Michael Saylor says he turned to ChatGPT when he needed to design a fresh way to fund his company’s aggressive Bitcoin strategy – and that the AI-assisted model ultimately helped raise about $15 billion.
According to Saylor, the AI chatbot played a central role in brainstorming and refining a preferred stock financing structure that Strategy later deployed across several offerings. Those offerings allowed the company to tap capital markets while directly linking investor exposure to its Bitcoin-heavy balance sheet.
In an interview, Saylor described using ChatGPT as a kind of high-speed research and ideation partner. He said the system helped him analyze different capital-raising frameworks, test assumptions, and shape the architecture of the preferred stock products that Strategy later brought to market. From his perspective, AI did not replace financial advisors or lawyers, but it significantly accelerated his own thinking.
“AI helped me create $15 billion,” Saylor said, emphasizing that the figure reflects the amount of capital raised through the preferred stock program and other related financing, not revenue generated by the technology itself. The actual execution – underwriting, investor marketing, legal structuring, regulatory filings – was carried out by human professionals at the company and its financial partners.
Bitcoin-tied preferred shares
Strategy has assembled a suite of Bitcoin-backed preferred securities, including tickers STRC, STRK, STRF and STRD. Each instrument is designed with its own blend of yield, risk, and sensitivity to both the company’s balance sheet and Bitcoin’s price cycles. In practice, this gives investors a menu of options: some products emphasize higher dividends, others prioritize lower volatility or more direct exposure to the firm’s Bitcoin holdings.
These securities sit at the intersection of corporate finance and digital assets. On one side, they resemble traditional preferred stock with set dividend obligations and seniority over common equity. On the other, their underlying economics are heavily influenced by Strategy’s decision to hold an unusually large Bitcoin position as part of its treasury strategy. That makes their performance closely tied to how well the company can manage leverage, crypto price swings, and capital allocation.
Saylor’s use of AI in designing these instruments offers a concrete example of how generative models are starting to shape U.S. corporate finance. It is not about AI autonomously issuing securities; every structure still has to withstand legal, accounting, and regulatory scrutiny. But AI can compress weeks of conceptual work into hours, surfacing historical analogues, pointing to edge cases, and challenging assumptions that might otherwise go untested.
“Don’t try to outwork the robots”
For Saylor, the lesson goes beyond one financing deal. He sees AI as a new layer of leverage for knowledge workers – something that amplifies human creativity and judgment rather than simply automating repetitive tasks. In his view, the most valuable employees in the coming decade will be those who learn to ask sharper questions and orchestrate AI tools effectively.
“Don’t try to outwork the robots,” he warned. Instead, he argues, people should learn to “harness the robots” – using systems like ChatGPT to explore complex ideas, model scenarios, and pressure-test strategies that once required teams of specialists or months of manual research. The advantage, he says, goes to those who can combine human context, ethical judgment, and strategic vision with the speed and breadth of AI.
This framing positions AI as a complement, not a competitor, to human decision-making. Rather than viewing automation as a threat to white-collar work, Saylor suggests that entrepreneurs and executives who integrate AI into their workflows will be able to move faster, explore more financing options, and react more dynamically to changing market conditions.
A more flexible Bitcoin treasury policy
Saylor’s comments on AI arrived as Strategy showed a willingness to actively manage, rather than simply hoard, its massive Bitcoin stash. Recent disclosures revealed that the company sold 1,638 BTC between July 27 and August 2 for approximately $104.73 million. Roughly half of those proceeds – $52.4 million – went toward paying dividends on its preferred stock, with the remaining $52.3 million used to repurchase STRC shares on the market.
These moves underline how tightly the Bitcoin portfolio is integrated into the company’s overall capital strategy. Rather than treating Bitcoin as an untouchable reserve, Strategy appears prepared to rotate a portion of its holdings when it can bolster shareholder value, meet fixed obligations, or support its preferred equity instruments.
After the sale, Strategy reported holding 842,138 BTC as of August 2. The company said its aggregate purchase cost for this position was about $63.51 billion, implying an average acquisition price of $75,419 per Bitcoin. This scale makes Strategy one of the largest corporate holders of Bitcoin in the world and explains why its financing structures, stock price, and investor base are so tightly linked to crypto market cycles.
On August 5, blockchain data tracked another movement of 1,030 BTC – worth roughly $66.14 million at the time – from wallets associated with Strategy. The company did not immediately confirm whether this represented a sale, a treasury transfer, or internal wallet management. At the time the details were compiled, there had been no subsequent regulatory filing updating the firm’s reported on-balance-sheet Bitcoin holdings.
Beyond Bitcoin: benefits and broader strategy
Parallel to its financing innovations, Strategy has begun expanding its corporate profile with more conventional employee-focused initiatives. Recently, the company signed on to the Invest America Business Pledge and committed to annually contributing $250 to so‑called Trump Accounts for eligible children of its U.S. employees.
Under this program, children born on or after January 1, 2025, will also receive a one-time $1,000 contribution from the company, matching the initial deposit from the federal government. While the sums are modest in the context of Strategy’s multi-billion-dollar balance sheet, the initiative signals that the company wants to be known not only for aggressive Bitcoin accumulation and inventive capital structures, but also for long-term employee and family benefits.
This combination – high-risk, crypto-linked treasury management on one side and more traditional benefits on the other – illustrates the dual identity Strategy is trying to cultivate: a financial innovator that still checks familiar corporate boxes around workforce support and social commitments.
AI’s growing role in balance-sheet engineering
The way Saylor describes his use of ChatGPT suggests a broader shift in how CFOs, treasurers, and founders might approach balance-sheet design. Historically, complex financings required lengthy cycles of consultations with bankers, lawyers, and internal teams before executives could even settle on a few viable options. Today, AI tools can help leaders generate and test dozens of structures conceptually before ever calling a bank.
For instance, an AI system can be asked to compare the implications of convertible debt versus preferred equity, outline historical precedents for equity-linked notes tied to commodity prices, or flag typical covenant packages and risk factors seen in past transactions. Much of this can be done in natural language and iterated rapidly, allowing executives to arrive at meetings with advisors already armed with specific, refined ideas.
In Strategy’s case, this appears to have accelerated the development of a family of Bitcoin-linked preferred shares that were unusual enough to require creativity, but still had to be standardized enough to fit within regulatory frameworks. AI did not write the legal documents or negotiate the underwriting terms, yet it helped Saylor navigate the conceptual landscape of what was possible.
Risk, regulation, and the limits of AI in finance
Even as Saylor champions AI as a value-creation engine, the Strategy story highlights the boundaries of what these tools can safely do in a regulated environment. Any financing scheme recommended or shaped by a model like ChatGPT must still be vetted by securities lawyers, accountants, compliance teams, and, in the case of public offerings, market regulators.
There are also real risks: AI can hallucinate precedent, misinterpret niche regulations, or overlook edge cases that matter greatly in securities law. That is why, for now, generative models function best as brainstorming and research accelerators – not as autonomous architects of financial products.
Furthermore, tying preferred stock performance to a volatile asset like Bitcoin amplifies both upside and downside for investors. While AI may help stress‑test scenarios, the ultimate risk is borne by investors who accept exposure to Bitcoin price swings as part of their expected return profile. Strategy’s ongoing challenge is to maintain enough liquidity and balance-sheet strength to honor dividends, manage buybacks, and ride out crypto bear markets without putting the company itself under strain.
A template for future corporate crypto strategies?
Strategy’s approach offers a possible template for other firms that want to hold digital assets at scale without relying solely on traditional debt. By using preferred shares with different risk-return profiles, a company can segment its investor base: some may seek high fixed dividends regardless of Bitcoin’s path, while others might prefer more direct crypto exposure in exchange for potentially higher upside.
If this model proves durable – meaning Strategy can continue attracting capital, paying dividends, and preserving or growing its Bitcoin stack – more corporations could be encouraged to consider similar structures. They may not all copy the exact design, but they could use AI tools to explore their own bespoke combinations of equity, debt, tokenized instruments, and derivatives.
This is where AI’s role becomes especially potent: it can help executives simulate how a portfolio of financing instruments might behave under different interest-rate regimes, regulatory changes, and market shocks, long before any securities are actually issued.
What it means for workers and executives
Saylor’s insistence that workers learn to “harness the robots” speaks to a broader shift in professional expectations. In finance, strategy, and corporate development, the baseline skill set is likely to expand to include fluent use of AI for modeling, drafting, and scenario analysis. Employees who can turn ambiguous business goals into targeted AI prompts, then filter the output through their own expertise, will have a structural advantage.
For executives, this means leadership is no longer only about choosing among options presented by advisors; it is also about co-creating those options with AI, then refining them with human teams. The Strategy case shows how a single leader, working closely with an AI tool, can drive the conception of a large-scale financing architecture that reshapes the company’s capital structure.
The next test for Strategy’s AI-shaped model
Looking ahead, the real measure of success for Strategy’s preferred stock program will not be the novelty of using ChatGPT, but the model’s resilience over time. Can these securities consistently attract new and repeat investors? Will the company be able to meet its dividend obligations through both bull and bear markets in Bitcoin? And can it maintain or even expand its Bitcoin holdings without overextending its balance sheet?
Saylor’s description of AI’s role underscores how technology can influence the design phase of complex financial structures. However, long-term performance will be determined by more traditional forces: investor appetite, capital-market conditions, regulatory developments, Bitcoin’s price trajectory, and the company’s operational execution.
If Strategy can navigate those pressures successfully, its experience may mark an early, high-profile example of how generative AI and corporate crypto strategies can intersect – not as a futuristic abstraction, but as a tangible driver of multi‑billion‑dollar capital formation.