “We have seen contention between engineering and product. Engineers are there to create core plans, so we built a no-code experience for product teams to be able to create new price plans and then perform changes, like adding coupons.”
Within the global cloud billing market, which is expected to reach $6.5 billion by 2025, there are a set of Octane competitors, like Chargebee and Zuora, that Khanolkar said are tackling the subscription management side and succeeding in the past several years. Now there is a usage and consumption-based world coming and a whole new set of software businesses, like Octane, coming in to succeed there.
The new round of funding was led by Basis Set Ventures and included Dropbox co-founder Arash Ferdowsi, Github CTO Jason Warner, Fortress CTO Assunta Gaglione, Scale AI CRO Chetan Chaudhary, former Twilio executive Evan Cummack, Esteban Reyes, Abstraction Capital and Script Capital.
“With the rise of product-led growth and usage-based pricing models, usage-based billing is a critical and foundational piece of infrastructure that has been simply missing,” said Chang Xu, partner at Basis Set Ventures, via email. “At the same time, it’s something that every department cares about as it’s your revenue. Many later-stage companies we talk to that have built this in-house talk about the ongoing maintenance costs and wishes that there is a vendor they can outsource it to.”
We are super impressed with the Octane team with their dedication to building a best-in-class and robust usage-based billing solution. They’ve validated this opportunity by talking to lots of engineering teams so they can solve for all the edge cases, which is important in something as mission critical as billing. We are convinced that Octane will become an inevitable part of the tech infrastructure.”
The new funding will go primarily toward hiring engineers, as well as product, marketing and sales staff. Octane currently has seven employees, and Khanolkar expects to be around 10 by the end of the year.
The company is working with a large range of companies, primarily focused on infrastructure and the depth gauge industries. Octane is also seeing some unique use cases emerge, like a construction company using the usage meter to track the hours an employee works and companies in electric charging using the meter for those purposes.
“We didn’t envision construction guys using it, but in theory, it could be used by any company that tracks time — even legal,” Khanolkar added.
He declined to speak about the company’s revenue, but did say it now had two to three years of runway.
Up next, the company plans to roll out new features like price experimentation based on usage to help customers better make decisions on how to price their software, another problem Khanolkar sees happening. It will build ways that customers can try different plans against usage data to validate which one works the best.
“We are still in the early innings of consumption-based models, but we see more end users opting to go with an enterprise that wants to let them try out the software and then pay as they go,” he added.
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Zoom announces first startups receiving funding from $100M investment fund
For more than a year now, Zoom has been on a mission to transform from an application into a platform. To that end it made three announcements last year: Zoom Apps development tools, the Zoom Apps marketplace and a $100 million development fund to invest in some of the more promising startups building tools on top of their platform. Today, at the closing bell, the company announced it has made its first round of investments.
Ross Mayfield, product lead for Zoom Apps and integrations, spoke to TechCrunch about the round of investments. “We’re in the process of creating this ecosystem. We felt it important, particularly to focus on the seed stage and A stage of partnering with entrepreneurs to create great things on this platform. And I think what you see in the first batch of more than a dozen investments is representative of something that’s going to be a significant ongoing undertaking,” he explained.
He said while they aren’t announcing exact investment amounts, they are writing checks for between $250,000 and $2.5 million. They are teaming with other investment partners, rather than leading the rounds, but that doesn’t mean they aren’t working with these startups using internal resources for advice and executive backing, beyond the money.
“Every one of these investments has an executive or senior sponsor within the company. So there’s another person inside that knows the lay of the land, can help them advance and spend more personal time with them,” Mayfield said.
The company is also running several Zoom chat channels for the startups receiving investments to learn from one another and the Zoom Apps team. “We have a shared chat channel between the startup and my team. We have a channel called Announcements and a channel called Help, and another one that the startups created called Community,” he said.
Every week they use these channels to hold a developer office hour, a business office hour (which Mayfield runs) and a community hour, where the startups can gather and talk amongst themselves about whatever they want.
Among the specific categories receiving funding are collaboration and productivity, community and charity, DE&I and PeopleOps, and gaming and entertainment. In the collaboration and productivity category, Warmly is a sales tool that provides background and information about each person participating in the meeting ahead of time, while allowing the meeting organizer to create customized Zoom backgrounds for each event.
Another is Fathom, which alleviates the need to take notes during a meeting, but it’s more than recording and transcription. “It gives you this really simple interface where you can just tag moments. And then, as a result you have this transcript of the video recording, and you can click on those tagged moments as highlights, and then share a clip of the meeting highlights to Salesforce, Slack and other tools,” Mayfield said.
Pledge enables individuals or organizations to request and collect donations inside a Zoom meeting instantly, and Canvas is a hiring and interview tool that helps companies build diverse teams with data that helps them set and meet DEI goals.
These and the other companies represent the first tranche of investments from this fund, and Mayfield says the company intends to continue looking for startups using the Zoom platform to build their startup or integrate with Zoom.
He says that every company starts as a feature, then becomes a product and then aspires to be a line of products. The trick is getting there. The goal of the investment program and the entire set of Zoom Apps tools is about helping these companies take the first step.
“The art of being an entrepreneur is working with that risk in the absence of resources and pushing at the frontier of what you know.” Zoom is trying to be a role model, a mentor and an investor on that journey.
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Zeal banks $13M to offer employers a ‘build your own’ payroll product infrastructure
Embedded fintech company Zeal secured $13 million in Series A funding to continue developing its platform for building individualized payroll products.
Spark Capital led the Series A, with participation from Commerce Ventures and a group of individual investors, including Marqeta CEO Jason Gardner and CRO Omri Dahan, Robinhood founder Vlad Tenev, UltimateSoftware executives Mitch Dauerman and Bob Manne and Namely founder Matt Straz. The latest round now gives the company $14.6 million in total funding, which includes a $1.6 million seed round in 2020, CEO Kirti Shenoy told TechCrunch.
The Bay Area company’s origin was as Puzzl, a payment processing startup for the gig economy, founded in 2018 by Shenoy and CTO Pranab Krishnan. It was part of Y Combinator’s 2019 cohort. The pair had to pivot the company after needing to move some of its thousands of 1099 contractors to W2 employee status.
They went looking for payroll processors that could handle high volumes of payroll automatically, like ADP or Paycor, but found they didn’t match some of the capabilities Shenoy and Krishnan wanted, including to pay workers daily and customize earning components.
To ensure other companies didn’t run into the same problem, they decided to build a payroll API that enables their customers to build their own payroll products, even being able to pay their workers everyday. Traditionally, companies would layer together antiquated third-party payroll tools and spend millions of dollars on consulting fees. Zeal’s API tool modernizes the payroll process and takes on the payroll liability while managing the back-end payment logistics, Shenoy said.
Currently, enterprises use Zeal to pay large volumes of workers and keep payment data on their own native systems, while software platforms that sell business-to-business services use Zeal to build their own payroll product to sell to their customers.
“Our mission is to touch every American paycheck with our tax and payment technology, ensuring that American employees are paid correctly and efficiently,” Krishnan said.
And that is a complex goal: there are 200 million American employees, over $8.8 trillion of payroll is processed annually in the U.S. and the country’s 11,000 tax jurisdictions produce over 25,000 income tax code changes a year.
Meanwhile, Shenoy cited IRS data that showed more than 40% of small and medium businesses pay at least one payroll penalty per year. That was one of the drivers for Zeal’s latest product, the Abacus gross-to-net calculator, which payroll companies can use to ensure they are compliant in paying their income taxes.
The co-founders intend to use the new funding to build out their team and strengthen compliance measures to ensure its track record with enterprises.
“We are starting to win more enterprise deals and moving millions of dollars each day,” Shenoy said. “This has been a legacy space for so long, so companies want to work with a provider to move fast.”
Shenoy predicts that more companies will shift to hyper-customized experiences in the next five to 10 years. Whereas the default was a company like ADP, companies will want to control their own data and build products so their customers can do everything payroll-related from one platform.
As part of the investment, Spark Capital’s partner Natalie Sandman has joined Zeal’s board of directors. The firm previously invested in other embedded fintech companies like Affirm and Marqeta, and she thinks there are new experiences in the sector that APIs can unlock.
Sandman felt the payroll-building pain points herself when she worked at Zenefits. At the time, the company was trying to do the same thing, but there were no APIs to connect with. There were all of these spreadsheets to transfer data, but one wrong deduction would trickle down and cause a tax penalty.
Shenoy and Krishnan are both “customer-obsessed,” she said, and are balancing speed with thoughtfulness when it comes to understanding how their customers want to build payroll products.
She is seeing a macro shift to audience-driven human resources where bringing new employees online will mean embedding them into products that will be more valuable versus the traditional spreadsheet.
“To me, it is a no-brainer that APIs provide flexibility in the way wages and deductions need to be made,” Sandman said. “You can lose trust in your employer. Payroll is at the deepest trust point and where you want transparency and a robust solution to solve that need.”
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Cribl raises $200M to help enterprises do more with their data
At a time when remote work, cybersecurity attacks and increased privacy and compliance requirements threaten a company’s data, more companies are collecting and storing their observability data, but are being locked in with vendors or have difficulty accessing the data.
Enter Cribl. The San Francisco-based company is developing an “open ecosystem of data” for enterprises that utilizes unified data pipelines, called “observability pipelines,” to parse and route any type of data that flows through a corporate IT system. Users can then choose their own analytics tools and storage destinations like Splunk, Datadog and Exabeam, but without becoming dependent on a vendor.
The company announced Wednesday a $200 million round of Series C funding to value Cribl at $1.5 billion, according to a source close to the company. Greylock and Redpoint Ventures co-led the round and were joined by new investor IVP, existing investors Sequoia and CRV and strategic investment from Citi Ventures and CrowdStrike. The new capital infusion gives Cribl a total of $254 million in funding since the company was started in 2017, Cribl co-founder and CEO Clint Sharp told TechCrunch.
Sharp did not discuss the valuation; however, he believes that the round is “validation that the observability pipeline category is legit.” Data is growing at a compound annual growth rate of 25%, and organizations are collecting five times more data today than they did 10 years ago, he explained.
“Ultimately, they want to ask and answer questions, especially for IT and security people,” Sharp added. “When Zoom sends data on who started a phone call, that might be data I need to know so I know who is on the call from a security perspective and who they are communicating with. Also, who is sending files to whom and what machines are communicating together in case there is a malicious actor. We can also find out who is having a bad experience with the system and what resources they can access to try and troubleshoot the problem.”
Cribl also enables users to choose how they want to store their data, which is different from competitors that often lock companies into using only their products. Instead, customers can buy the best products from different categories and they will all talk to each other through Cribl, Sharp said.
Though Cribl is developing a pipeline for data, Sharp sees it more as an “observability lake,” as more companies have differing data storage needs. He explains that the lake is where all of the data will go that doesn’t need to go into an existing storage solution. The pipelines will send the data to specific tools and then collect the data, and what doesn’t fit will go back into the lake so companies have it to go back to later. Companies can keep the data for longer and more cost effectively.
Cribl said it is seven times more efficient at processing event data and boasts a customer list that includes Whole Foods, Vodafone, FINRA, Fannie Mae and Cox Automotive.
Sharp went after additional funding after seeing huge traction in its existing customer base, saying that “when you see that kind of traction, you want to keep doubling down.” His aim is to have a presence in every North American city and in Europe, to continue launching new products and growing the engineering team.
Up next, the company is focusing on go-to-market and engineering growth. Its headcount is 150 currently, and Sharp expects to grow that to 250 by the end of the year.
Over the last fiscal year, Cribl grew its revenue 293%, and Sharp expects that same trajectory for this year. The company is now at a growth stage, and with the new investment, he believes Cribl is the “future leader in observability.”
“This is a great investment for us, and every dollar, we believe, is going to create an outsized return as we are the only commercial company in this space,” he added.
Scott Raney, managing director at Redpoint Ventures, said his firm is a big enterprise investor in software, particularly in companies that help organizations leverage data to protect themselves, a sweet spot that Cribl falls into.
He feels Sharp is leading a team, having come from Splunk, that has accomplished a lot, has a vision and a handle on the business and knows the market well. Where Splunk is capturing the machine data and using its systems to extract the data, Cribl is doing something similar in directing the data where it needs to go, while also enabling companies to utilize multiple vendors and build apps to sit on top of its infrastructure.
“Cribl is adding opportunity by enriching the data flowing through, and the benefits are going to be meaningful in cost reduction,” Raney said. “The attitude out there is to put data in cheaper places, and afford more flexibility to extract data. Step one is to make that transition, and step two is how to drive the data sitting there. Cribl is doing something that will go from being a big business to a legacy company 30 years from now.”
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Bodo.ai secures $14M, aims to make Python better at handling large-scale data
Bodo.ai, a parallel compute platform for data workloads, is developing a compiler to make Python portable and efficient across multiple hardware platforms. It announced Wednesday a $14 million Series A funding round led by Dell Technologies Capital.
Python is one of the top programming languages used among artificial intelligence and machine learning developers and data scientists, but as Behzad Nasre, co-founder and CEO of Bodo.ai, points out, it is challenging to use when handling large-scale data.
Bodo.ai, headquartered in San Francisco, was founded in 2019 by Nasre and Ehsan Totoni, CTO, to make Python higher performing and production ready. Nasre, who had a long career at Intel before starting Bodo, met Totoni and learned about the project that he was working on to democratize machine learning and enable parallel learning for everyone. Parallelization is the only way to extend Moore’s Law, Nasre told TechCrunch.
Bodo does this via a compiler technology that automates the parallelization so that data and ML developers don’t have to use new libraries, APIs or rewrite Python into other programming languages or graphics processing unit code to achieve scalability. Its technology is being used to make data analytics tools in real time and is being used across industries like financial, telecommunications, retail and manufacturing.
“For the AI revolution to happen, developers have to be able to write code in simple Python, and that high-performance capability will open new doors,” Totoni said. “Right now, they rely on specialists to rewrite them, and that is not efficient.”
Joining Dell in the round were Uncorrelated Ventures, Fusion Fund and Candou Ventures. Including the new funding, Bodo has raised $14 million in total. The company went after Series A dollars after its product had matured and there was good traction with customers, prompting Bodo to want to scale quicker, Nasre said.
Nasre feels Dell Technologies Capital was “uniquely positioned to help us in terms of reserves and the role they play in the enterprise at large, which is to have the most effective salesforce in enterprise.”
Though he was already familiar with Nasre, Daniel Docter, managing director at Dell Technologies, heard about Bodo from a data scientist friend who told Docter that Bodo’s preliminary results “were amazing.”
Much of Dell’s investments are in the early-stage and in deep tech founders that understand the problem. Docter puts Totoni and Nasre in that category.
“Ehsan fits this perfectly, he has super deep technology knowledge and went out specifically to solve the problem,” he added. “Behzad, being from Intel, saw and lived with the problem, especially seeing Hadoop fail and Spark take its place.”
Meanwhile, with the new funding, Nasre intends to triple the size of the team and invest in R&D to build and scale the company. It will also be developing a marketing and sales team.
The company is now shifting from financing to customer- and revenue-focused as it aims to drive up adoption by the Python community.
“Our technology can translate simple code into the fast code that the experts will try,” Totoni said. “I joined Intel Labs to work on the problem, and we think we have the first solution that will democratize machine learning for developers and data scientists. Now, they have to hand over Python code to specialists who rewrite it for tools. Bodo is a new type of compiler technology that democratizes AI.”
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Rutter comes out of stealth with $1.5M in funding for its e-commerce API
Rutter, a remote-first company, is developing a unified e-commerce API that enables companies to connect with data across any platform.
On Friday the company announced it was emerging from stealth with $1.5 million in funding from a group of investors including Haystack, Liquid 2 and Basis Set Ventures.
Founders Eric Yu and Peter Zhou met in school and started working on Rutter, which Zhou called “Plaid for commerce,” in 2017 before going through the summer 2019 Y Combinator cohort.
They stumbled upon the e-commerce API idea while working in education technology last year. The pair were creating subscription kits and learning materials for parents concerned about how their children would be learning during the global pandemic. Then their vendor customers had problems listing their storefronts on Amazon, so they wrote scripts to help them, but found that they had to write separate scripts for each platform.
With Rutter, customers only need one script to connect anywhere. Its APIs connect to e-commerce platforms like Shopify, Walmart and Amazon so that tech customers can build functions like customer support and chatbots, Yu told TechCrunch.
Lan Xuezhao, founding and managing partner of Basis Set Ventures, said via email that she was “super excited” about Rutter first because of the founders’ passion, grit and speed of iteration to a product. She added it reminded her of another team that successfully built a business from zero to over $7 billion.
“After watching them (Rutter) for a few years, it’s clear what they built is powerful: it’s the central nervous system of online commerce,” Xuezhao added.
As the founders see it, there are two big explosions going on in e-commerce: the platform side with the adoption of headless commerce — the separating of front end and back end functions of an e-commerce site, and new companies coming in to support merchants.
The new funding will enable Yu and Zhou to build up their team, including hiring more engineers.
Due to the company officially launching at the beginning of the year, Yu did not disclose revenue metrics, but did say that Rutter’s API volume was doubling and tripling in the last few months. It is also supporting merchants that connect with over 5,000 stores.
Some of Rutter’s customers are building one aspect of commerce, like returns, warranties and checkouts, but Yu said that since Shopify represents just 10% of e-commerce, the company’s goal is to take merchants beyond the marketplace by being “that unified app store for merchants to find products.”
“We think that in the future, the e-commerce stack of a merchant will look like the SaaS stack of a software company,” Zhou added. “We want to be the glue that holds that stack together for merchants.”
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Stacker raises $20M Series A to help business units build software without coding
No-code platforms have developed into a hot market, and Stacker, a London-based no-code platform, is attempting to bring the concept to a new level. Not only can you create a web application from a spreadsheet, you can pull data from a variety of sources to create a sophisticated business application automatically (although some tweaking may be required).
Today the company announced a $20 million Series A led by Andreessen Horowitz, with participation from existing investors Initialized Capital, Y Combinator and Pentech. Today’s investment brings the total raised to $23 million, according to Crunchbase data.
Michael Skelly, CEO and co-founder at Stacker, says that the idea is to take key business data and turn it into a useful app to help someone do their job more efficiently. “[We enable] people in business to create apps to help them in their working life — so things like customer portals, internal tools and things that take the data they’re already using, often to run a process, and turn that into an app,” Skelly explained.
“We really think that in order to actually be useful for business, you need to be hooked into the data that a business cares about. And so we let people bring their spreadsheets, SQL databases, Salesforce data, bring all the data that they use to run their business, and automatically turn it into an app,” he said.
Once the company pulls that data in and creates an app, the user can begin to tweak how things look, but Stacker gives them a big head start toward creating something usable from the get-go, Skelly said.
Jennifer Li, a partner at lead investor Andreessen Horowitz, likes the startup’s approach to no-code. “We’ve been watching the no-code space for a while, and Stacker stands apart from the rest because of its thoughtful product approach, allowing business operators to instantly generate a functional app that perfectly fits existing business processes,” she said in a blog post announcing the funding round.
The company currently has 19 employees, with plans to put the new capital to work to reach 30-40 by the end of the year. Skelly sees building a diverse company as a key goal and is proactive and thoughtful about finding ways to achieve that. In fact, he has identified three ways to approach diversity.
“Firstly is just making sure that we get a diverse pipeline of people. I really think that the ratio of the people you talk to is probably going to be the biggest indicator of the people you hire. Secondly we try to find ways we can hire people who are maybe further down their career profile, but [looking] to grow,” he said.
Thirdly, and I think this is something that is not talked about enough, there are plenty of people who would like to get into programming roles, and who are underrepresented, and so we have members of our team who are converting from various non-technical roles to DevOps — and I think it’s just like a really great route to add to the overall pool [of diverse candidates],” he said.
The company is remote-first, with Skelly in London and his co-founder based in Geneva, and they intend to stay that way. They founded the company in 2017 and originally created a different product that was much more complex and required a lot of hand holding before eventually concluding that making it simple was the way to go. They released the first version of the current product at the end of 2019.
The company has a big vision to be the software development tool for business units. “We really think that in the future just like everyone’s got email, a chat tool, a spreadsheet and a video conferencing tool nowadays, they will also have a software tool where they write and run the custom software that they run their business on,” he said.
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ThirdAI raises $6M to democratize AI to any hardware
Houston-based ThirdAI, a company building tools to speed up deep learning technology without the need for specialized hardware like graphics processing units, brought in $6 million in seed funding.
Neotribe Ventures, Cervin Ventures and Firebolt Ventures co-led the investment, which will be used to hire additional employees and invest in computing resources, Anshumali Shrivastava, Third AI co-founder and CEO, told TechCrunch.
Shrivastava, who has a mathematics background, was always interested in artificial intelligence and machine learning, especially rethinking how AI could be developed in a more efficient manner. It was when he was at Rice University that he looked into how to make that work for deep learning. He started ThirdAI in April with some Rice graduate students.
ThirdAI’s technology is designed to be “a smarter approach to deep learning,” using its algorithm and software innovations to make general-purpose central processing units (CPU) faster than graphics processing units for training large neural networks, Shrivastava said. Companies abandoned CPUs years ago in favor of graphics processing units that could more quickly render high-resolution images and video concurrently. The downside is that there is not much memory in graphics processing units, and users often hit a bottleneck while trying to develop AI, he added.
“When we looked at the landscape of deep learning, we saw that much of the technology was from the 1980s, and a majority of the market, some 80%, were using graphics processing units, but were investing in expensive hardware and expensive engineers and then waiting for the magic of AI to happen,” he said.
He and his team looked at how AI was likely to be developed in the future and wanted to create a cost-saving alternative to graphics processing units. Their algorithm, “sub-linear deep learning engine,” instead uses CPUs that don’t require specialized acceleration hardware.
Swaroop “Kittu” Kolluri, founder and managing partner at Neotribe, said this type of technology is still early. Current methods are laborious, expensive and slow, and for example, if a company is running language models that require more memory, it will run into problems, he added.
“That’s where ThirdAI comes in, where you can have your cake and eat it, too,” Kolluri said. “It is also why we wanted to invest. It is not just the computing, but the memory, and ThirdAI will enable anyone to do it, which is going to be a game changer. As technology around deep learning starts to get more sophisticated, there is no limit to what is possible.”
AI is already at a stage where it has the capability to solve some of the hardest problems, like those in healthcare and seismic processing, but he notes there is also a question about climate implications of running AI models.
“Training deep learning models can be more expensive than having five cars in a lifetime,” Shrivastava said. “As we move on to scale AI, we need to think about those.”
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No-code is code
Today, the release of OpenAI Codex, a new Al system that translates natural language to code, marks the beginning of a shift in how computer software is written.
Over the past few years, there’s been growing talk about “no code” platforms, but this is no new phenomenon. The reality is, ever since the first programmable devices, computer scientists have regularly developed breakthroughs in how we “code” computer software.
The first computers were programmed with switches or punch cards, until the keyboard was invented. Coding became a matter of typing numbers or machine language, until Grace Hopper invented the modern compiler and the COBOL language, ushering in decades of innovation in programming languages and platforms. Languages like Fortran, Pascal, C, Java and Python evolved in a progression, where the newest language (built using an older language) enabled programmers to “code” using increasingly more human language.
Alongside languages, we’ve seen the evolution of “no-code” platforms — including Microsoft Excel, the 1980s granddaddy of no-code — that empower people to program computers in a visual interface, whether in school or in the workplace. Anytime you write a formula in a spreadsheet, or when you drag a block of code on Code.org or Scratch, you’re programming, or “coding,” a computer. “No code” is code. Every decade, a breakthrough innovation makes it easier to write code so that the old way of coding is replaced by the new.
Does this mean coding is dead? No! It doesn’t replace the need for a programmer to understand code. It means coding just got much easier, higher impact and thus more important.
This brings us to today’s announcement. Today, OpenAl announced OpenAI Codex, an entirely new way to “write code” in the natural English language. A computer programmer can now use English to describe what they want their software to do, and OpenAl’s generative Al model will automatically generate the corresponding computer code, in your choice of programming language. This is what we’ve always wanted — for computers to understand what we want them to do, and then do it, without having to go through a complex intermediary like a programming language.
But this is not an end, it is a beginning. With Al-generated code, one can imagine an evolution in every programming tool, in every programming class, and a Cambrian explosion of new software. Does this mean coding is dead? No! It doesn’t replace the need for a programmer to understand code. It means coding just got much easier, higher impact and thus more important, just as when punch cards were replaced by keyboards, or when Grace Hopper invented the compiler.
In fact, the demand for software today is greater than ever and will only continue to grow. As this technology evolves, Al will play a greater role in generating code, which will multiply the productivity and impact of computer scientists, and will make this field accessible to more and more computer programmers.
There are already tools that let you program using only drag-and-drop, or to write code using your voice. Improvements in these technologies and new tools, like OpenAI Codex, will increasingly democratize the ability to create software. As a result, the amount of code — and the number of coders — in the world will increase.
This also means that learning how to program — in a new way — is more important than ever. Learning to code can unlock doors to opportunity and also help solve global problems. As it becomes easier and more accessible to create software, we should give every student in every school the fundamental knowledge to not only be a user of technology but also a creator.
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Former Facebook teammates raise $10.4M in Sequoia-led round to launch features development
Statsig is taking the A/B testing applications that drive Facebook’s growth and putting similar functionalities into the hands of any product team so that they, too, can make faster, data-informed decisions on building products customers want.
The Seattle-based company on Thursday announced $10.4 million in Series A funding, led by Sequoia Capital, with participation from Madrona Venture Group and a group of individual investors, including Robinhood CPO Aparna Chennapragada, Segment co-founder Calvin French-Owen, Figma CEO Dylan Field, Instacart CEO Fidji Simo, DoorDash exec Gokul Rajaram, Code.org CEO Hadi Partovi and a16z general partner Sriram Krishnan.
Founder and CEO Vijaye Raji started the company with seven other former Facebook colleagues in February, but the idea for the company started more than a year ago.
He told TechCrunch that while working at Facebook, A/B testing applications, like Gatekeeper, Quick Experiments and Deltoid, were successfully built internally. The Statsig team saw an opportunity to rebuild these features from scratch outside of Facebook so that other companies that have products to build — but no time to build their own quick testing capabilities — can be just as successful.
Statsig’s platform enables product developers to run quick product experiments and analyze how users respond to new features and functionalities. Tools like Pulse, Experiments+ and AutoTune allow for hundreds of experiments every week, while business metrics guide product teams to build and ship the right products to their customers.
Raji intends to use the new funding to hire folks in the area of design, product, data science, sales and marketing. The team is already up to 14 since February.
“We already have a set of customers asking for features, and that is a good problem, but now we want to scale and build them out,” he added.
Statsig has no subscription or upfront fees and is already serving millions of end-users every month for customers like Clutter, Common Room and Take App. The company will always offer a free tier so customers can try out features, but also offers a Pro tier for 5 cents per thousand events so that when the customer grows, so does Statsig.
Raji sees adoption of Statsig coming from a few different places: developers and engineers that are downloading it and using it to serve a few million people a month, and then through referrals. In fact, the adoption the company is getting is “bottom up,” which is what Statsig wants, he said. Now the company is talking to bigger customers.
There are plenty of competitors for this product, including incumbents in the market, according to Raji, but they mostly focus on features, while Statsig provides insights and ties metrics back to features. In addition, the company has automated analysis where other products require manual set up and analysis.
Sequoia partner Mike Vernal worked at Facebook prior to joining the venture capital firm and had worked with Raji, calling him “a top 1% engineer” that he was happy to work with.
Having sat on many company boards, he has found that many companies spend a long time talking about sales and marketing, but very little on product because there is not an easy way to get precise numbers for planning purposes, just a discussion about what they did and plan to do.
What Vernal said he likes about Statsig is that the company is bringing that measurement aspect to the table so that companies don’t have to hack together a poorer version.
“What Statsig can do, uniquely, is not only set up an experiment and tell if someone likes green or blue buttons, but to answer questions like what the impact this is of the experiment on new user growth, retention and monitorization,” he added. “That they can also answer holistic questions and understand the impact on any single feature on every metric is really novel and not possible before the maturation of the data stack.”
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