BigBear.ai Holdings, Inc. (BBAI): what the price requires
At today's price, BigBear.ai Holdings, Inc. (BBAI) is priced for today's economics sustained for ~22.9 years. boothcheck doesn't publish a fair value or a price target; it shows what the price assumes, so you can judge whether that bar is too high.
Generated: 2026-07-14 · Exported: 2026-07-16 · Source: https://boothcheck.com/report/BBAI
Headline
| Field | Value |
|---|---|
| Ticker | BBAI |
| Company | BigBear.ai Holdings, Inc. |
| Current price | $3.06/sh |
What The Price Requires (Inversion)
The assumption today's price embeds, recovered by inverting the valuation.
| Field | Value |
|---|---|
| Inversion basis | revenue-multiple |
| EV / sales paid | 10.4x |
| Steady-state operating margin assumed | 8.9% |
| Must persist for | 22.9y |
The company earns no operating profit yet; the inversion runs on the revenue multiple and an assumed steady-state margin.
Solve inputs: computed at a 11.8% cost of capital; growth searched up to the 25% self-funding ceiling; each 1pp moves the implied horizon ~3 years.
Reconcile: at the x-ray's 9.3% required return this reads ~16.1 years; the models below use their own rates.
How unusual the bet is: elevated
| Reference | Value |
|---|---|
| vs own history | +0.52σ |
| sustained it ~10 years at this level | 15% |
| implied end-window share | 0% |
Valuation X-Ray
Every valuation family lands below the price. The price therefore requires assumptions beyond what those standard frames encode.
How the valuation models price the stock relative to the market price. Price/FV above 1.0 means the market pays more than that lens defends (expensive); at or below 1.0 the lens can defend the price.
| Family | Median price/FV | Models | Reads |
|---|---|---|---|
| Asset | 1.93x | 2 | expensive |
| Earnings | — | 0 | — |
| Relative | 1.42x | 1 | expensive |
| Growth | — | 0 | — |
Families that call it expensive: Asset
The models below discount at their own flat-beta convention rates (cost of equity 9.3%, WACC 9.0%); the inversion above states its own rate.
Per-Model Detail (n=3)
| Model | Family | FV | Price/FV | Applicable | Methodology |
|---|---|---|---|---|---|
| DCF Perpetual Growth | Growth | $0.00 | — | no | Negative/zero FCF — equity value floored at $0 |
| DCF Exit Multiple | Growth | $0.00 | — | no | Negative/zero FCF or EBITDA — equity value floored at $0 |
| Relative Valuation | Relative | $2.15 | 1.42x | yes | P/S fallback (negative EPS): Sector P/S 8.0x × TTM revenue — excluded from consensus |
| Simple DDM | Growth | — | — | no | — |
| Two-Stage DDM | Growth | — | — | no | — |
| Simple Excess Return | Asset | $1.67 | 1.83x | yes | Reference only (book value floor): BV/sh $1.67, ROE negative |
| Two-Stage Excess Return | Asset | $1.50 | 2.04x | yes | Reference only (book value with convergence): BV/sh $1.67, ROE converges to ke |
| Discounted Future Market Cap | Growth | $0.86 | 3.55x | no | Rev $0.1B, growth -15% (input: historical growth; tapered), Terminal P/S: 6.8x / 8.0x / 9.2x (bear / base = today's held flat / bull, cap 8x) |
| Peter Lynch Fair Value | Relative | $0.00 | — | no | Negative/zero EPS — earnings-based value floored at $0 |
| Margin Trajectory | Growth | — | — | no | — |
| Earnings Power Value | Earnings | — | — | no | — |
| Residual Income | Asset | — | — | no | — |
| Graham Number | Asset | — | — | no | — |
| EV/EBITDA Relative | Relative | — | — | no | — |
| FCF Yield | Earnings | — | — | no | — |
| SBC-Adj FCF Yield | Earnings | — | — | no | — |
| Ben Graham Formula | Earnings | — | — | no | — |
| ROIC-Justified P/B | Asset | — | — | no | — |
| P/Sales Sector | Relative | $2.15 | 1.42x | no | Revenue $0.13B × sector P/S 8.0x |
| PEG Fair Value | Relative | — | — | no | — |
| Earnings Yield | Earnings | — | — | no | — |
| Funds From Operations Multiple | Relative | — | — | no | — |
| Clinical Phase NPV | Growth | — | — | no | — |
| Merton | Asset | — | — | no | — |
| V5 Mechanical | — | — | — | no | — |
Solvency
| Field | Value |
|---|---|
| Net cash | $316.2m |
| Interest coverage | -10.9x |
| Share count CAGR (dilution) | 37.6% |
| Burning cash | yes |
Operating profit is negative or near zero and the company has no demonstrated through-cycle (mid-cycle) operating margin to normalize against, so years-to-repay cannot be computed honestly.
Bullet Takeaways
- BigBear.ai sells AI software and analytics mainly to U.S. defense and intelligence agencies, and the trajectory that matters is margin and backlog: first-quarter 2026 gross margin jumped to 34.0% from 21.3% a year earlier, and backlog grew 14% sequentially to $281.9 million.
- The biggest risk is the price relative to a money-losing business: the stock trades at roughly 11 times revenue while the company posted a $56.8 million quarterly net loss and is still burning cash, so the valuation rests entirely on a future it has not yet earned.
- What moves the stock next is contract wins converting to revenue against the $135 million to $165 million full-year guidance, alongside continued dilution after the company converted $124.6 million of notes to equity.
Bull Case
The trajectory worth leading with is the margin, because it is where the business model is starting to show it can work. BigBear.ai's gross margin jumped to 34.0% in the first quarter of 2026 from 21.3% a year earlier, driven by the acquisition of Ask Sage and its higher-margin generative-AI platform. For a company that sells AI analytics to the government, the shift from low-margin services toward higher-margin software products is the single most important change, because it is the difference between a body-shop contractor and a scalable software company. The net loss narrowed too, to $56.8 million from a larger loss a year earlier, with the per-share loss cut roughly in half to $0.12 from $0.25. The losses are still large, but the direction is improving.
The backlog is the second piece of the trajectory. It grew 14% sequentially to $281.9 million in the first quarter, fueled by national-security wins including a $53 million sole-source classified contract with an intelligence-community customer. A sole-source classified award is meaningful: it means the government chose BigBear.ai without competition, which signals a capability the agency could not easily get elsewhere. The company stacked close to $75 million of wins in the quarter and affirmed its full-year revenue guidance of $135 million to $165 million.
The demand backdrop favors the company. Defense and intelligence agencies are spending to embed AI into mission systems, and BigBear.ai is positioned in exactly that lane, with a former senior government official as CEO and a roster of national-security customers. The balance sheet gives it room to keep investing: the company holds roughly $316 million of net cash after settling its convertible notes, well more than enough to fund operations while the margin mix improves. The bull case is that the gross-margin inflection and the growing classified backlog mark the point where BigBear.ai turns from a cash-burning hopeful into a real, software-led defense-AI business, and the demand environment is pulling in its direction.
Bear Case
The hard truth a holder has to face is that the price is a story, and the story has to hold for many years to be worth what the market pays today. BigBear.ai trades at roughly 11 times its revenue, and inverting that price says the business eventually has to reach an operating margin near 9% and grow revenue at its self-funding ceiling for something like two decades. The company does not earn that margin; it loses money, with a quarterly net loss of $56.8 million and negative operating income across the trailing year. The price is not paying for what BigBear.ai is; it is paying for what it might become, and of comparable fast-growers, only about 15% sustained the pace the price assumes for even ten years.
The revenue line undercuts the narrative more than the margin headline suggests. First-quarter revenue actually fell 1% year over year to $34.4 million, as lower volume on Army programs offset the contribution from the Ask Sage acquisition. A company priced for two decades of ceiling growth that just posted flat-to-down organic revenue is the tension at the heart of the bear case. Worse, the government-contracting model carries risks the price ignores: the filing warns that any award "may be subject to appeals, disputes, or litigation, including but not limited to bid protests by unsuccessful bidders" and that the business is exposed to "lapses in appropriations for the federal government." Backlog is not revenue until the contracts are funded and performed, and federal budgets can stall.
The dilution is the quiet thief. BigBear.ai has funded its losses by issuing stock and converting debt to equity: the share count has grown nearly 38% over the past year, and in January 2026 the company settled $124.6 million of convertible notes largely by converting them into shares. The filing is explicit that it "may need to raise additional capital, which may not be avail"able on acceptable terms. Every share issued to fund the burn spreads the eventual upside across more owners, so even if the business succeeds, today's holder may own a much smaller slice of it. The bear case is structural: a loss-making company priced at 11 times revenue, with flat organic growth and a rising share count, is a narrative bet where the value methods, which land at a small fraction of the price, offer no floor if the story slips.
Valuation
BigBear.ai cannot be valued on earnings, because it has none; it loses money, so the price is read against its sales. At about 11 times revenue, the price implies the business eventually earns an operating margin near 9% and grows revenue at its self-funding ceiling for roughly two decades. That is a demanding bet for any company and especially for one whose revenue was flat to slightly down in the most recent quarter. The high revenue multiple is the market pricing a future scale the company has not demonstrated.
The method picture is thin by necessity. Because the company is unprofitable with negative returns, the earnings-based and asset-return lenses do not produce meaningful valuations, and the engine flags them as reference points only. What remains is the revenue-multiple read, which itself lands below the price, and a book-value floor far beneath it. In plain terms, there is no family of method that reaches the price on current fundamentals; the price is supported only by the forward-growth-and-margin story embedded in the revenue multiple. A buyer should understand there is no valuation anchor here from earnings or assets, only a bet on a business model that is still proving it can scale profitably.
Solvency is the one genuine cushion. The company holds roughly $316 million of net cash after settling its convertible notes, with about $349 million of liquid assets, so it has the runway to keep operating while it pursues the margin improvement. But the cash came partly from issuing stock and converting debt to equity, which is why the share count grew nearly 38% in a year, so the runway is being bought with dilution. The most decisive point for the valuation is that the entire price is a forward bet with no downside support: the growing classified backlog is the one concrete anchor, but until it converts into profitable, growing revenue, the value methods say the price sits far above what the business is worth today, and the dilution steadily lowers each holder's share of whatever it becomes.
Catalysts
BigBear.ai's first quarter of 2026 was a mix of margin progress and flat top-line. Revenue slipped 1% year over year to $34.4 million, as lower volume on Army programs offset the contribution from the Ask Sage acquisition, while the net loss narrowed to $56.8 million and the per-share loss improved to $0.12 from $0.25. The clearest positive was the gross margin, which rose to 34.0% from 21.3% a year earlier on the higher-margin Ask Sage generative-AI mix.
The catalyst to track is contract wins converting to revenue. The company stacked close to $75 million in wins during the quarter, grew backlog 14% sequentially to $281.9 million, and highlighted a $53 million sole-source classified contract with an intelligence-community customer. Management affirmed full-year revenue guidance of $135 million to $165 million, so the key question is whether the backlog converts fast enough to hit that range, since first-quarter revenue was flat.
The overhang to watch is the capital structure. In January 2026 the company settled $124.6 million of its 2029 convertible notes largely by converting them to equity, adding to a share count that has grown sharply over the past year. Further capital needs and the pace of dilution are the variables that determine how much of any eventual success reaches current holders. The next earnings reports, with the revenue progression against guidance, the gross-margin trend, and any new financing or dilution, are the cleanest read on whether the trajectory the bull case points to is real or whether the narrative is outrunning the numbers.
Peer Cohorts (Per Segment, With Filing Citations)
BigBear.ai (consolidated) (reported)
- PLTR (Palantir Technologies Inc.)
- (no filing in the citation store)
- AI (C3.AI, INC.)
- (no filing in the citation store)
- PATH (UiPath, Inc.)
- (no filing in the citation store)
- VRNS (VARONIS SYSTEMS, INC.)
- (no filing in the citation store)
- QLYS (QUALYS, INC.)
- (no filing in the citation store)
- INTA (Intapp, Inc.)
- (no filing in the citation store)
- NCNO (nCino, Inc.)
- (no filing in the citation store)
- BL (BlackLine, Inc.)
- (no filing in the citation store)
Methodology Note
- Priced-in inversion: the valuation is inverted on the current price to recover the operating-income growth, duration, and steady-state margin the price embeds (ROE for financials, FFO growth for REITs).
- Valuation x-ray: the valuation models, grouped into four families (asset, earnings, relative, growth). Each model is expressed as a price/FV ratio (distance from price), not a point fair-value estimate. The spread across families is the disagreement.
- Solvency: net cash/debt, net-debt-to-NOPAT, interest coverage, and share-count CAGR from EDGAR financials (net debt / FFO and fixed-charge coverage for REITs; regulatory-capital framing for financials).
- Peer cohorts: per-segment comparables with deep-linkable SEC filing citations.
Fundamentals sourced from SEC EDGAR filings. Current price from Databento. The priced-in inversion and valuation x-ray are computed by the boothcheck engine; narrative composed by AI from the structured data.
Sources
BigBear.ai Q1 2026 results · company financial data