According to the paper, OpenAI believes the gains from A.I. could otherwise become concentrated among a small number of companies, including firms like OpenAI itself. The document warns that without intervention, A.I. could widen inequality by rewarding those already positioned to benefit while leaving other workers and communities behind. 

The company’s proposal goes beyond broad rhetoric. In the document, OpenAI says a public fund could be seeded through cooperation between policymakers and A.I. companies, then invested in long-term assets tied both to A.I. firms and to the wider economy adopting the technology. Returns from that fund, it says, could be distributed directly to citizens, giving more Americans a share of A.I. wealth regardless of whether they already own stocks or other financial assets. 

OpenAI also argues that the tax system may need to change as A.I. shifts economic activity away from wages and toward corporate profits, capital gains and automated labor. The paper says policymakers could respond by increasing reliance on capital-based taxes, considering targeted levies on sustained A.I.-driven returns and exploring taxes related to automation, while preserving funding for programs such as Social Security, Medicaid and housing assistance. 

The proposal is notable not only because of its ambition, but because it comes from one of the companies racing to build the technology likely to cause the disruption. OpenAI describes the paper as an early, exploratory set of ideas rather than a final blueprint, but its message is clear: if A.I. creates enormous wealth, the public should not be left watching from the sidelines.

According to Utah’s agreement with Legion Health, the AI can handle renewals only for patients already taking approved medications and must escalate many cases to a human clinician. The state says the system is barred from prescribing controlled substances, benzodiazepines, antipsychotics and other higher-risk drugs, and must route patients to a licensed professional if it detects suicidality, severe side effects, mania, pregnancy or simply a request for human review. 

Utah has been careful to frame the move as regulatory mitigation, not endorsement. In the state’s own description, the pilot is meant to test whether AI can safely reduce bottlenecks around routine prescription renewals in a controlled environment, while gathering data before any permanent legal changes are considered. The Office of AI Policy says most counties in Utah face mental health provider shortages, and that automating low-risk renewals could free clinicians to focus on more complex patients. 

The safeguards are extensive. For the first 250 requests, a licensed physician must review the AI’s recommendation before anything is sent to the pharmacy, and the company must exceed a 98% agreement rate with human reviewers. The next 1,000 cases are subject to retrospective review with a required 99% agreement rate before the system can move into ongoing monthly sampling and reporting to the state. Utah also says prescriptions generated through the pilot still carry the name of a licensed physician.

The Ring Pro is central to Ultrahuman’s comeback strategy, featuring a redesigned unibody metal structure that helped the startup secure U.S. clearance. The new device boasts improvements such as longer battery life and enhanced on-device processing, and is available for U.S. pre-orders starting at $399. Kumar said

“We believe the Ring Air is a non-infringing model, and we are fighting that in federal court in the U.S.”

The new approval follows an October ruling by the U.S. International Trade Commission in favor of Oura, that significantly restricted imports of Ultrahuman’s earlier Ring Air model.This decision resulted in as much as $50 million in lost sales, according to CEO Mohit Kumar. The U.S. continues to be the most vital market for smart rings, representing about 60% of the 4.4 million units sold globally in 2025.

The period of import restrictions has also caused rapid market consolidation in favor of Oura. The company capitalized on Ultrahuman’s absence, increasing its U.S. market share from 63.3% to 85%, while Ultrahuman’s share plummeted to low single digits from its peak of 24.6% in Q2 2025. The U.S. market previously accounted for up to 50% of Ultrahuman’s revenue. The company plans an immediate and aggressive rollout of the Ring Pro. According to Kumar it will take five to six months to reach full scale as it rebuilds its supply chain and distribution.

Image: Ultrahuman

He stated he’s pursuing this project because semiconductor manufacturers are not producing chips fast enough to meet his companies’ AI and robotics demands. Musk said:

“We either build the Terafab or we don’t have the chips, and we need the chips, so we build the Terafab.”

According to Bloomberg Musk shared his plans during an event in downtown Austin, Texas, with a photo indicating that the “Terafab” facility will be “gigafactory,” located near Tesla’s Austin headquarters.

He also added that the aim is to produce chips capable of supporting 100–200 gigawatts of computing power annually on Earth, as well as one terawatt in space. He did not provide a timeline for the plan.

Image: Presentation of the Terafab project

This was evident in recent selection process for the joint AI accelerator for Indian startups run by Google and venture firm Accel. According to Accel partner Prayank Swaroop, among more than 4,000 applications, “wrapper” ideas were prevalent— none were included among the five startups chosen for the latest cohort, TechCrunch reports.

The AI-focused Atoms program, launched in November by Google and Accel, aims to support early-stage startups developing AI products tied to India. The selected startups will receive up to $2 million in funding from Accel and Google’s AI Futures Fund, along with up to $350,000 in Google cloud and AI compute credits.

According to Swaroop around 70% of the rejected applications were “wrappers”—startups adding AI features like chatbots to existing software without rethinking workflows through AI.

He added that many of the startups focused on marketing automation and AI recruiting tools, where investors saw limited differentiation.

India’s expanding AI ecosystem continues to prioritize enterprise applications – approximately 62% of applications targeted productivity tools, and around 13% focused on software development and coding. Roughly three-quarters of applications were enterprise-focused rather than consumer products, though Swaroop had hoped for more proposals in healthcare and education.

Jonathan Silber, co-founder and director of Google’s AI Futures Fund, said the five selected startups align closely with areas where Google expects AI to see deeper real-world adoption. Many combine multiple models depending on the workflow, with the goal of providing Google with feedback on real-world performance.

The startups selected this year are:

• K-Dense, developing an AI “co-scientist” to accelerate research in life sciences and chemistry;
• Dodge.ai, building autonomous agents for enterprise ERP systems;
• Persistence Labs, focused on voice AI for call center operations;
• Zingroll, creating a platform for AI-generated films and shows;
• Level Plane, applying AI to industrial automation in automotive and aerospace manufacturing.

Image: Prayank Swaroop LinkedIn Profile

Formula 1 has always sold itself as the pinnacle of motorsport. That description still fits, but it no longer goes far enough. In 2026, F1 is better understood as one of the world’s most compressed and visible innovation environments, a place where artificial intelligence, cloud computing, advanced fuels, digital twins, real-time analytics, and high-performance engineering converge under conditions that leave no room for delay, redundancy, or imprecision. The sport is still about speed. But increasingly, speed is the output of something bigger: a technology stack. 

That is what makes Formula 1 so compelling for professional technology and innovation media. Unlike many sectors where experimentation happens quietly inside labs or behind enterprise procurement cycles, F1 innovation unfolds in public. It is measurable in lap time, visible in race strategy, constrained by regulation, and judged every weekend against the toughest benchmark available: competitive performance. Every design choice, every model, every simulation, and every operational decision must stand up not in theory, but in motion.

Why the 2026 Rules Matter

The sport’s next chapter makes that clearer than ever. Formula 1’s 2026 rules package is one of the largest technical resets in years, built around redesigned cars, active aerodynamics, smarter energy deployment, and advanced sustainable fuels. Formula1.com says the new era will bring cars that are more challenging for teams and drivers, while relying on “advanced sustainable fuel and smarter energy use.” The new fuels themselves are made from sources such as carbon capture, municipal waste, and non-food biomass, and Formula 1 says they are independently certified to meet strict sustainability standards. 

This matters because Formula 1 is no longer innovating in isolated technical categories. It is innovating across systems. The crossing point between technology and innovation in F1 is not simply the car. It is the way mechanical engineering, software, energy systems, manufacturing, logistics, and media now operate as one connected performance architecture. A faster car still matters, of course, but so does the quality of the simulation environment that predicted its behavior, the cloud infrastructure that processed its data, the machine-learning tools that surfaced anomalies, and the human-machine workflows that turned data into decisions on race day. 

AI, Cloud, and the Race Weekend Brain

In that sense, Formula 1 increasingly resembles the broader economy. Many modern industries are moving toward software-defined operations, where physical assets are shaped by digital models and strategic advantage comes from linking data, compute, and execution. F1 just gets there first, and under more intense conditions.

One of the clearest examples is the sport’s deepening use of AI and cloud technologies. Through its work with AWS, Formula 1 has been building tools that do far more than decorate a broadcast. AWS says F1’s Track Pulse uses machine learning and generative AI to give the broadcast team a clearer real-time picture of on-track action, including live driver battles, top speeds, and predictive storytelling cues. This is significant not only as a fan product, but as evidence of how AI is changing the way complex live systems are interpreted and packaged. In a data-rich environment, the challenge is no longer collecting information. It is deciding what matters in time to act on it. 

Digital Twins and Simulation at Speed

That same principle applies even more sharply inside teams. The modern F1 operation is saturated with telemetry, historical comparisons, environmental variables, and strategic possibilities. The competitive edge comes from filtering that information intelligently and turning it into high-confidence decisions in seconds. McLaren’s work with Deloitte offers a strong example of this shift. Deloitte says it helped advance McLaren’s digital twin simulation technology to run 30,000 simulations per second while extracting actionable insights from more than a million data points captured during each race. On race day, that simulation environment can analyze millions of possible scenarios and help shape calls on pit stops, tire strategy, and fuel management. 

That is not motorsport as most audiences once understood it. It is operational intelligence in a high-speed setting. And it closely mirrors where many technology-intensive businesses are heading: toward decision environments in which digital twins, scenario modeling, and real-time analytics support human judgment rather than replace it. Formula 1 demonstrates that innovation is not just about building better systems; it is about building systems that help people make better decisions under pressure.

Sustainability as a Performance Challenge

The 2026 rule changes also reinforce F1’s growing role as a laboratory for the energy transition. Formula 1’s own sustainability material makes clear that advanced sustainable fuel is not a side project or symbolic gesture. It is embedded in the sport’s larger decarbonization strategy. According to Formula 1’s 2025 Sustainability Update, the sport had reduced its carbon emissions by 26% by the end of 2024 compared with its 2018 baseline, despite substantial growth in races, attendance, and audience. The report says Formula 1 is “on track” for its Net Zero by 2030 target, and notes that from 2026 advanced sustainable fuel will be introduced in Formula 1 cars as part of a broader effort that also includes green energy, logistics changes, and sustainable aviation fuel. 

This is where Formula 1’s innovation model becomes especially relevant beyond racing. In many sectors, sustainability is still handled as a compliance layer added after the core engineering work is done. In F1, sustainability is increasingly becoming a design constraint and a performance problem to solve. The fuel cannot simply be cleaner on paper; it must work at the highest level of competition. It must operate in engines built for extreme stress. It must satisfy engineers, suppliers, regulators, and manufacturers at the same time. Formula 1’s explanation of the new fuels emphasizes that they are “drop-in” fuels, designed to replace fossil equivalents without requiring engine redesign in road-relevant contexts. That does not mean F1 alone will transform global transport, but it does mean the sport is helping move sustainable-fuel development from concept to credibility.

The Car as an Integrated System

The same can be said of aerodynamics and vehicle systems. Formula 1’s 2026 framework is designed not only to preserve speed, but to rethink how speed is generated and managed. Active aero, tighter energy management, and new control tools shift the competitive focus toward more dynamic system optimization. This makes the car itself more software-mediated and the race weekend more analytically demanding. Teams will have to balance aerodynamic efficiency, energy deployment, and racecraft in ways that make integrated systems thinking more important than ever.

That evolution is easy to miss if F1 is viewed only through its glamour or its spectacle. But from a technology standpoint, it is one of the sport’s most important transitions. The old image of Formula 1 innovation was built around visible hardware breakthroughs: dramatic wings, exotic materials, iconic engines. The new image is more distributed. It includes simulation environments, machine-learning layers, cloud-native collaboration, sustainability engineering, and the increasingly sophisticated translation of race data into decisions and products.

Innovation Beyond the Garage

Even the fan experience now reflects that shift. Formula 1 is not just building faster cars; it is building a smarter media platform around the race itself. The AWS-F1 partnership shows how digital infrastructure and AI are being used not merely to report what happened, but to anticipate storylines, visualize complex race dynamics, and make a highly technical sport more legible in real time. That has implications far beyond entertainment. It points to a future in which data-heavy environments, from industrial operations to financial systems, increasingly rely on AI-assisted narrative layers to help human users understand fast-changing conditions. 

There is also a business lesson in the way Formula 1 structures innovation. Contrary to the popular assumption that more resources automatically produce better outcomes, F1 thrives on constraints. Development is limited by financial rules, technical rules, and testing restrictions. That forces efficiency. It rewards teams that build tighter feedback loops between modeling and reality, between design and manufacturing, between operations and post-race learning. In the wider technology economy, where many organizations are trying to do more with less while still delivering transformation, that discipline may be one of Formula 1’s most transferable advantages. 

Why F1 Matters to the Innovation Economy

This is why Formula 1 deserves to be taken seriously not only as a motorsport property, but as a strategic lens on innovation itself. It shows what happens when multiple technologies mature at once and are forced to interact in a real-world system. It shows how AI becomes useful when attached to urgent decisions. It shows how sustainability becomes meaningful when it is tied to performance. It shows how digital twins become valuable when they inform actions rather than dashboards. And it shows how competitive pressure can accelerate the fusion of software, hardware, energy, and experience design.

Formula 1 is often described as the future arriving early. In 2026, that idea feels less like a slogan and more like an operating model. At the crossing point of technology and innovation, Formula 1 is no longer simply a showcase for advanced engineering. It is a proving ground for how modern systems are built, optimized, and understood. For anyone trying to track where high-performance innovation is really heading, the paddock is no longer a niche place to look. It is one of the best places to start. 

Images: Formula1.com News

The capability was announced on X by Anthropic engineer Thariq Shihipar, and is currently live for about 5% of users, with a wider rollout planned over the coming weeks.

Developers can enable the feature by typing /voice, then speak instructions for Claude Code to execute — for example, asking it to “refactor the authentication middleware.” Limitations and technical details have not been fully disclosed, including whether there are caps on voice interactions or whether a third-party voice provider is involved.

Image: Thariq Shihipar on X, Anthropic

At a time when many teams report that AI has accelerated code production faster than it has sped up the rest of the software lifecycle, Cursor is making the case that the bottleneck has moved: not writing code, but reviewing it, keeping it healthy, and responding when it breaks. Automations, the company argues, are meant to help scale those “other parts of the development lifecycle” that haven’t kept pace with agent-driven coding.

From “agent in your editor” to “agent in your pipeline”

Cursor describes Automations as cloud agents that spin up on demand in a cloud sandbox, follow instructions defined by the user, and “verify” their own output. The same automations can be configured with the models and MCPs (Model Context Protocol servers) a team already uses, and Cursor says agents can also use a memory tool to learn from past runs and improve over time.

The product framing is straightforward: define a trigger, write the prompt, choose the tools the agent can access—and then let it run continuously in the background. In the company’s forum announcement, Cursor highlights the breadth of actions these agents can take, including opening pull requests, commenting on code, sending Slack messages, calling MCP servers, and using “Memories” across runs.

For teams that have already experimented with Cursor’s agentic workflows, Automations is essentially a shift in posture: agents aren’t just helpers you invoke; they become workers you schedule.

The practical use cases: review, monitoring, and “chores”

Cursor’s launch post groups early automations into two buckets: review/monitoring and chores (recurring engineering tasks and cross-tool knowledge work).

Review and monitoring: agents as continuous codeowners

Cursor’s first claim is that automations can review changes at scale—from “style nits” to “security vulnerabilities and performance regressions.”

The company points to Bugbot as the conceptual predecessor: a review agent that runs when PRs are opened or updated, “triggered thousands of times a day,” and which Cursor says has “caught millions of bugs” since its launch. Automations, Cursor argues, generalizes that idea so teams can create many specialized reviewers.

Cursor shares three internal examples:

• Security review automation triggered on every push to main, built to run longer (without blocking PR flow), audit diffs for vulnerabilities, avoid re-litigating issues already discussed in the PR, and post high-risk findings to Slack. Cursor says this has already caught “multiple vulnerabilities and critical bugs.”
• “Agentic codeowners” automation that classifies PR risk based on blast radius, complexity, and infrastructure impact; auto-approves low-risk PRs; assigns up to two reviewers for higher-risk changes based on contribution history; then summarizes decisions in Slack and logs them to Notion via MCP for audit and tuning.
• Incident response automation triggered by PagerDuty, using Datadog via MCP to investigate logs, checking the repo for recent changes, and notifying on-call engineers in Slack—along with a PR proposing a fix. Cursor says this “significantly reduced” incident response time.

The pattern is notable: Cursor is not positioning these agents as simply generating suggestions, but as executing a loop—classify → investigate → act → write artifacts (Slack message/Notion log/PR)—that maps closely to how engineering teams actually operate.

Chores: daily and weekly agent work that stitches tools together

Cursor’s second bucket covers recurring tasks, including:

• Weekly Slack digest summarizing meaningful repo changes over seven days, highlighting major merged PRs, bug fixes, technical debt, and security/dependency updates.
• Test coverage automation that runs every morning to identify coverage gaps, add tests following existing conventions, run relevant test targets, and open a PR.
• Bug report triage, referenced as another chores category where agents can help consolidate and process incoming issues.

This category is where “always-on” becomes more than a slogan: the work isn’t prompted by a developer thinking to ask, but by time, process, or operational signals.

Early signals: Rippling and the rise of personal “ops agents”

Cursor’s post also includes a real-world example from Rippling, where a staff engineer describes building automations as a personal assistant layer on top of daily work streams. In Cursor’s telling, the workflow looks like this: drop meeting notes, action items, Loom links, and TODOs into a Slack channel; then a cron-based automation runs every two hours, reads those items alongside GitHub PRs, Jira issues, and Slack mentions, deduplicates them, and posts a dashboard.

Rippling also uses Slack-triggered automations to turn threads into Jira issues and summarize discussions into Confluence, extending into tasks like incident triage, weekly status reports, and on-call handoff.

That kind of “personal operations layer” is a strong indicator of where teams may take this next: not just making CI smarter, but making the organization more legible by having agents continuously translate conversations and events into structured artifacts.

“Anything can be an automation”—but the real bet is governance

Cursor’s launch leans on practitioner enthusiasm. A Decagon engineer highlights flexibility:

I love that automations work for both quick wins and more complex workflows… I still have full flexibility to catch any webhook or plug into custom MCPs when I need to.

And Rippling’s Tim Fall frames the value as focus and offloading:

Automations have made the repetitive aspects of my work easy to offload… Anything can be an automation!

But the deeper product bet is visible in Cursor’s internal examples: audit trails, Slack summaries, Notion logging, risk classification, guardrails, and “verify its own output.” This isn’t just “agents run code”; it’s “agents run processes,” which raises governance questions: What can the agent merge? What can it access? How do you review its actions? Who owns failures?

Cursor’s “agentic codeowners” example is telling precisely because it emphasizes risk scoring and auditability—not just speed.

Toward a “factory that creates your software”

Cursor’s headline metaphor is explicit: automations are “powered by cloud agents that use their own computers to build, test, and demo their work,” and teams can now “build the factory that creates your software” by configuring agents to continuously monitor and improve a codebase.

A Runlayer co-founder, quoted by Cursor, sums up the aspiration as leverage—small teams moving like large ones:

We move faster than teams five times our size because our agents have the right tools, the right context, and the right guardrails.

In the near term, the most credible impact is in the unglamorous corners of engineering: security checks that don’t block PRs, tests written after merges, incident response that starts before humans join the channel, and triage that turns chaos into queues. If Cursor Automations works as described, the “agent era” story shifts from better autocomplete to a more radical claim: software teams can operationalize AI as an always-on layer of labor embedded directly into the engineering pipeline.

Image: Cursor Blog

According to Reuters, Mr. Altman told attendees that the rapid expansion of advanced A.I. systems will require “staggering amounts of energy,” adding that breakthroughs in clean and abundant power are essential to sustain progress.

Compute is the limiting factor for A.I.,

he said, noting that the industry must prepare for energy demand on a scale few policymakers currently anticipate.

Mr. Altman, who leads OpenAI, has increasingly linked the development of frontier models to physical infrastructure. At Davos, he argued that while model capabilities continue to advance, the ability to deploy them widely will depend on reliable, scalable electricity generation.

Why It Matters Now

Mr. Altman’s remarks come amid a global race to secure semiconductor supply chains and build hyperscale data centers. Utilities in parts of the United States and Europe have reported record requests for new grid connections from technology companies constructing A.I.-optimized facilities. In some regions, regulators have raised concerns about transmission bottlenecks and grid stability.

The challenge is not only total generation but reliability. A.I. training clusters require uninterrupted, high-density power. While renewable sources such as wind and solar are expanding rapidly, their variability creates integration challenges unless paired with storage or firm generation.

At Davos, Mr. Altman suggested that long-term solutions could include advanced nuclear technologies and other forms of high-output clean energy. He has previously invested in energy ventures, arguing that abundant power is foundational to unlocking A.I.’s economic potential.

How Analysts Interpreted the Message

Energy and technology analysts see Mr. Altman’s comments as part caution, part strategic positioning. By highlighting energy as the next bottleneck, A.I. leaders may be signaling to governments that infrastructure policy is now inseparable from digital competitiveness.

Efficiency improvements are underway. New generations of A.I. accelerators deliver greater performance per watt, and researchers are developing smaller, more specialized models that reduce computational load. Techniques such as quantization and sparsity can lower inference costs significantly.

Yet many experts warn of a rebound effect: as computing becomes more efficient and less expensive, overall usage tends to increase. In that scenario, total energy consumption may continue rising even as individual tasks become more efficient.

Pushing Back on Water and Per-Query Claims

Days after Davos – last week, Mr. Altman addressed a related set of concerns while speaking at an event hosted by The Indian Express during his visit to India for a major A.I. summit.

There, he pushed back forcefully against viral claims about A.I.’s environmental footprint. Concerns about the water usage of systems like ChatGPT are “totally fake,” he said, acknowledging that water consumption had once been higher “when we used to do evaporative cooling in data centers.”

Now that we don’t do that, you see these things on the internet where, ‘Don’t use ChatGPT, it’s 17 gallons of water for each query’ or whatever,

Mr. Altman said.

This is completely untrue, totally insane, no connection to reality.

He also rejected comparisons suggesting that a single ChatGPT query consumes the equivalent of 1.5 iPhone battery charges.

There’s no way it’s anything close to that much,

he said when asked about the figure.

At the same time, Mr. Altman acknowledged that broader concerns about total energy use are legitimate. It is “fair” to worry about

the energy consumption — not per query, but in total, because the world is now using so much AI,

he said. In his view, that reality strengthens the case for accelerating investment in nuclear, wind and solar power.

The world needs to move towards nuclear or wind and solar very quickly,

he added.

The Data and the Debate

There is currently no global legal requirement for technology companies to disclose detailed energy and water consumption figures for specific A.I. workloads, leaving researchers to estimate impacts independently. Some academic studies have linked large data center clusters to localized increases in electricity demand and, in certain cases, higher wholesale power prices.

The International Energy Agency estimates that global data center electricity consumption could exceed 1,000 terawatt-hours annually by the end of the decade, roughly equivalent to Japan’s total electricity use today. A.I.-specific workloads are projected to account for a rising share of that total.

Mr. Altman has argued that public discussions often frame the issue unfairly, particularly when they compare the energy required to train a large A.I. model with the energy needed for a single human task.

Many discussions about ChatGPT’s energy usage are unfair,

he said in India, especially when they focus on

how much energy it takes to train an AI model, relative to how much it costs a human to do one inference query.

But it also takes a lot of energy to train a human,

he added.

It takes like 20 years of life and all of the food you eat during that time before you get smart. And not only that, it took the very widespread evolution of the 100 billion people that have ever lived and learned not to get eaten by predators and learned how to figure out science and whatever, to produce you.

In his telling, the more appropriate comparison is between a trained A.I. system answering a question and a human doing the same task.

If you ask ChatGPT a question, how much energy does it take once its model is trained to answer that question versus a human?

he said.

And probably, A.I. has already caught up on an energy efficiency basis, measured that way.

What Can Be Done

Policy experts point to several responses. Accelerating grid modernization, streamlining permitting for new transmission lines and investing in next-generation clean energy projects could help absorb the surge in demand. Co-locating data centers with dedicated renewable or nuclear facilities is another approach already being explored by major technology firms.

There is also a growing push for “energy-aware A.I.” — designing models and systems that optimize not only for accuracy and speed, but also for power efficiency.

At Davos, Mr. Altman framed the challenge as an opportunity rather than a deterrent. If artificial intelligence can dramatically increase productivity and scientific discovery, he suggested, then investing in abundant clean energy may be one of the most consequential economic decisions of the decade.

In tying A.I.’s trajectory to the electrical grid, Mr. Altman underscored a broader reality: the digital revolution now rests squarely on physical infrastructure. And the next frontier in artificial intelligence may depend less on algorithms than on kilowatts.

Material by Iva Abadjievа

Image: The Indian Express Youtube Channel, “Sam Altman Unfiltered: ChatGPT, AI Risks & What’s Coming Next, 40 Questions in 60 Minutes”

Image: Sam Altman at World Economic Forum from Benedikt von Loebell at Flickr 

The move adds another founder-turned-investor to one of Silicon Valley’s most influential venture capital partnerships. In the statement, signed by partners Ev, Chetan, Eric, and Peter, Benchmark emphasized its long-standing philosophy of operating as a true partnership rather than a collection of individual investment franchises.[/vc_column_text]

The Benchmark partnership is built on a shared commitment to the craft of venture capital, where our work is defined by the depth of service and commitment to the founders we work with,

the firm wrote.

We believe this work does not scale and is best practiced where we win as a team of partners.

From Lattice Founder to VC Partner

Benchmark first encountered Altman more than a decade ago when he founded Lattice, a people management software platform that grew into a category leader. The firm noted his leadership during the market turbulence of 2020 and praised his emphasis on transparency and team-building.

We admired Jack’s character and the way he prioritized transparency and authenticity to build a great team,

the partners wrote.

Altman later founded venture capital firm Alt Cap, where he focused on early-stage investments and built a reputation for close founder relationships.

Backing the Next Generation of Founders

As an investor, Altman has backed companies including Rippling, Owner, Avoca, Rogo, and Legora, among others.

Benchmark said founders consistently describe Altman as a hands-on partner.

Founders told us ‘I call Jack first to work through the toughest problems,’ ‘He is my most trusted partner on the board,’ and ‘Jack provides steady and grounded support that is rooted in having been a founder himself,’

the letter stated.

The firm highlighted Altman’s

relentless energy, deep intellectual curiosity, and a competitiveness to see founders win,

along with what it described as high integrity.

Reinforcing the Equal Partnership Model

Benchmark reiterated its distinctive operating model, where general partners share equal authority and responsibility.

We have always believed that our firm’s strength lies in its equal partnership: a small, focused group of individuals who operate with the same authority, responsibility, and singular mission to support entrepreneurs from the earliest stages,

the partners wrote.

By bringing Altman into the partnership, Benchmark said it aims to add a fresh perspective while maintaining its long-standing philosophy of founder-first venture investing.

Who Is Jack Altman?

Jack Altman is a Silicon Valley entrepreneur and venture capitalist best known as the founder and former CEO of Lattice, a people management and HR software platform he launched in 2015.

Under his leadership, Lattice grew into a leading workplace performance and engagement platform, serving thousands of companies globally. Altman gained recognition for steering the company through rapid growth and the operational challenges of 2020.

After stepping down as CEO, he founded Alt Cap, an early-stage venture firm where he backed startups such as Rippling and other high-growth technology companies.

In 2026, he joined Benchmark as a General Partner, becoming part of one of Silicon Valley’s most prominent venture capital partnerships.

Jack Altman is also the younger brother of Sam Altman, CEO of OpenAI.

Material by Irina Kalaydjieva

Image: Lattice, Interviews, video “Championing Low-Ego Leadership at Figma”