Space technology investment hit record levels in 2025 and is expected to rise further in 2026, driven by defense spending and launch services.
The post Space Sector Investment Set to Grow in 2026 After Record Year appeared first on TechRepublic.
Space technology investment hit record levels in 2025 and is expected to rise further in 2026, driven by defense spending and launch services.
The post Space Sector Investment Set to Grow in 2026 After Record Year appeared first on TechRepublic.
Vega Cloud, a Spokane-area tech startup that makes software to help companies manage their cloud spending, has been placed into the hands of a receiver after declaring it could no longer pay its debts.
Among those debts: nearly $830,000 owed to cloud giant Amazon Web Services.
Vega Cloud, founded in 2018 and based in Liberty Lake, Wash., had raised $12.2 million and reached about $7 million in annual revenue as of 2023, according to PitchBook data. It had also cracked the GeekWire 200 — ranking #181 in the most recent quarterly update of our Pacific Northwest startup index.
What brought Vega Cloud to this point isn’t clear. Responding to our email inquiry this weekend, co-founder and CEO Kris Bliesner said the company is going through a restructuring via receivership, and said he wished he could say more about the situation.
The company had less than $17,000 in the bank when it was placed into receivership Thursday, Jan. 15, in King County Superior Court in Seattle, the filing shows. It employed about 35 people as of earlier this month, down from about 65 two years ago, according to LinkedIn.
Receivership is a state-level process often used as an alternative to bankruptcy. In this case, Vega Cloud executed what’s known as an Assignment for the Benefit of Creditors, which puts a neutral party in charge of the company, pauses creditor collections, and places decisions about asset sales and payments under court supervision.
Sometimes those assets sell mostly intact, allowing new investors to give a business another try. But at this point, it’s not yet clear what will happen to the company’s employees or product.
In a March 2024 interview for GeekWire’s special series on Spokane, Bliesner described Vega Cloud’s trajectory in optimistic terms, saying the company was planning a $20 million to $30 million funding round and eyeing the public markets.
“We’re trying to push the envelope at Vega to maybe do the IPO route,” Bliesner said at the time. “We think that’s a viable thing for us.”
Vega Cloud operates in the sector known as FinOps, short for financial operations, helping companies get a handle on their cloud spending by bringing together finance and technical teams to track costs and avoid waste.
This is becoming more and more important as businesses pour money into cloud computing, often without realizing how much they’re spending on unused resources. Vega Cloud focused specifically on helping mid-sized companies manage spending across AWS, Azure, and Google Cloud, using automated tools to spot problems and recommend fixes.
In the tight-knit Spokane tech community, Vega Cloud has been seen as a startup with the potential to make it big. We took note of the company in 2022, when it raised $9 million.
Investor and entrepreneur Martin Tobias, a longtime fixture in Pacific Northwest enterprise tech, invested in Vega Cloud shortly after moving from Seattle to Spokane during the pandemic. He told us in early 2024 that it would probably be one of his most successful investments.
Tobias said Bliesner was exactly the kind of founder he looks for: someone with deep experience in a market who had tried to solve something one way, realized it wasn’t going to scale, and came up with a better solution.
“He took a new approach to an old problem,” Tobias said at the time.
Bliesner previously co-founded cloud migration startup 2nd Watch, which raised about $56 million before selling a majority interest to Singapore-based investor ST Telemedia.
Vega Cloud’s court filings give an inside look at the privately held business.
First, the company had real customers and revenue. The filings list contracts with companies including Paramount, Hearst, Deloitte, Molina Healthcare, John Wiley & Sons, and Cal Poly, among others. It lists roughly $264,000 in accounts receivable.
The largest secured creditor is Sun Mountain Private Credit Fund I, owed $3.5 million. That debt is backed by Vega Cloud’s intellectual property — its software, patents, trademarks, and domain names. Any proceeds from a sale of those assets would go first to that lender.
In addition to the roughly $830,000 owed to AWS, the court records show convertible promissory notes totaling about $2.5 million that were issued to investors throughout 2025.
The records list current and former employees who are owed unpaid commissions, bonuses, and expense reimbursements, with some bonus obligations dating back to 2023. The company also owes payroll and withholding taxes to the IRS and multiple state tax agencies.
Bliesner is the largest shareholder at about 30%. Other significant investors include Album Ventures (10%), Cowles Company (3%), Rudeen & Company (3%), Kick-Start III and IV (combined 4%), Tacoma Venture Fund (1.5%), and Pitbull Ventures (1%).
The shareholder list also includes Voyager Capital, Alliance of Angels, Incisive Ventures, and Morning Star Foundation, along with dozens of individual investors.
Under court supervision, the receiver can now take possession of Vega Cloud’s assets and records, secure its bank accounts and data, evaluate and sell assets such as intellectual property, collect remaining receivables, and distribute proceeds to creditors in priority order.
The filings do not include a timeline for asset sales or any plan for the business to continue operating. Those details typically emerge later through receiver reports.
AI has attracted unprecedented levels of capital and attention. And questions are growing about the so-called AI bubble: Are too many startups chasing the same ideas? Are valuations running ahead of real adoption? And will all this investment pay off — or pop?
GeekWire polled a handful of Seattle-area venture capitalists about whether they think an AI bubble exists, and how startups should prepare as they plan for 2026.
Taken together, the investors paint a picture of a market that is overheated in places, but far from broken. They see clear signs of excess in AI — especially in early-stage private companies where valuations often outpace real traction. But they largely reject the idea of a catastrophic bubble, and most argue that the technology itself is already delivering real value.
They differ on the details: Some see the biggest excess in data center buildouts. Others point to narrative-driven startups raising at huge valuations without real customer traction. One investor puts AI’s full impact 10 to 20 years out. Another sees immediate opportunity as companies rethink their software spending, making longtime vendors vulnerable.
Their advice to startup founders: ignore the hype, focus on real customer problems, build durable revenue and efficient businesses, and be ready for some market cooling.
Read their full responses below.
“There’s clear froth in parts of the AI market, especially in early-stage private valuations where companies are priced well ahead of fundamentals, which fits a classic ‘bubble’ definition. In the public markets, the strongest AI companies are backing valuations with outsized earnings and growth, so it doesn’t look like a traditional bubble there.
The most pronounced exuberance is in the private markets, particularly at seed and Series A, where many investors are trying to get in earlier on AI exposure. As a result, capital is chasing startups with limited traction and valuations that price in outcomes that may take years of execution to justify.
Startups should focus on durable business fundamentals early on. Build repeatable revenue through annual or multi-year contracts, solve real customer problems, and differentiate by integrating deeply into the customer tech stack to create real product and company flywheels. Long-term success comes from delivering measurable value and defensible growth over time.”
“Many factors are at play here. You have a new and genuinely transformative technology in AI. Over the long term, it will radically reshape how nearly every industry operates. At the same time, history tells us that new technologies tend to be overestimated in the short term and underestimated in the long term. The most profound, fully realized impacts of AI may still be 10-to-20 years away.
In the near term (the next few years), I expect some pullback in the public markets as investors come to terms with the fact that true ‘enterprise readiness’ for AI will take time. This doesn’t suggest anything catastrophic — just that the roughly 21 percent year-over-year growth we’ve seen in the Nasdaq is unlikely to be sustainable and may revert closer to the 30-year average of around 10 percent. After a few meaningful pullbacks, pundits will inevitably claim that AI is overhyped. In reality, this would simply represent a normalization after an extraordinary, AI-fueled run in the public markets.
Late-stage private markets will see some overly hyped companies — this happens in every boom cycle. The winners will be bigger than ever, but the losses will also be bigger than ever. When you have companies like Anthropic growing from $1 billion to a projected $9 billion of revenue in 2025, it’s clear that AI is already delivering real, material impact in the world.
For startups, there’s no better time to be building than now. M&A markets are back, customers have budget, and talent wants to work on interesting projects. With that said, there is a lot of noise, so it’s best to go deep and really focus on a core customer problem. Most of the growth we’ve seen to date is in the infrastructure layer — the next few years will be about the next generation of AI-powered applications.”
“Yes, a significant amount of capital being deployed globally in AI (and particularly in the data center buildout) is almost certainly being misallocated. Specifically in startups, outside a few presumed winners (OpenAI, Anthropic, Cursor), the concern is less overcapitalization and more the prices at which financings are being done relative to the actual cash flows and margin potential of the companies being financed.
That said, unlike some recent bubbles I can think of (crypto, metaverse, etc.) there are actual babies in the bathwater this time. LLMs are remarkably capable tools even at their current state of development, and will remain core to many software development and knowledge work tasks long after rationality has returned to the financial landscape.
The founder and investor challenge in moments like the current one is how to make decisions that will look smart ten years from now, not just in the current moment. Are there ways to apply LLMs to create durable business value in segments of the economy that are not likely to be overcapitalized or competed to zero by the near-term flood of dollars? The only alternative strategy is to try to pick winners in the capital wars and pay whatever the market demands for those assets, but history suggests that’s a very low odds proposition for even the best players.
The recipe for success in times like this is not that different from any other time: pick a customer segment that you understand better than anyone else, engage deeply with those customers to understand what problems you can uniquely solve with LLMs that were too hard or expensive to solve previously, build quickly and iteratively to show value to those customers, and maintain that pace of shipping and learning for as long as you can.
That may sound simple, but it’s remarkable how few founding teams are able to pull it off, and that why startups are so hard, and so fun.”
“Broadly, I don’t think we’re in an AI bubble right now. Similar concerns existed when we launched the Azure platform about fifteen years ago. Back then, people were initially worried about racing to a zero-margin business.
Today’s massive AI infrastructure buildouts will shape the operational software layers that drive real-world performance — compute orchestration, data pipelines, memory systems, and large-scale inference efficiency. Value is shifting toward packaging and deploying intelligence across enterprise workflows.
Enterprise software startups should position themselves in the growing TAM of delivering full, end-to-end solutions and new ways of doing things where humans collaborate with AI agents. Winning startups will encompass both the growing IT TAM and economics of a portion of the labor market as well.
We are now seeing unprecedented malleability of CIO budgets. The deeply entrenched application stack can now shift to new players which are built with AI from the ground up. The market opportunity is massive, and companies should set their sights on building the new megacaps, not minor feature companies.”
“Yes, we are in an AI bubble, but not in the way most people think.
Capital and valuations are running well ahead of fundamentals, particularly for companies without clear customer pull, durable differentiation, or credible/reasonable paths to profitability. We’re seeing a growing gap between narrative-driven AI companies where ‘AI’ is largely a positioning exercise, and value-driven AI companies that use the technology to deliver measurable, repeatable value for customers.
The bubble seems most pronounced at the early and growth stages where AI storytelling can temporarily substitute for traction and raise capital at lofty valuations. Some strong companies will emerge from this cycle, but there will be meaningful drawdowns, recaps, or shutdowns as many startups fail to grow into those expectations.
Looking ahead to 2026, my advice to founders is straightforward:
2026 is going to be an incredible moment to build. The cost of experimentation and building products has collapsed, and founders no longer need educational credentials (CS degrees or an MBA) to create real products and revenue. The next generation of durable AI companies will be built by small teams who focus less on hype and more on efficient execution. We’re definitely excited to see more teams building incredible products this upcoming year.”
“From my perspective, what we’re seeing is less an AI bubble and more a classic venture cycle playing out around a genuinely transformative platform shift. Venture has always adapted to new normals alongside major technology inflections (cloud, mobile, social), and AI is the fastest-moving one we’ve seen to date.
The difference this time is speed, scale, and capital availability. AI adoption is happening at a faster clip and at a much larger scale than prior platform shifts, all while private-market capital has reached historic highs. As those forces collide, pricing, timelines, and investor behavior evolve.
Capital moving ahead of fundamentals is not new. There will be some shakeouts, but that doesn’t mean underlying value creation isn’t happening. Companies with real technology, real distribution, and real customers will endure.”
The past year may go down as one of the most consequential in technology history, in both the Seattle tech community and the world. But in some ways, it’s not without precedent.
As we sat down to reflect on the past year, we rewound all the way back to January — when, as part of a larger discussion with Bill Gates, we asked the Microsoft co-founder to compare the early days of the PC with these early years of AI.
Gates reflected on the PC era as a moment of computing becoming free, effectively.
“Now what’s happening is intelligence is becoming free,” he said, “and that’s even more profound than computing becoming free.”
As we looked through GeekWire’s top stories of the year, almost every one felt like a subplot to that larger narrative. On this special year-end episode of the GeekWire Podcast, we reviewed the articles that resonated most with readers, and compared notes to make sense of it all.
Listen below, and continue reading for episode notes and links.
“We look forward to joining Matt on his private island next year.” — Kiana Ehsani, CEO of Vercept, after her co-founder Matt Deitke left to join Meta for a reported hundreds of millions of dollars.
Proud Seattleite and grammarian Ken Jennings on Jeopardy!, correcting a contestant: “Sorry, Dan, we are sticklers in Seattle. It’s Pike Place — no s.”
Ambika Singh, CEO and founder of Armoire, accepting the Workplace of the Year award at the GeekWire Awards: “It is not a surprise to any of you that we are losing community outside of these walls in this country. But here, it feels alive and well.”
Subscribe to GeekWire in Apple Podcasts, Spotify, or wherever you listen.
Audio editing by Curt Milton
OPINION -- I recently had a conversation with senior intelligence community leaders about their desire to build stronger partnerships with private-sector technology companies—the so-called “Silicon Valley” ecosystem. They were asking for advice on how to engage, build relationships, and ultimately establish strategic partnerships.
But the companies they were most interested in? They were largely consumer-facing platforms. Innovative, yes—but not mission-aligned. That conversation highlighted a broader, more fundamental gap I’ve been thinking about for a long time: Why are there no U.S. offensive cyber unicorns?
We certainly have defense contractors who do cyber work—on site, on contract, embedded with the government. And we have standout cybersecurity companies like CrowdStrike, Mandiant, and Dragos focused on detection, response, and resilience. But where are the startups building offensive cyber tools and platforms? Where’s the VC-backed innovation model we’ve seen in drones, hypersonics, and space?
Save your virtual seat now for The Cyber Initiatives Group Winter Summit on December 10 from 12p – 3p ET for more conversations on cyber, AI and the future of national security.
Companies like Anduril and SpaceX have proven that Silicon Valley-style innovation—product-focused, capital-efficient, fast-moving—can thrive in the national security space. So why hasn’t that approach been applied to offensive cyber? Yes, there are legal and secrecy constraints. But those same constraints haven’t stopped commercial companies from building weapons systems or highly classified ISR platforms.
Take a look at the NatSec100 - a curated list of top defense and national security startups. You’ll find companies working on AI, autonomy, sensing, and cybersecurity. But not a single one focused on offensive cyber. Why not?
Shouldn’t we want the best minds at CrowdStrike or Mandiant to spin off and build next-generation offensive platforms? Shouldn’t the DOD and IC be seeding these ideas and building an ecosystem that encourages this kind of innovation?
I believe we should.
Follow Bryan on LinkedIn or right here at The Cipher Brief.
The Cipher Brief is committed to publishing a range of perspectives on national security issues submitted by deeply experienced national security professionals.
Opinions expressed are those of the author and do not represent the views or opinions of The Cipher Brief.
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Read more expert-driven national security insights, perspective and analysis in The Cipher Brief
The Cipher Brief's Special Report on Nat Sec EDGE 2025
The Nat Sec EDGE 2025 conference took place June 5–6, 2025 in Austin, Texas.
Foreword
The 2025 Nat Sec EDGE Conference brought together a diverse coalition of leaders from government, industry, investment, and innovation to confront a shared reality: America’s national security advantage is eroding-and our ability to adapt at speed will determine the outcome of future conflicts.
Across two days of discussions, senior officials, technologists, operators, and investors delivered a clear message: the U.S. is engaged in an unprecedented strategic competition with near-peer adversaries who are moving faster, with fewer constraints, in an effort to achieve dominance in emerging domains. While the U.S. still holds an innovation edge, our traditional systems for acquisition, classification, and risk management are too slow, too fragmented, and too siloed to respond to the velocity of today’s threats.
What emerged from this gathering in Austin, TX was not just urgency-but clarity. The U.S. needs a new model for national security innovation-one built around speed, trust, integration, and mission-first execution. This means enabling “new primes” that can move at the pace of technology, equipping the defense industrial base with secure pathways to scale, and empowering operators and decision-makers with the tools to bridge policy, procurement, and operational need.
It also means recognizing that the problem is no longer technological- it’s sociological. The innovation exists. The capital exists. The threat is clear. What’s missing are the connective tissues: the incentives, partnerships, and trust frameworks that can accelerate solutions from concept to deployment.
This report captures the most critical messages and moments from Nat Sec EDGE. It is intended as both a record and a roadmap-for those shaping the future of American security.
Suzanne Kelly, Brad Christian, Ethan Masucol and Connor Curfman contributed to this report.
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