Matt Barnard is a finance leader at Vertice who writes about managing software purchasing and AI costs. They draw on finance-team problems to explain how companies can track spending, negotiate software contracts, and build purchasing workflows.
Matt Barnard is useful for practical finance-operator posts on software purchasing, procurement, and AI cost visibility.
Best for
A B2B FinTech vendor selling enterprise spend management for finance-leader thought leadership.
Not for
Consumer savings apps seeking broad US consumer awareness or app-install campaigns.
Key takeaways
AI spend is a visibility problem before it is a cost problem
Token-based pricing breaks the annual budget, so steering beats restricting
Buying rules should live inside the workflow, not in more approval gates
Check first: Confirm audience job function, seniority, company size, region, and paid or co-marketing availability.
Main ideas
AI spend is a visibility problem before it is a cost problem · Token-based pricing breaks the annual budget, so steering beats restricting · Buying rules should live inside the workflow, not in more approval gates
Matt Barnard is a finance leader at Vertice who writes about software purchasing, procurement, and the changing costs of AI.
Their posts draw on finance-team challenges: a purchasing process that fails as a company grows, software commitments finance discovers too late, and AI spending that shifts faster than annual budgets can track.
They connect these problems to specific decisions about visibility, approvals, and investment, making Barnard’s work relevant to readers interested in enterprise spending.
Barnard makes abstract finance and procurement problems concrete, arguing for workflow changes rather than more controls.
In one post, they contrast the $5,000 purchase that gets stuck with the $50,000 commitment finance misses, and propose automatic routing for low-risk requests alongside contextual review for major contracts.
In posts about AI costs, they argue that finance should track spending as it happens and judge it against output, rather than simply restrict usage.
The work also includes a before-and-after picture of software buying and a short Vertice–Vendr acquisition announcement, which provides company context but offers less of an independent argument.
Taken together, these posts offer a practical finance-operator perspective on enterprise purchasing and AI costs.
Their product references mean readers should distinguish Barnard’s analysis from Vertice’s commercial claims.
Matt Barnard writes about how companies buy software and, more recently, pay for AI.
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Two priorities run through the posts: make spend visible and steerable in real time, rather than discovering it on a bill; and build purchasing rules into the workflow instead of enforcing them through more approvals. Drawing on finance-team problems—a purchase process that broke at 500 employees and token pricing that moves daily—they reach the same conclusion: measure consumption against output and steer it, rather than clamp down or add gates.
AI spend is a visibility problem before it is a cost problem
Barnard says the question they hear most from finance peers is "what's our AI spend, and what do we do about it?"
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The first half is usually answered with "more than we thought," and by then it's too late to act.
Their position: the goal isn't to make the number smaller, but to understand what drives it, whether it's a good investment, and where there's room to optimize.
That means tying spend to teams and commitments in real time, with an alert before a threshold is crossed.
Barnard wants AI spend tied to teams and commitments, with threshold alerts and optimization prompts that don't slow teams down. Vertice built this as AI Cost Optimization, drawing on data across $1bn in processed AI spend.
The follow-up pushes the same position further: Barnard rejects clamping down on teams' token use, arguing that teams getting it right measure AI spend against output and steer in real time.
Token-based pricing breaks the annual budget, so steering beats restricting
Barnard argues the fastest-growing line a CFO should track is AI token spend, not headcount or per-seat cost, because it keeps moving after signature.
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Annual budgets were built for a world where a contracted number stayed put; token spend shifts across teams and tools, largely without central visibility, and reaches finance only after the money is gone. Restricting consumption, they argue, limits adoption instead of fixing the forecast.
Barnard cites average AI-native contract values up 59% in six months, argues that velocity matters as much as the total, and says AI-native buyers are overpaying 19% against benchmark. The fuller data breakdown is in the Vertice AI Index.
Buying rules should live inside the workflow, not in more approval gates
Barnard describes a purchasing model that worked at 50 employees—every purchase went through them—but broke at 500: a $5k tool became an approval bottleneck while a $50k commitment went on a corporate card that finance saw only when the statement arrived.
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Both failures trace to the same gap: no process between "someone needs a tool" and "the company is now paying for it."
Adding approvals and sign-offs, they argue, just moves the bottleneck to a committee and leaves engineers chasing a Slack thread to get a design tool upgraded.
The alternative is routing logic inside the workflow: low-risk, low-value requests approved automatically; high-value contracts flagged to the right person with context for a fast decision; and compliance checks run inline rather than after the fact.
At 500 people, Barnard found a $5k tool stuck waiting while a $50k commitment went unnoticed on a corporate card until the statement arrived. Both traced back to the missing process between request and payment.
In a later post, Barnard contrasts buying teams in 2022—fragmented across email, Slack and rogue credit-card expenses, with auto-renewals carrying an 11% "inflation adjustment" and finance and IT acting as traffic cops for every purchase—with those in 2026: a single request front door, agentic AI executing over 70 manual tasks from price benchmarking to contract extraction, and approvals completed in minutes inside Slack or Teams.
Negotiate on benchmark data, not gut feel and old spreadsheets
Barnard repeatedly argues that software negotiations should draw on pricing data, not instinct.
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They describe buyers entering negotiations with exact pricing from a large vendor set and a large volume of processed spend, and point to company datasets as the source of benchmarks and savings recommendations. The figures are Vertice's own, presented as evidence of scale rather than independent research.
Barnard cites exact pricing data from over 32,000 vendors and more than $30bn in processed spend, plus savings recommendations drawn from $1bn in processed AI spend. The Vertice AI Index offers more detail.
After Vertice acquired Vendr, Barnard described the combined procurement intelligence dataset as the largest in the world by an order of magnitude, feeding better-trained AI agents and smarter benchmarks.
Start here to understand the operating problem behind Barnard’s writing: why does enterprise software buying need a system at all?
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The post answers with their own failure cases rather than an abstract pitch.
Barnard describes the exact point where a single-approver purchasing model breaks.
At 50 people, every software purchase ran through Barnard and it worked.
At 500, the same model produced two opposite failures at once: Barnard became a bottleneck on $5k tools that should have taken a day to approve, and a $50k commitment was made on a corporate card that finance only discovered when the statement arrived.
Barnard traces both to the missing process between "someone needs a tool" and "the company is now paying for it," and argues against the instinct to add more approvals and sign-offs, which just moves the bottleneck to a committee.
Barnard's alternative is routing logic inside the workflow: low-risk, low-value requests approved automatically, high-value contracts flagged to the right person with context, compliance checked inline rather than after the fact.
For brands planning a campaign
A spend-management or procurement vendor can build messaging around the headcount threshold where manual approvals break down, and the two resulting failures: slow small purchases and invisible large ones. These are symptoms buyers will recognise in their own organisations.
This is Barnard’s clearest statement of the change underway and the practices they consider obsolete.
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Read it second for the before-and-after case they make.
A side-by-side comparison of buying teams in 2022 and 2026.
In 2022, purchasing was fragmented across email, Slack and rogue credit card expenses; auto-renewals arrived with an 11% “inflation adjustment” as standard practice; finance and IT acted as traffic cops; and negotiations relied on gut feel and outdated spreadsheets.
In 2026, a single dynamic “front door” captures every request; agentic AI executes over 70 manual tasks, from price benchmarking to contract extraction; approvals are completed in minutes inside Slack or Teams; and buyers enter negotiations with pricing data from over 32,000 vendors and $30bn+ in processed spend.
Drawing on 15 years in finance, Barnard notes that companies making the switch still face tight budgets and aggressive vendor tactics.
For brands planning a campaign
The 2022-versus-2026 contrast gives co-marketing or webinar content a concrete narrative frame, while the named figures can support a benchmark or state-of-the-market piece.
The piece shows how Barnard turns a recurring customer question into a product requirement and makes their position clear: finance should measure and steer AI costs, not simply cut them.
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Barnard opens with the question Barnard says keeps coming: "What's our AI spend, and what do we do about it?"
Barnard notes the first half is usually answered with "more than we thought," and that by the time finance knows, it is too late to act.
The causes Barnard names are token usage that changes daily, spend scattered across a dozen models and tools, and a portion buried inside SaaS already being paid for.
On the second half, Barnard draws a line: the job is not to make the number smaller, it is to know what drives it, whether it is a good investment, and where there is room to optimise — none of which can be answered from a bill read after the event.
Barnard then describes what Barnard wanted: AI spend tied to teams and commitments in real time, with alerts before a threshold is crossed, and states that Vertice built this as AI Cost Optimization, drawing on data across $1bn in processed AI spend.
For brands planning a campaign
For an enterprise spend or FinOps audience, this is a strong thought-leadership anchor: the framing of AI cost as a visibility and investment question, rather than a cost-cutting question, can carry directly into briefing content or a sponsored discussion.
The previous item explains the problem; this one gives the figures and Barnard’s position against limiting AI adoption.
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Useful for assessing whether their commentary is backed by data.
Barnard argues that the fastest-growing line a CFO should track is AI token spend, not headcount or per-seat cost.
They cite three figures: average AI-native contract values rose 59% in the last six months; velocity matters as much as the total; and AI-native buyers are overpaying 19% against benchmark.
Annual budgets once assumed a number would stop moving after signing, but token-based spend shifts constantly across teams and tools without central visibility.
By the time it reaches finance, the money is already gone.
Barnard rejects clamping down and telling teams to consume fewer tokens.
Instead, they say, teams getting it right measure AI spend against output and steer in real time.
They point to the Vertice AI Index for the full data breakdown.
For brands planning a campaign
The statistics—59% contract-value growth and 19% overpayment against benchmark—give a brand reusable, quotable data points for a report, panel or campaign aimed at finance leaders evaluating AI budgets.
Read this for the commercial context behind Barnard’s claims about benchmarking data and the sources of the datasets they cite.
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It is a short background item rather than an argument.
Vertice acquired Vendr, which Barnard says makes the combined company the world’s largest procurement intelligence dataset by an order of magnitude.
They say customers will benefit from better-trained AI agents, smarter benchmarks, and expert recommendations backed by more real-world pricing data than anyone else can draw on.
The post links to the company’s own explanation.
For brands planning a campaign
This corporate milestone offers a timely hook for a partner, but it is an announcement rather than a position. Use it for context in a brief, not as a campaign angle on its own.
Approach: plain, finance-first argument closed with a product or data link4
Benchmarking and negotiation leverage from spend data3
AI spend as a new line item for finance2
Procurement process design at company scale1
From reactive to proactive software buying1
Consolidation and the Vendr acquisition1
AI spend as a new line item for finance
Barnard repeatedly frames AI token consumption as a cost finance teams cannot see until the bill lands.
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Token-based pricing changes daily, spreads across many models and tools, and partly sits inside SaaS contracts already being paid for.
They argue the CFO's job is not to shrink the number but to understand what drives it, whether it is a good investment and where there is room to optimize.
That requires tying spend to teams and commitments in real time, with alerts before a threshold is crossed.
A related post cites average AI-native contract values up 59% in six months and AI-native buyers overpaying 19% against benchmark.
For brands planning a campaign
This is the closest match to a FinTech brief on enterprise spend optimization.
Barnard supplies cost-governance language a brand can build product education or thought leadership around: token spend, thresholds, benchmarks and real-time visibility.
It fits campaigns that sell visibility and control to finance teams rather than technical AI content.
Barnard describes software buying at 50 versus 500 employees.
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At 50 people every purchase went through them, and that worked.
At 500, the model broke: a $5k tool became an approval bottleneck while a $50k commitment went on a corporate card without finance knowing until the statement arrived.
They reject adding approval gates because that only moves the bottleneck to a committee, and describe routing low-risk requests automatically, flagging high-value contracts with context for a fast decision, and running compliance checks inline rather than after the fact.
Tells the problem through a before-and-after scale story, names the failure mode of the obvious fix, then offers an alternative.
A personal anecdote about Vertice, with two illustrative purchase values.
For brands planning a campaign
A ready-made argument for campaigns about procurement automation, spend controls or approval workflows. The scale narrative gives a brand a concrete opening problem and a clear alternative, presented as lived experience from a finance seat rather than vendor theory.
A side-by-side comparison of buying teams in 2022 and 2026.
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In 2022, purchasing was fragmented across email, Slack and rogue credit-card expenses; auto-renewals with inflation adjustments were normal; finance and IT acted as traffic cops for every purchase; and negotiations rested on gut feel and stale spreadsheets.
In 2026, Barnard describes a single dynamic request front door, agentic AI executing over 70 manual tasks from price benchmarking to contract extraction, approvals completed in minutes inside Slack or Teams, and buyers entering negotiations with exact pricing data.
They state 15-plus years in finance.
Numbered before-and-after lists, closed with a direct invitation to discuss procurement setup.
For brands planning a campaign
The clearest change-over-time framing in the material, and a strong fit for a brand selling a purchasing front door or agentic procurement tooling.
The numbered contrast can be adapted directly into a sponsored comparison.
Ask which specific claims the brand can stand behind.
Benchmarking and negotiation leverage from spend data
Barnard returns to the idea that negotiation should be driven by data rather than instinct.
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Figures cited across posts include exact pricing from over 32,000 vendors, more than $30bn in processed spend, savings recommendations drawn from $1bn in processed AI spend, and AI-native buyers overpaying 19% against benchmark. The Vertice AI Index is offered as the deeper data source.
For brands planning a campaign
A data-led hook for procurement or FinTech brands that want numbers in the copy. Treat the figures as company-owned marketing claims rather than independent research, and verify them before reusing them in a campaign.
Barnard announced that Vertice acquired Vendr, saying the combined company has built the largest procurement intelligence dataset by an order of magnitude, feeding better-trained AI agents, smarter benchmarks and expert recommendations backed by more real-world pricing data.
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Brief and promotional, with no independent detail or analysis of the deal.
For brands planning a campaign
Shows that Barnard will post corporate milestones, so a brand should expect participation in company news, not only opinion pieces. The post is short and promotional, supporting awareness of an M&A event rather than a subject-matter argument.
Approach: plain, finance-first argument closed with a product or data link
The posts follow a consistent structure: open with a question or a contrast, build the case in short, plain paragraphs aimed at finance leaders, and close with a product link or a chat or data invitation, such as Vertice AI Cost Optimization, a procurement-setup discussion or the AI Index.
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The terminology is finance-facing rather than deeply technical, and each post ties the topic back to Vertice's product or dataset.
For brands planning a campaign
Content will carry a clear call to action rather than neutral commentary, which suits co-marketing and product-education campaigns but less so placements that need an unaffiliated editorial voice.
Matt Barnard’s posts centre on Vertice, where they describe products and data the company has built as a finance leader.
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The clearest commercial relationship is their role at Vertice; the posts are written from inside the business (“as a CFO”, “we built it”, “We built this at Vertice”).
The posts also describe Vertice’s acquisition of Vendr, which expanded its procurement pricing data.
Figma, Slack and Teams appear as examples of workplace friction, not commercial relationships.
Vertice
Employment
Barnard writes as a finance leader at Vertice, the software and AI spend management company.
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One post recounts the purchasing problems Vertice set out to solve: “We built this at Vertice because I lived the problem.” Two others introduce Vertice products and data—Vertice AI Cost Optimization and the Vertice AI Index—in the first person, framing the work as something their team built.
Barnard announced Vertice’s acquisition of Vendr, describing the combined procurement intelligence dataset and the benchmarking and AI agent data available to Vertice customers.
Matt Barnard's posts circle one question: how a company should decide what software and AI to buy, and how it should track the cost after the decision.
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A good place to start is the post about purchasing at 50 people versus 500, which describes both a stalled $5k tool and an unnoticed $50k card charge, then argues for routing rules built into the workflow instead of more approval layers.
Read the two AI-spend posts together: the later one sharpens the earlier argument, saying finance should measure token consumption against output and steer it in real time rather than tell teams to use fewer tokens.
The numbered 2022-versus-2026 comparison and the acquisition announcement make the same argument in list and company-news formats.
The three creators further down work in adjacent areas and are worth comparing directly.
AI cost managementSoftware procurementToken-based pricingVendor negotiationBenchmarkingProcurement automationSaaS spend optimizationLinkedIn postsBefore-and-after listsCFOs and finance teamsProcurement teams
Justin Etkin
Etkin treats AI buying as an ongoing cost problem, not a one-off purchase: token spend changes every day, and engineering, finance and procurement each hold part of the answer, with procurement best placed to connect them.
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A Carta case study describes using benchmark intelligence to find an extra 21% of savings mid-negotiation, echoing Barnard's argument for pricing benchmarks over instinct.
Etkin writes from the procurement-platform side, with named customer stories and specific negotiation tactics, and takes a more optimistic view of procurement's expanded role in the business. Barnard argues from the finance seat, focusing on visibility, thresholds and workflow routing, and closes with product or data links rather than case studies.
Menashe writes short, structured posts for finance leaders about where AI meets financial operations, pairing a concrete outcome with a stated limit: a forecast cycle cut from three weeks to five days, or reconciliation reduced by two days, alongside an explicit list of what the tool will not fix.
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Barnard's posts also pair specific examples with named caveats.
Menashe focuses on accounting and the close—lineage for AI answers, exception review queues, and syncing source systems into reporting—rather than procurement or vendor negotiation. The buying decision Barnard writes about barely features.
Toomey writes as a working procurement lead about the friction of buying AI—security reviews, more stakeholders and longer cycles—and how autonomous source-to-pay tooling can address it by routing requests, pulling supplier and contract data, and checking policy.
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Barnard covers the same intersection of procurement process and AI adoption.
Toomey's posts are driven by platforms and announcements, framing the shift through a product launch and an executive's take while stressing the need to keep pace with the business. Barnard builds an argument from a purchasing failure and focuses on cost visibility rather than requests and approvals.
Matt Barnard's account is small and focused on software purchasing workflows, AI token spend, benchmark pricing, and procurement automation—the same territory a FinTech vendor selling enterprise spend optimization would want to reach.
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Five dated posts between 23 April and 13 August 2026 consistently argue that finance and procurement teams should move from after-the-fact bill review to live visibility of software and AI spend.
That makes Barnard a persistent voice on spend optimization, but the posts don't show whether their readers are the buyers a vendor wants to reach.
Before committing budget, request audience data on job function, seniority, company size, and region, plus any audience survey covering buyer roles and recent per-post impressions or engagement on spend-optimization posts.
Barnard's posts do not set out a partnership offer.
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They write as a finance leader at Vertice, promoting the company's AI spend tracking and cost optimization, software purchasing workflows, and acquisition of Vendr.
The posts cover procurement problems—fragmented buying, auto-renewals with inflation adjustments, and token spend that moves daily—and link readers to Vertice pages.
They are company promotion, not disclosed sponsorships.
No rate card, format list, audience package, deliverable specification, or inquiry route for outside brands appears in the posts.
Brands considering a paid collaboration should ask Barnard directly whether sponsored work is available.
Need: a B2B FinTech vendor selling enterprise spend management wants a credible subject-matter voice for a finance-leader thought-leadership campaign on controlling AI and software costs.
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Decision: good fit, with conditions.
Barnard's posts are already written for this buyer.
They open with the question finance leaders ask — what is our AI spend and what do we do about it? — and argue that the job is not to shrink the number but to understand what drives it.
They describe real-time visibility tied to teams and commitments, with alerts before thresholds are crossed.
That maps directly to a spend-optimization product education campaign.
The 2022-versus-2026 procurement contrast and the 59% AI-native contract-value growth and 19% overpayment figures give the brand concrete narrative and data hooks.
The 50-versus-500 people purchasing story offers a recognizable opening problem for webinars, guides, or a co-authored series.
Conditions: the posts don't provide audience demographics or engagement, so this is a topical fit, not a proven distribution fit.
Confirm whether Barnard is available for paid or co-marketing work.
Use them as a finance-operator voice and potential co-marketing partner if terms are confirmed, not as a reach buy.
When to choose a specialist
Need: a consumer savings app wants a high-reach creator to run a paid awareness and app-install campaign aimed at broad US consumers under 35.
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Decision: poor fit.
The posts target B2B finance leaders, covering software purchasing workflows, AI token cost governance, procurement automation, benchmark pricing, and a corporate acquisition.
Nothing addresses personal saving, consumer credit, budgeting, or app adoption.
The account had 2,733 followers on 8 October 2026.
The posts provide no demographic, employer, seniority, region, impression, or engagement data, so they don't show whether the audience includes enough target consumers for a reach campaign.
The posts also read as company promotion for Vertice, with product or data links at the close, rather than neutral consumer editorial.
No format, rate, deliverable, or inquiry route is published, so a paid placement requiring disclosed sponsorship can't be planned.
Reject, or test only after the brand provides audience data proving consumer overlap and Barnard confirms a consumer-facing sponsorship offer.
A worked campaign example
Worked campaign angle for the good-fit brief: "The AI line item only finance can see." A three-touch thought-leadership sequence for a FinTech spend-management vendor, built from Barnard's existing arguments.
Touch one: a co-authored LinkedIn post or short video opening with the CFO question Barnard reports receiving, then the finding that the answer is usually more than finance thought—and that by the time it knows, it is too late to act. Use the 59% rise in average AI-native contract values and the 19% overpayment against benchmark as the data spine, labelled as Vertice data in the copy.
Touch two: a 30-minute finance-leader roundtable or webinar on why cutting token use is the wrong first move.
The angle is Barnard's line that the job is not to make the number smaller but to know what drives it, whether it is a good investment, and where to optimize.
The vendor supplies a benchmark worksheet; Barnard supplies the operator framing from the 50-versus-500 purchasing story: the problem is not too few approvals, but no real-time connection between usage, teams, and commitments.
Touch three: a short follow-up post or email with a threshold-alert checklist and a call to compare a current AI bill against benchmark.
Why this works: it uses the structure Barnard already publishes in, turns the cited figures into campaign assets, and stays within the vendor's spend-visibility argument. Before production, confirm whether Barnard is available for a paid or co-marketing arrangement and how the Vertice data may be cited; request audience data for the account; and decide whether the deliverable is a sponsored series or an earned interview if no sponsorship offer exists.
Choose Justin Etkin when the campaign angle is procurement negotiation and sourcing tactics rather than CFO-level spend governance.
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Etkin's posts show a buyer negotiating with a vendor that had stopped moving and using benchmark intelligence to find another 21% savings on a Carta deal.
A second post argues AI buying works more like buying steel than software because token spend, pricing, and demand move constantly, positioning procurement to connect usage, forecast, and price.
That fits a vendor selling negotiation intelligence, sourcing capacity, or procurement workflow better than a finance-led AI cost-visibility campaign, especially if the brand wants an event hook around procurement when the deal never closes.
Etkin's evidence is narrower on finance leadership and cost governance: the posts center procurement's seat and negotiation outcomes, not a CFO's real-time view of AI spend tied to teams and commitments.
The commercial thread points to Tropic, so a FinTech spend-management brand should ask whether Etkin can work with another vendor.
Use Etkin when the buyer task is helping procurement negotiate and source better AI deals; use Matt Barnard when it's helping finance see and steer AI and software spend.
Choose Omer Menashe when the campaign's buyer task is trustworthy finance AI and source-trail reporting rather than top-line spend optimization.
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The posts argue finance AI is useless without lineage: which ledger entry, contract, vendor file, supplier change, or assumption moved the forecast.
One example describes a CFO cutting a forecast cycle from three weeks to five days after the system surfaced a supplier pricing shift that spreadsheets would have missed.
A second post lays out a narrower operational step: sync unapplied cash into a reporting store, route exceptions to a daily queue, cut month-end reconciliation by two days, and prove one high-value sync before expanding.
That fits a vendor selling finance data pipelines, close automation, reconciliation, or AI with audit trails.
Menashe's posts do not cover AI token contracts, software purchasing volume, benchmark pricing, or procurement automation at the scale Barnard's campaign uses.
They are better for finance-operations and controllership audiences than for a CFO-level AI spend control narrative.
The tradeoff is depth in lineage and close workflows versus Barnard's breadth across enterprise spend categories and procurement transformation.
Use Menashe when the proof point is that the answer must be defensible; use Barnard when spend is moving and finance needs to see it now.
Choose Sarah Toomey when the campaign needs a procurement-operator voice on autonomous source-to-pay and AI-native procurement adoption.
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Toomey's posts describe 15+ years in sourcing and running procurement inside a finance team, then introduce an autonomous source-to-pay platform that routes requests, pulls supplier and contract data, checks policy, and moves work toward a decision.
The other post frames AI buying as slow and expensive because of security reviews and extra stakeholders, arguing teams need visibility to keep pace without skipping rigor.
That suits a vendor selling source-to-pay automation, procurement orchestration, or an AI-native procurement platform, particularly when the audience is procurement leaders rather than CFOs.
Toomey's content is more product-launch and procurement-function focused than Barnard's finance-led cost governance.
It offers less evidence on AI token spend, benchmark pricing, or CFO-facing budget control.
The commercial angle is tied to a specific platform, so a spend-management brand should ask about availability.
Use Toomey when the campaign promise is making procurement autonomous and fast without losing control; use Barnard when it's knowing and steering what the company spends on AI and software.
