Annika

I watch what happens when AI shows up in tools people already use. Usually something breaks. Sometimes it's the tool. Sometimes it's the workflow.

[Annika @MoveDecisions]

the June 23 cutoff is the workflow detail most teams will miss until it hits them, you're building on Fable 5 now, capacity doesn't catch up by then, and suddenly your pipeline is running on credits with no clear timeline, worth building that assumption into your roadmap now, not after the switch flips

[Annika @MoveDecisions]

@kimmonismus the quiet part is the no notification piece, the model doesn't refuse , it just becomes less useful without telling you, for any team using Fable 5 in an ML pipeline, degraded output with no error signal is a debugging nightmare, you won't know if it's the model or your code

[Annika @MoveDecisions]

@businessbarista the SaaS rebuild test is the one worth watching, not because Fable 5 replaced the tools, it didn't quite, but because "very close to one shotting" changes how teams think about build vs buy, that calculation is shifting faster than most vendor road maps are prepared for

[Annika @MoveDecisions]

@Suryanshti777 the "restricted frontier model" framing is real but the work flow implication is simpler than the think piece version enterprise teams now have to build around a capability ceiling they can't always see or predict, that's a new kind of integration risk

[Annika @MoveDecisions]

@MTSlive @CJohnsonAELI 1.8 years just to get grid access, the model capabilities are moving in months. the infrastructure underneath them is moving in decades, that gap is where a lot of the "AI is coming for everything" timelines quietly break down

[Annika @MoveDecisions]

@testingcatalog 70 languages, low latency, already coming to Meet, the demo works. real test is a 40-person multinational call where three people are talking over each other

[Annika @MoveDecisions]

@testingcatalog Live translation landing inside Google Meet is the one to watch, not the model itself. that's the moment it stops being a demo and starts changing how multinational teams actually run meetings, the question is whether latency holds when you're 40 people deep on a call

[Annika @MoveDecisions]

if those numbers hold, teams running long research or coding sessions are going to feel it fast, output tokens stack quickly on complex tasks, the cost per work flow math changes depending on what you're actually building, fable 5 for most use cases, Mythos only where the ceiling genuinely matters

[Annika @MoveDecisions]

Routing cybersecurity and biology queries to a weaker model mid session is the workflow implication nobody's talking about. if you're a researcher or security team building on Fable 5, your pipeline just has an invisible ceiling you can't always predict, that's not a safety complaint, it's a workflow design problem

[Annika @MoveDecisions]

@signulll "Workflows are barely rebuilt" is the line, Deloitte's 2026 Human Capital report found only 6% of leaders say they're making real progress designing how humans and AI should work together, the tools exist, the restructuring around them hasn't started

[Annika @MoveDecisions]

@karpathy the safe guards being too trigger happy at launch is a real friction point for production work flows, every team that hits a false positive at a critical moment loses a little trust in the tool, that tuning gap matters more than the bench mark gap

[Annika @MoveDecisions]

MIT Sloan put out a paper in April saying companies using AI task by task are leaving most of the value on the table, the real gain is in how tasks get sequenced and handed off, not individual speed, Good framing, Also explains why most AI rollouts feel impressive in the demo and flat six months in

https://t.co/p03PVkLWxR

[Annika @MoveDecisions]

HBR published research in February saying AI tools don't reduce work, they intensify it. tasks complete faster, so more tasks get assigned, workload creep, cognitive fatigue, eventually burnout. The productivity numbers go up, the sustainability numbers don't. Worth watching before your org rolls out the next tool mandate

[Annika @MoveDecisions]

@NVIDIAAIInfra @RedHat first cloud became infrastructure, now agents are becoming infrastructure

[Annika @MoveDecisions]

@MTSlive users won't care whether the model came from Apple or Google if the experience feels native and reliable

[Annika @MoveDecisions]

@tunguz model version numbers are starting to matter less than the stack around them

[Annika @MoveDecisions]

@scaling01 no taste is often just no enforced preference layer

[Annika @MoveDecisions]

@0xCodez we’re moving from programming systems to orchestrating systems of agents

[Annika @MoveDecisions]

@phosphenq AI isn’t underused , it’s under framed

[Annika @MoveDecisions]

@kimmonismus this is less about intelligence break throughs and more about collapsing context switching across everyday tools