SaaS borç tuzağı piyasa değerlemelerini sıkıştırıyor: yapay zekâ yükselişiyle oyun değişiyor

Kısaca

SaaS endeksinin 2025 yılında yüzde 6,5 düştüğü not edildi; borçla büyüme eleştiri konusu oldu. Kısa vadede yapay zekâ odaklı stratejiler, eski borç odaklı büyümeyi geride bırakma yönünde işaret veriyor. Gözlemciler, borç tuzağının yaklaşan kırılmayı tetikleyebileceğini ve izlenecek stratejilerin önemini vurguluyor.

Ana mesele

SaaS borç tuzağı, borçlanmanın aşırı artmasıyla değerlemelerin baskı altında kalmasına yol açıyor.

Ne değişti?

Değerlendirme değişti: Yapay zekâ odaklı stratejiler geçmişteki borç bazlı vizyonları yeniden değerlendiriyor.

Beni nasıl etkiler?

Okuyucular için finansal yönetimde borç yapısını dikkatle izleme gerekliliğini vurgular.

Ne oldu?

Yapay zekâ odaklı büyüme söylemi, SaaS firmalarının borç üzerinden değerlemeyi sürdürme çabasını gündeme taşıdı.

Neden şimdi?

Piyasaların mevcut gereksinimleri ve borç maliyetlerindeki değişim, bu yapının kırılganlığını büyüttü.

Neden önemli?

Borç odaklı stratejilerin bozulması, değerlemelerin hızlı gerilemesine yol açabilir ve finansal istikrarı etkiler.

Kimler etkileniyor?

• Yatırımcılar
• şirket yönetimleri
• kamu politikaları
• kredi veren finansal kurumlar

Sektör ve piyasa etkisi

Teknoloji sektöründeki SaaS firmaları üzerinde baskı artıyor; değerlemeler ve borçlanma davranışları düzeltiliyor.

Riskler

• Borçlanmanın aşırı büyümesi
• değerleme baskılarının derinleşmesi
• kredibilite kaybı

Takip edilmesi gerekenler

• Borçlanma maliyetindeki değişimler
• hisse geri alımı trendinin finansal sağlığa etkisi
• AI stratejileriyle uyumlu gerçek verimlilik ölçütleri
• yatırımcı tepkileri ve regülasyonlar

Haberin tamamı

Last week the debate was whether the AI industry was about to slow down. Anthropic’s Dario Amodei called for the industry to pace itself for safety reasons, semis sold off, the Journal ran a piece on whether an AI slowdown would break the market, and everyone with a microphone weighed in on the data-center bubble. Steve Rosenbush at the WSJ CIO Journal quoted me on the frontier-lag question that same week: “Most of them are not using the end of the frontier. A version from two years ago would be perfectly fine. There are audiences that barely can prompt. So a delay wouldn’t make a big difference.” I made the broader case in Bubble Talk Is How You Spot Someone Who Missed AI that the US buildout is a business, not a bubble. That argument was correct then and correct now. It was also the wrong debate.

The bubble nobody is looking at has more legs than the one everyone is arguing about. It is the SaaS debt trap. When their multiples collapsed, the SaaS incumbents took on record debt, bought back their own stock, and dressed the whole thing up as an AI strategy. Salesforce ran the most extreme version. HubSpot , Workday, ServiceNow , and Adobe ran variations. The bounce worked once. The endpoint is a debt-driven death loop that ends in a Bending Spoons offer letter. I made a prediction of a bounce in valuations in June, when I wrote The Last Great Head Fake in Software History , but this is not what I was expecting.

Had you asked me in April, I could not have imagined this was the playbook every leader in the category would run. In April I wrote Software Is Over . Intelligence is the new core substrate. SaaS is the legacy one. Five months later, watching what the incumbents actually did in response, I think I might have been too soft. This is my update.

If speed is the cornerstone of AI-first, look at what five months just did to the legacy SaaS category and apply that same speed to your own thinking.

The data-center bubble was the wrong bubble to watch. The SaaS debt trap is the one with real legs, and it is closer to snapping than the market has priced.

The market was already pricing my April thesis in when I wrote the anchor. The SaaS index fell 6.5% in 2025 while the S&P 500 rose 17.6%. Median SaaS revenue multiples went from 18x in 2021 to about 3x. IBM dropped 13.2% on February 23, its worst single day in more than 25 years, after Anthropic showed Claude Code modernizing COBOL. Jasper went from $120 million to $55 million in revenue in one year as soon as the model layer improved.

Every one of those data points has firmed up. Anthropic in particular. The $30 billion ARR mark I cited in April was an end-of-Q1 pace. Bloomberg reported in August that the annualized run rate crossed $65 billion at the end of July, up sevenfold from the $9 billion it exited 2025 on. Yesterday Bloomberg cited the New York Times reporting Anthropic is on track to top $100 billion in annualized revenue this year and could list as soon as November. Q2 2026 revenue alone was $11.5 billion, up 14x year over year. That is just Anthropic.

Sit with that. Anthropic added a Snowflake plus a Palantir to its run rate every quarter this year. Every dollar of that spend is a dollar of intelligence sitting under whatever screen a SaaS incumbent is still trying to charge for. Cheaper tokens compound the pressure because they make the AI-native replacement cheaper to build, every model improvement makes it easier to build. I walked the pricing dynamics in Peak Token .

Put a market-cap frame on the same shape. Anthropic alone, at $965 billion (and expected to trade at IPO for double that), is worth more than Salesforce, Adobe, ServiceNow, Workday, and HubSpot combined. Those five names sit at roughly $544 billion of public equity as of September 18. Add OpenAI at $852 billion. The two leading language-model labs are approaching two trillion dollars in private equity value, comparable to a multiple of the entire pure-SaaS public category.

To say model companies are eating the world is an understatement. Think about it this way, that value is only the language-model slice of the AI puzzle. There are other core brains coming right behind them. Fei-Fei Li’s World Labs raised $1.23 billion for spatial world models. Isomorphic Labs is training protein folding out of DeepMind. Microsoft’s MatterGen is training materials science. Physical Intelligence and Skild AI are training robotics foundation models. Runway is training video. Suno is training music. ElevenLabs is training voice. Black Forest Labs is training image. DeepSeek is shipping open-weight frontier releases. xAI is training Grok. Perplexity is training answer models. Poolside is training code. At Collective[i] we trained the Economic Model. Each are critical brains and each are taking over.

Every one of these is a market a language model cannot address on its own. Every one has serious capital training against it right now. Some will end up bigger than what the LLMs unlocked. The substrate is competing on the balance sheet, and it is already winning.

The substrate is compounding faster than the incumbents can rebrand around it. This is the single most important number in the SaaSpocalypse thesis, and it has only gotten stronger.

Salesforce’s own stock proved the thesis, then reversed it, then proved it again in real time. It dropped 30% through June to a $147 low. By early September it had ripped to $263. On September 18 it closed at $237.92. The round trip took two weeks. The thing that changed was not the product.

Haberin tamamı için kaynak bağlantısını ziyaret edin.

Kaynaklar

• Fortune