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For open-source programs, AI coding tools are a mixed blessing

A world that runs on increasingly powerful AI coding tools is one where software creation is cheap — or so the thinking goes — leaving little room for traditional software companies. As one analyst report put it, “vibe coding will allow startups to replicate the features of complex SaaS platforms.”

Cue the hand-wringing and declarations that software companies are doomed.

Open-source software projects that use agents to paper over long-standing resource constraints should logically be among the first to benefit from the era of cheap code. But that equation just doesn’t quite stick. In practice, the impact of AI coding tools on open source software has been far more mixed.

AI coding tools have caused as many problems as they have solved, according to industry experts. The easy-to-use and accessible nature of AI coding tools has enabled a flood of bad code that threatens to overwhelm projects. Building new features is easier than ever, but maintaining them is just as hard and threatens to further fragment software ecosystems.

The result is a more complicated story than simple software abundance. Perhaps, the predicted, imminent death of the software engineer in this new AI era is premature.

Quality vs quantity

Across the board, projects with open codebases are noticing a decline in the average quality of submissions, likely a result of AI tools lowering barriers to entry.

“For people who are junior to the VLC codebase, the quality of the merge requests we see is abysmal,” Jean-Baptiste Kempf, the CEO of the VideoLan Organization that oversees VLC, said in a recent interview.

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Kempf is still optimistic about AI coding tools overall but says they’re best “for experienced developers.” 

There have been similar problems for Blender, a 3D modeling tool that has been maintained as open source since 2002. Blender Foundation CEO Franceso Siddi said LLM-assisted contributions typically “wasted reviewers’ time and affected their motivation.” Blender is still developing an official policy for AI coding tools, but Siddi said they are “neither mandated nor recommended for contributors or core developers.”

The flood of merge requests has gotten so bad that open-source developers are building new tools to manage it.

Earlier this month, developer Mitchell Hashimoto launched a system that would limit GitHub contributions to “vouched” users, effectively closing the open-door policy for open-source software. As Hashimoto put it in the announcement, “AI eliminated the natural barrier to entry that let OSS projects trust by default.”

The same effect has emerged in bug bounty programs, which give outside researchers an open door to report security vulnerabilities. The open-source data transfer program cURL recently halted its bug bounty program after being overwhelmed by what creator Daniel Stenberg described as “AI slop.”

“In the old days, someone actually invested a lot of time [in] the security report,” Stenberg said at a recent conference. “There was a built-in friction, but now there’s no effort at all in doing this. The floodgates are open.”

It’s particularly frustrating because many of open-source projects are also seeing the benefits of AI coding tools. Kempf says it’s made building new modules for VLC far easier, provided there’s an experienced developer at the helm.

“You can give the model the whole codebase of VLC and say, ‘I’m porting this to a new operating system,’” Kempf said. “It is useful for senior people to write new code, but it’s difficult to manage for people who don’t know what they’re doing.”

Competing priorities

The bigger problem for open-source projects is a difference in priorities. Companies like Meta value new code and products, while open-source software work focuses more on stability.

“The problem is different from large companies to open-source projects,” Kempf commented. “They get promoted for writing code, not maintaining it.”

AI coding tools are also arriving at a moment when software, in general, is particularly fragmented.

Open source investor Konstantin Vinogradov says AI tools are running into a long-standing trend in open-source engineering.

“On the one hand, we have exponentially growing code base with exponentially growing number of interdependences, And on the other hand, we have number of active maintainers, which is maybe slowly growing, but definitely not keeping up,” Vinogradov said. “With AI, both parts of this equation accelerated.”

It’s a new way of thinking about AI’s impact on software engineering — one with alarming implications for the industry at large.

If you see engineering as the process of producing working software, AI coding makes it easier than ever. But if engineering is really the process of managing software complexity, AI coding tools could make it harder. At the very least, it will take a lot of active planning and work to keep the sprawling complexity in check.

For Vinogradov, the result is a familiar situation for open-source projects: a lot of work to do, and not enough good engineers to do it.

“AI does not increase the number of active, skilled maintainers,” he remarked. “It empowers the good ones, but all the fundamental problems just remain.”

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Reliance unveils $110B AI investment plan as India ramps up tech ambitions

Mukesh Ambani, the billionaire chairperson of Indian conglomerate Reliance, on Thursday unveiled the group’s ₹10 trillion (about $110 billion) plan to build AI computing infrastructure in India over the next seven years.

Speaking at the India AI Impact Summit in New Delhi on Thursday, Ambani said the investment would fund gigawatt-scale data centers, a nationwide edge computing network, and new AI services integrated with Reliance’s Jio telecom platform.

Reliance has already begun construction of multi-gigawatt data centers in Jamnagar, Gujarat, Ambani said, and more than 120 megawatts of capacity is expected to come online in the second half of 2026.

Ambani’s pledge adds to a growing wave of AI investment in India. Earlier this week, Adani Group outlined plans to invest about $100 billion to build AI data centers in the country, and the Indian government expects more than $200 billion in AI infrastructure spending over the next two years.

Global technology firms are also stepping up their presence, with OpenAI partnering with the Tata Group to develop about 100 megawatts of AI capacity in the country, and plans to scale that to 1 gigawatt eventually.

Ambani said the push is essential for India’s technological self-reliance, saying the country “cannot afford to rent intelligence,” and that Reliance aims to cut the cost of AI services as dramatically as it once reduced mobile data prices in the country.

“The biggest constraint in AI today is not talent or imagination,” Ambani said. “It is scarcity and high cost of compute.”

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The build-out, Ambani said, would be supported by Reliance’s green energy capacity, which stretches to 10 gigawatts of surplus power from solar projects in Gujarat and Andhra Pradesh.

Reliance will partner with Indian enterprises, startups, and academic institutions to embed AI in industries ranging from manufacturing and logistics to agriculture, healthcare and financial services.

Jio has already been striking AI partnerships: it last year landed a deal with Google to offer free Gemini AI Pro access to millions of its users in India.

Reliance also plans to develop AI capabilities in several Indian languages to spur adoption of the tech, Ambani said.

The aggressive push highlights how India’s largest conglomerates are racing to secure a foothold in what is expected to be one of the country’s biggest technology opportunities.

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This VC’s best advice for building a founding team

One of the most consequential decisions early-stage founders have to make is who they will bring on as their founding team. The first five to 10 employees will have a massive impact on the company culture, and the precedents set with them are difficult to change down the road. That’s why this season on Build Mode, we’re diving into what it takes to build a world-class founding team. 

To kick off season two, Isabelle Johannessen is joined by Yuri Sagalov, managing director at General Catalyst and former founder, YC partner, and seed investor at Wayfinder Ventures. Sagalov has worked with hundreds of pre-seed and seed-stage companies and has seen firsthand the best (and worst) ways to hire in the early days.

In this episode, Sagalov offers his best pieces of advice for founders who are hiring their first team, strategically building their cap table, and forming compensation structures that can scale with the company.

The three types of investors (and which one to avoid) 

Sagalov categorizes investors into three main buckets: the ones that are heavily involved and function as an extension of your team, the ones that will give you a check and then vanish, and the micromanagers. 

The first type of investor is the most valuable according to Sagalov: “They’re going to help you with recruiting, hiring, go to market. And the most interesting thing with those investors is often it’s completely disconnected from the check size.”

Although it may feel counterintuitive to turn down investments, working with VCs who will become overly involved in the process may cause more harm than good in the long run. Sagalov said, “The only bucket that I avoid is this third bucket of investors who give you money and they’re kind of in your kitchen, meddling. They have an opinion on everything. They get stressed out when things don’t go right.”

In a fundraise, everyone is putting their best foot forward, so Sagalov suggests reaching out to current portfolio companies before committing to an investor.  “The best thing you can do as a founder is actually talk to portfolio companies, talk to other founders that they’ve worked with, ask for concrete examples of how they’ve been helpful, if they’ve been helpful, and then actually ask how they were when things didn’t go right.”

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How to split equity with a co-founder 

As an investor, Sagalov looks for co-founders who have created an equity split that is fair but also hedges for future misalignments. He suggests creating a slight differentiation by plus or minus one share so there is a clear way to break deadlocks. 

Sagalov also reminds early-stage founders that these early decisions have staying power: “Oftentimes founders over-index on ‘I came up with the idea, so I deserve the lion’s share.’  Most of the journey of the company is ahead of them. You don’t want someone waking up five years from now feeling like they put in equal blood, sweat, and tears but own one-fifth of the equity.”

Talk to early employees about risk and compensation 

The first few hires should be all in on the startup’s mission. Oftentimes, joining an early-stage startup can be perceived as risky. Sagalov emphasized the importance of discussing the risks and potential benefits: “Fundamentally, what you’re looking for when you’re hiring the first few people are missionaries who, beyond even the compensation, want to join you for the mission of the business,” he said. “You want to be honest with them that there is a lot of risk on the journey.”

Next week on Build Mode, we’re talking with Sarah Lucena, the founder and CEO of Mappa, who discusses how founders can take compatibility into account and hire the right fit for the team the first time, using their AI tool. 

Isabelle Johannessen is our host. Build Mode is produced and edited by Maggie Nye. Audience Development is led by Morgan Little. And a special thanks to the Foundry and Cheddar video teams. 

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Freeform raises $67M Series B to scale up laser AI manufacturing 

Tech investors haven’t given up on the dream of making physical products with the same speed and ease as coding software. 

Executives at Freeform, a startup developing a novel 3D printing system for metal components, told TechCrunch that the company raised a $67 million Series B to expand its manufacturing platform. 

Investors include Apandion, AE Ventures, Founders Fund, Linse Capital, NVidia’s NVentures , Threshold Ventures, and Two Sigma Ventures. FreeForm declined to disclose the company’s post-financing valuation, which Pitchbook cites as $179 million.

CEO and cofounder Erik Palitsch said the funding would allow the company to upgrade its current GoldenEye printing system, which uses 18 lasers to fuse metal powders into precision components, to a new version. Dubbed Skyfall, the next iteration of the platform would use hundreds of lasers to produce thousands of kilograms of metal parts each day. 

That’s the culmination of a vision Palitsch launched in 2018 after developing rocket engines at SpaceX, where they found that industrial machines for printing metal components are expensive, finicky, and not well designed for mass manufacturing. 

Their new company would build its platform from the ground up to achieve higher throughput and flexibility, with an emphasis on active software controls. Palitsch says Freeform’s platform is “AI native,” noting a partnership with Nvidia that allows the company to access advanced GPUs.

“I think we’re the only quote-unquote manufacturing company out there that has H200 clusters in a data center on site,” Paltisch told TechCrunch. “What are they doing? We’re running real-time physics-based simulations and learning all the different aspects of the end to end manufacturing workflow.”

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The data collected by sensors in the company’s manufacturing platform and during the simulations allows Freeform to rapidly improve production quality and quantity. 

“We have more meaningful data on the physics of the metal-printing process than any company in the world,” head of talent Cameron Kay said. 

While Palitsch said he could not disclose any customers, he said the company is already delivering hundreds of “mission-critical” parts to buyers. Now, the company wants to hire as many as 100 new employees and expand its facility to start executing on its contract backlog. 

Manufacturing-as-a-service has grown as a category as venture investors have taken a greater interest in building vehicles, robots, and energy production systems. For example, Hadrian recently earned a $1.6B valuation from its investors while developing automated production for defense, and VulcanForms and Divergent have raised hundreds of millions to develop metal-printing services of their own.

This piece has been updated to reflect former president Thomas Ronacher’s departure from Freeform.

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